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A Generalized Adder for Cell Size Homeostasis: Effects on Stochastic Clonal Proliferation
No full textThis article was originally published in Biophysical Journal. The version of record is available at: https://doi.org/10.1016/j.bpj.2025.03.011.
© 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.
This article will be embargoed until 03/21/2026.Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.AS acknowledges the support of NIH-NIGMS via grant R35GM148351
Field monitoring, pathogen quantification, and greenhouse screening as a foundation for integrated management approaches for Pythium in corn
No full textBetts, Alyssa K.Pythium root rot (PRR), caused by various species in the Pythium and Globisporangium genera, is an important early season disease in corn (Zea mays L.), particularly in the Mid-Atlantic region of the United States. Initial symptoms of PRR in corn include necrotic root tissue that may result in pre- or post-emergence damping-off, which can significantly reduce stand. Integrated pest management (IPM) is a comprehensive approach to pest control that uses cultural, physical, biological, and chemical methods to manage pests below an economic injury level. Current management practices for Pythium are either preventative and include the use of at-plant treatments, or reactive, involving complete replanting of affected fields in severe cases. This research aimed to build a foundation for IPM approaches for Pythium in corn through quantifying economic season-long impacts of PRR on surviving corn yield, assessing inoculation methods for controlled studies, investigating the suitability of qPCR to detect Pythium clades in soil samples, and evaluating current at-plant management strategies. Field and greenhouse studies were conducted to explore these methods. Plants that display early symptoms of Pythium infection can survive to reproductive maturity, but the economic impact is not well documented. A field study was conducted in 2021 and 2022 to track paired symptomatic and symptomless plants and quantify economic loss. Symptomatic plants had reduced root weight, stalk diameter and a 66% yield reduction compared to symptomless plants. Controlled environment inoculation trials identified Pythium-colonized sand-cornmeal as the most consistent inoculation method, and the use of rhizoboxes allowed for the visualization of root-pathogen interactions. Additionally, quantitative polymerase chain reaction (qPCR) was tested as a non-invasive tool for Pythium detection and quantification from soil samples. This technique could not differentiate Pythium abundance between soil collected 20 cm from the base of symptomatic versus symptomless plants. However, differences in Pythium density were noted across years, fields, and soil depths identifying utility of this approach for pathogen confirmation within fields and potential for development of diagnostic testing to compare risk across fields. To evaluate impacts of at-plant management, cover crop termination timing experiments showed increased seedling disease severity compared to a no cover crop control, with highest severity observed in plots with termination 3–7 days post planting. A greenhouse screening trial was conducted to evaluate the efficacy of one chemical seed treatment and two at-plant biological products, finding efficacy from picarbutrazox in two out of three experimental runs. These studies advance the understanding of PRR impact on corn while contributing to the development of IPM strategies that can improve PRR management in the Mid-Atlantic.University of Delaware, Department of Plant and Soil SciencesPh.D
Graph and hypergraph learning: theory and applications in computational network analysis
No full textTong, GuangmoNeural networks, the computational models inspired by the human brain as powerful and advanced machine learning models, have played a central role in artificial intelligence since the 1980s. Given the power of being universal approximators, learning with neural networks has achieved remarkable performance on wide-range tasks, excelling across numerous applications such as computer vision, natural language processing, and graph-structured data analysis. In particular, modern architectures of neural networks applied in graphs and hypergraphs, combined with scalable optimization techniques and the availability of large-scale data, have further amplified their practical effectiveness across diverse domains. However, understanding their generalization capabilities, which refers to a model’s ability to perform accurately on previously unseen data, remains a fundamental challenge, particularly when modeling complex higher-order structures and competitive dynamics inherent in many real-world scenarios, i.e., social network analysis. ☐ This dissertation addresses these challenges by providing a rigorous generalization and learnability analysis of neural networks explicitly designed for higher-order relational data, with a specific application on computational network analysis. In particular, we combine the theoretical analysis with the conditions and mechanisms that enable efficient learning, which are then complemented by extensive empirical studies to validate these theoretical insights. Specifically, our work first study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks with finite VC dimensions, which enables analytical sample complexity and generalization bounds. Based on the proposed hypothesis space, we design efficient algorithms under the empirical risk minimization scheme. For worm dissemination across the wireless sensor networks, we design a communication model under various worms, which are considered vulnerable to attacks by worms and their variants. We learn our proposed model to analytically derive the dynamics of competitive worm propagation. We develop a new searching space combined with complex neural network models. Finally, we develop margin-based generalization bounds for four representative classes of hypergraph neural networks, including convolutional-based methods (UniGCN), set-based aggregation (AllDeepSets), invariant and equivariant transformations (M-IGN), and tensor-based approaches (T-MPHN). Through the PAC-Bayes framework, our results reveal the manner in which hypergraph structure and spectral norms of the learned weights can affect the generalization bounds, where the key technical challenge lies in developing new perturbation analysis for hypergraph neural networks, which offers a rigorous understanding of how variations in the model's weights and hypergraph structure impact its generalization behavior. ☐ By utilizing PAC learnability and PAC-Bayesian frameworks, this work offers theoretical foundations for analyzing the relationship between the neural network architectures and their generalization performance, which shows potential to utilize the theoretical results for model optimization. In addition, with empirical validation on both synthetic and real-world datasets, this work reinforces the applicability of the theoretical findings and demonstrates the models’ ability to handle complex high-order data and competitive dynamics. Altogether, these contributions advance the foundational understanding of neural networks and offer practical methodologies for improving their generalization in structured, competitive environments.University of Delaware, Department of Computer and Information SciencesPh.D
Elastic Coefficients of Polyether Ether Ketone from First-Principles Calculations
No full textThis article was originally published in Materials Research. The version of record is available at: https://doi.org/10.1590/1980-5373-MR-2025-0080
This is an Open Access article distributed under the terms of the Creative Commons Attribution license, https://creativecommons.org/licenses/by/4.0/ which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.First-principles calculations based on the density functional theory (DFT) represent a sophisticated technique to investigate the mechanical strength of materials in general, although underexplored in polymeric structures such as high-performance thermoplastic polymers. In this study, DFT calculations were systematically conducted to evaluate the effects of strain on the structure of polyether ether ketone, determining the maximum elasticity modulus in a perfect alignment condition of the polymer chain. The atom positions and arrangement of the polymer chains were set based on total energy and force minimizations. The four lowest energy structures were stretched up to 10 Å per monomer, and the results have shown a mean elasticity modulus of 5.93±0.74 GPa, which we attribute to the upper limit for aligned and stretched polymeric chains.The authors acknowledge the financial support from National Council for Scientific and Technological Development (CNPq) under project 407431/2022-5, 303160/2023-3 and 447331/2024-8; São Paulo Research Foundation (FAPESP)
under projects 2019/22173-0 and 2018/24964-2; The authors thank the Composite Technology Center (NTC) from the Federal University de Itajubá-Brazil and the University of Delaware-USA for the general facilities
Relative performance feedback in the trucking industry: how rank information affects drivers’ fuel efficiency
No full textThis article was originally published in International Journal of Physical Distribution & Logistics Management.. The version of record is available at: https://doi.org/10.1108/IJPDLM-04-2025-0164
© Emerald Publishing LimitedPurpose
Trucking companies often use relative performance feedback (RPF) to promote fuel-efficient driving. Building on prior, largely experimental research, we examine the effects of RPF on fuel efficiency performance in the trucking industry. In so doing, we consider how ranking information conveyed to drivers in RPF impacts their subsequent miles per gallon (MPG). Furthermore, we hypothesize that drivers interpret such ranking information in a temporal context such that recent improvements or deteriorations in their rank moderate the rank–MPG relationship.
Design/methodology/approach
We analyze a driver-week-level panel dataset obtained from a US-based trucking company. We implement various regression analyses to estimate the hypothesized effects and ascertain the robustness of our findings. Potential endogeneity concerns are addressed as well.
Findings
We find that truck drivers' fuel efficiency performance increases (declines) after receiving RPF, indicating a higher (lower) rank. This effect, however, is not uniform across all drivers – it is observed for top- and bottom-ranked drivers but less pronounced for middle-ranked drivers. Additionally, both week-over-week deteriorations and improvements in drivers' rankings over time can diminish the impact of RPF on fuel efficiency.
Originality/value
Our work offers nuanced insights into how RPF affects truck drivers' fuel efficiency. We also contribute to the trucking-focused literature by highlighting feedback as a mechanism to alter operator behavior and efficiency. We similarly add to social comparison theory and prior RPF literature by documenting that temporal changes in relative performance – both improvements and deteriorations in a driver's rank – can moderate the way RPF affects subsequent performance. These insights collectively help inform the design of motor carriers’ performance feedback strategies
Surface defect passivation by hydrogen sulfide (H2S) reaction and stability under various stress conditions for different Si surfaces
No full textShafarman, William N.The objective of this research was to develop a novel Si surface passivation method using sulfur (S) as a passivating element to withstand industry-standard high temperature contacting and metallization schemes for p-type passivated emitter and rear contact (p-PERC) solar cells. With the successful application of an aluminum oxide (Al2O3) passivation layer, the PERC cell’s back surface passivation has been improved drastically. However, the front n+ diffused junction surface is still poorly passivated by the standard amorphous silicon nitride (a-SiNX:H) anti-reflection coating (ARC) layer. This project addressed the passivation challenges of both the front n+ emitter and the undiffused p-Si back surface. ☐ To pursue these objectives, systematic investigation of process-structure-properties-performance relationships of this novel advanced defect passivation approach was performed. S-passivation was carried out by reacting industrial Czochralski (Cz) Si wafers in H2S using an atmospheric pressure thermal chemical vapor reactor (APTCVR) at temperatures up to 700°C. After systematic optimization of reaction processes (temperature, time, and gas concentration), extremely low surface recombination velocities (SRVs) of 1.5 cm/s and 8 cm/s on n-type and p-type Si, respectively, by S-passivation were demonstrated. In-depth surface and interface characterization using Fourier transform infrared spectroscopy (FTIR), time-of-flight secondary ion mass spectroscopy (ToF-SIMS) and x-ray photoelectron spectroscopy (XPS) revealed sulfur bonded with Si as silicon sulfide (SiS2) after reacting the Si surface in H2S. Furthermore, application of the optimized S-passivation to the n+ diffused emitter surface led to a low surface recombination current density, J0n+ ≈ 40 fA/cm2 (~ 1/4 of the industry standard a-SiNX:H-passivation), and high implied VOC (686 mV) in p-PERC solar cell structures. The S-passivation was further shown to preserve the bulk quality of the p-type and n-type Si, better than the silicon dioxide (SiO2) or Al2O3 passivation processes. ☐ After successful demonstration of effective S-passivation of the Si surface, the air, thermal, and illumination stability of the passivation structure were characterized. S-passivation itself degrades in air due to competing reactions with moisture and oxygen to form oxides. This can be eliminated by an a-SiNX:H capping layer (also acting as an anti-reflective coating). After a-SiNX:H process optimization, illumination and thermally stable S-passivation with SRV < 5 cm/s and J0 < 80 fA/cm2 were demonstrated. Incorporation of S/a-SiNx:H passivation stack of n+ diffused emitter surface in p-PERC cells yielded an efficiency ≈ 20% with VOC ≈ 650 mV, using manufacturing metallization and contacting schemes. Loss of VOC and efficiency in completed p-PERC cell have resulted from the diffusion of sulfur and/or modification of the a-SiNX:H layer after high-temperature exposure in the contact firing process. This was corroborated from detailed surface analysis using XPS, ToF-SIMS and SEM. Nonetheless, promising results of S-passivation, being incorporated into p-PERC cell, inspired the application of S-passivation into Si-heterojunction cells that do not require high temperature metal firing step and showed an encouraging iVOC = 684 mV. However, after evaporated metallization a low cell VOC of 606 mV was recorded, which was caused by an increase in defect density (Dit) and decrease in fixed charge density (Qfix) estimated by numerical software at the SiS2/Si interface. ☐ While the S-passivation of Si surfaces show significant promise with excellent passivation quality of non-metallized cell structures, further works are needed to develop high-temperature-tolerant capping layer and/or engineering of advanced device structures.University of Delaware, Department of Materials Science and EngineeringPh.D
Synthesis of mature peptidoglycan fragments for innate immune programming
No full textGrimes, Catherine LeimkuhlerThe human body harbors trillions of microorganisms that collectively form the microbiome, a diverse community composed of both commensal and pathogenic bacteria. Commensal bacteria play essential roles in host health, including the synthesis of vitamins and the breakdown of otherwise indigestible nutrients. The innate immune system distinguishes between beneficial and harmful microbes through recognition of microbe-associated molecular patterns (MAMPs), such as peptidoglycan (PG), flagellin, and viral RNA. PG, a macromolecule composed of sugar chains crosslinked by peptides, is a major structural component of the bacterial cell wall. During bacterial turnover, fragments of PG are shed and detected by the host immune system, which mounts an inflammatory response to eliminate perceived threats. However, improper recognition of commensal-derived PG can lead to persistent inflammation, contributing to disorders such as Crohn’s disease and psoriasis. ☐ To investigate how PG fragments enter host cells, I collaborated with the Silverman Lab at UMass Chan Medical School to study the role of solute carrier (SLC) transporters in PG uptake. My work contributed to the identification of SLC46A2 as a transporter of meso-diaminopimelic acid (m-DAP)-containing PG fragments, and SLC46A3 as a transporter of muramyl dipeptide (MDP). I also found that methotrexate, a known SLC inhibitor, impedes PG transport—highlighting the potential of these carriers as therapeutic targets for inflammatory disease. To further study these transporters, I began designing and testing photoactivatable PG probes, with the goal of capturing and characterizing transporter-protein interactions. This work aims to uncover the substrate specificity and mechanistic roles of PG transporters in immune signaling. ☐ In a separate but related project, I focused on the bacterial pathogen Staphylococcus aureus, a Gram-positive organism known for its multi-drug resistance, including methicillin-resistant S. aureus (MRSA). A hallmark of S. aureus PG is the pentaglycine bridge, which crosslinks glycan chains and is essential for cell wall strength and survival. Using automated solid-phase peptide synthesis (SPPS), I synthesized mature PG fragments that incorporate this pentaglycine structure. These synthetic fragments provide a platform for probing the immunological impact of S. aureus-derived PG and offer a tool for studying host-pathogen interactions at a molecular level. ☐ Through my research—including the synthesis of meso-diaminopimelic acid (m-DAP) and the integration of automated synthetic methodologies—I have advanced the understanding of how specific peptidoglycan (PG) structures are transported by the host and established a foundation for the novel synthesis of muropeptides with potential therapeutic applications in treating bacterial infections and chronic inflammatory diseases.University of Delaware, Department of Chemistry and BiochemistryPh.D
Interior forests provide refugia for breeding birds from anthropogenic heat and sound pressure
No full textShriver, W. GregoryUrbanization and the associated increase in human activities is a major driver of global change. Among many other threats, urbanization affects climate regimes and human development alters which sounds are prevalent in the environment. The national parks in the National Capital Region Inventory & Monitoring Network (NCRN) are uniquely situated within the highly urbanized Washington D.C. metro region, protecting natural areas along an urban-rural gradient and offering a unique opportunity to study the effects of anthropogenic pressures on biodiversity in small urban parks. I utilized long-term monitoring of the breeding bird community in NCRN parks to study the effects of two uncommonly studied anthropogenic stressors: heat and noise pollution. Using multivariate generalized linear models, I tested the effects of local land surface temperatures (LST) on the bird community and how those relationships may be affected by surrounding land use: either forested, urban, or agricultural dominated landscapes. I found that the abundance of the bird community responded negatively to increased LST, especially for species with specialist traits. Areas with greater forest cover within the surrounding landscape help to mediated the negative effects of LST on abundance by providing habitat that was on average > 2°C cooler than urban or agricultural dominated landscapes. Sound pollution is another important threat from urbanization surrounding national parks, and I tested the efficacy of acoustic indices to predict bird diversity. Acoustic indices are methods to quantify and assess sound in the habitat. The strongest relationships occurred between the acoustic index which measures anthropogenic noise pollution (Normalized Difference Sound Index, NDSI) with bird community integrity, and both NDSI values and bird community integrity were strongly related to distance to major roads. These relationships jointly indicated that acoustic indices are quantifying the level of noise pollution from vehicle traffic, and that higher quality bird habitat occurs further away from roads. My thesis highlights the importance of intact forest habitat to help forest birds contend with the anthropogenic pressures of excess heat and noise pollution. Interior forest habitat buffers the negative effects of these pressures and provide higher quality breeding bird habitat. By examining how external pressures affect protected lands, my findings add to the current understanding of habitat quality and will help guide local- and regional-scale land management decisions.University of Delaware, Department of Entomology and Wildlife EcologyM.S
SOUNDSCAPES OF THE MIND: IDENTIFYING SOUND AND MUSICAL PREFERENCES IN CHILDREN WITH AUTISM SPECTRUM DISORDER IN RESPECT TO SOUND ENVELOPE, PITCH REGISTER, AND SOURCE
No full textenterAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder that
affects social communication, behavior, and self-regulation across emotional, motor,
and verbal domains. Many individuals with ASD experience auditory sensitivities,
which makes them more susceptible to experiencing a sensory overload which often
triggers adverse reactions. Many individuals with ASD strongly resonate with certain
kinds of music despite these sensitivities, while others may steer away from music
education, performance, and therapy altogether to avoid uncomfortable stimuli. While
people with ASD have varying opinions of music and sound, music therapy and
intervention for children with ASD have been shown to lead to positive impacts, such
as facilitating preference expression, communication, and more.
To better understand auditory preferences in children with ASD, I developed a
survey featuring 30 sound samples that varied by sound envelope characteristics
(ADSR: attack, decay, sustain, release), pitch register (high, medium, low), and source
type (synthesized, acoustic, ambient) that was taken by 11 children with and without
ASD. Results indicate that children with ASD show a stronger preference for slow
decays, long sustains, and slow releases compared to their neurotypical peers. While
both groups generally favored similar sound features, such as acoustic over
synthesized or ambient sources, the degree of preference varied significantly in some
variable levels. Notable differences were observed in response to sharp and weak
attacks, slow decays, long sustains, extreme release speeds, high pitch registers, and
non-acoustic sound sources.
This study contributes to the growing body of research on auditory perception
in individuals with ASD. Its findings may guide music therapists and educators in
tailoring musical experiences that align more closely with the auditory preferences of
children with ASD, thereby fostering more positive and effective outcomes in
therapeutic and educational contexts.ente