Study of gene co-expression network functioning in drought response during early endosperm development in maize

Drought stress is one of the major abiotic factors that have a huge impact on grain yield. The responses due to drought stress are very complex and depend on various factors like the duration of drought stress, the severity, and the developmental stage when the stress is imposed. Stress, when imposed during reproductive development, is, particularly critical for grain yield. We imposed drought stress during two windows of early endosperm development: from 2 days before pollination to 6 days after, and from the day of pollination to 8 days after. RNA was extracted from dissected endosperms on the final day of stress treatment and used for transcriptome profiling and Gene Co-expression Network Analysis (GCNA). We found that drought stress affects the grain yield and size of the kernel and endosperm when induced early during development. Gene co-expression modules associated with the treatments were enriched for diverse biological functions, including autophagy, cell cycle, microtubules, and carbohydrate biosynthesis. Stress enhanced starch accumulation in the endosperm, which is supported by altered carbohydrate metabolism seen in gene expression analysis. Stress also induced the expression of the ABA-responsive RAB17-YFP reporter. Our study affirms that the early onset of stress during endosperm development might have severe effects on development and, eventually, the grain yield in maize. Drought stress appeared to impact endosperm development in various ways: indirectly through impaired photosynthesis in maternal plant tissues and decreased availability of photosynthate to the kernel, and directly through impacts on endosperm functions. Hub genes were identified that are potentially key regulators in the co-expression networks functioning in stress responses during early endosperm development. In another study, to investigate brassinosteroid (BR) hormone signaling in maize, BIN2, a GSK3-like protein kinase that acts as a negative regulator of BR signaling, was targeted using RNAi. bin2- RNAi lines showed shorter stem internodes than their wild-type (WT) siblings, while male inflorescence internodes were elongated, making tassel branches longer. We observed elongated and wider leaf blades with crenulated margins, elongated leaf sheathes, and expanded auricles. In addition, bin2-RNAi plants show some interesting kernel traits, including increased embryo size and opaque endosperm with enlarged malformed starch grains. A robotic imaging system was used to demonstrate that the bin2-RNAi plants showed altered sensitivity to BR. bin2-RNAi plant phenotypes appeared epistatic to those of bri1-RNAi transgenic lines, consistent with their relative functions in BR signaling

Soybean gall midge control research efforts

Soybean gall midge, Resseliella maxima Gagné (Diptera: Cecidomyiidae), is a pest of soybean, Glycine max (L.) Merr. (Fabales: Fabaceae). Yield loss from this recently described fly was documented in Nebraska commercial soybean in 2018. As with any new economically important insect, much of the ecology and management is unknown. What is known is the female adult midges oviposit eggs into the base of soybean stems; eggs hatch and developing larvae consume the plant from the inside. This feeding injury causes plant wilting and potentially death. The larvae are protected inside the stem and makes conventional management tactics largely ineffective or inconsistent. The objective of this thesis is to contribute to the preliminary understanding of potential management tactics aimed at reducing the severity and ultimately economic losses impact of soybean gall midge. The first study evaluates the potential impact of preseason tillage in reducing the overwintering soybean gall midge population’s emergence. Two tillage implements and several tillage timings were compared at two locations in western Iowa. Low capture rates of adult midges in 2021 and 2022 make it difficult to determine if tillage is a viable suppression method. The next study determines whether or not two in-season tillage methods mitigate soybean gall midge infestation and plant injury by creating a barrier of soil around the soybean stem’s stem fissures where the soybean gall midge oviposits. The first tillage treatment, hilling, was found to move more soil than the second treatment, in-row cultivation. However, both treatments only caused a significantly lower larval incidence and percentage of plants wilted when compared to no management when soybean gall midge levels are relatively high. No yield response was seen among treatments as well. The third study assesses the methods and effectiveness of banding two non-insecticidal products directly onto the soybean stem to act as a barrier or deterrent to lower larval incidence and soybean injury. The banding process was successful, but no differences were shown between any treatments and next steps are examined

Performance and resilience of transmission tower systems under extreme weather events

The failure of transmission line systems resulting from severe weather conditions like derechos, hurricanes, and other extreme wind events has led to significant and widespread power outages. Thus, it is imperative to study the performance and resilience of these systems under such extreme weather events. The first study introduces a quasi-steady approach that combines aerodynamic force coefficients obtained from wind tunnel tests conducted in a straight-line configuration with empirically derived tornado wind speed profiles. The aim is to estimate the temporal variation of aerodynamic loads on lattice structures. This methodology proves particularly valuable for large and complex structures that cannot be feasibly modeled at realistic scales within the limited number of tornado simulation facilities worldwide. To accomplish this, the experimentally derived tornado wind speed profiles were extracted from a laboratory tornado wind field. Wind tunnel experiments were then conducted to evaluate the aerodynamic force coefficients of different sections of a lattice tower structure for various wind directions. The proposed method subsequently utilized the empirical tornado wind speed profiles and the measured aerodynamic force coefficients for each tower segment to calculate the time-varying wind forces acting on a model lattice tower. The calculation was performed for different orientation angles relative to the tornado's mean path, with the wind field at the tower's location being updated at each time step as the tornado passed by. The second study introduces a novel moment-matching technique designed to address these uncertainties and estimate the structural fragility of a transmission tower system. Nonlinear buckling analysis is conducted to identify limit states. Wind-load models are employed to consider coherence in both horizontal and vertical directions. Fragility analysis of the transmission tower system takes into account variability in structural parameters and wind loads. Realistic drag coefficients, determined from wind-tunnel tests conducted on the tower system under study, are utilized for the analysis. The influence of adjacent towers is also taken into consideration to incorporate more realistic boundary conditions. The study finally presents fragility curves for various wind directions, considering two states of the system: one with a balanced load (i.e., intact) and another with unbalanced forces (i.e., broken conductors). These curves provide insights into the vulnerability of the transmission line system under different wind conditions, allowing for a better understanding of its failure probability and resilience. The third study aims to assess the resilience of these systems by quantifying the probability of transmission tower failures caused by straight-line winds. While much research has focused on network-level cascade failures in power risk analysis, the analysis of physical cascade failures has been relatively overlooked. However, such events not only increase the likelihood of network-level cascade failures but also hinder the recovery process, as multiple towers (sometimes tens) may need to be replaced before power can be restored, which can be time-consuming. The probability of subsequent towers failing in a cascade is evaluated based on these fragility curves. Typically, strong anchor towers located at the ends of the tower line are implemented as a measure to prevent such cascade failures. These anchor towers are designed to withstand extreme conditions or unbalanced loads. In this study, the use of anchor towers is employed to optimize the design of the transmission tower line, reducing the risk of cascade failure while maintaining cost-effectiveness in mitigation efforts. The fourth study focuses on assessing the potential physical damage caused by combined hazards, specifically ice accretion and wind, on transmission tower system using finite element analysis. Monte Carlo simulation is employed to evaluate fragility functions for each component, including towers, conductors, and insulators, as well as the entire system under the combined effects of wind and ice. The study investigates the influence of wind speed, wind direction, and ice accretion magnitude on the probability of tower failures. These fragility functions provide valuable insights into the likelihood of transmission tower system failures and the possibility of domino effects along the transmission line. Additionally, by combining fragility functions with joint probability distributions of wind and ice loads, failure probabilities of transmission tower system are determined. The joint probability distribution for wind and ice load is generated for Midwest United States using copula functions which are combined with 3D fragility surfaces for the transmission tower system to obtain the failure probability of the system

AIIRA: AI Institute for Resilient Agriculture

AIIRA seeks to transform agriculture by creating a new AI-driven framework for modeling plants at various agronomically relevant scales. We accomplish this by designing and deploying AI-driven predictive models that fuse diverse data with siloed domain knowledge. AIIRA's vision, illustrated in Figure 1, consists of four technical thrusts with cross-cutting education, training, and outreach activities. Our activities are focused on theory, algorithms, and tools for the principled creation of goal-oriented AI tools deployed at plant and field scales. Our use-inspired AI developments are tightly integrated with USDA-relevant challenges in crop improvement and sustainable crop production. Our strong social science focus ensures sustained AI adoption across the ag value chain. Our cyberinfrastructure (CI) efforts ensure cohesive, sustainable, and extensible CI to reproducibly share and manage data assets and analysis workflows to a diverse spectrum of the Ag community. Taken together, this will ensure long-term payoffs in AI and agriculture. AIIRA has established a new field of Cyber Agricultural Systems at the intersection of plant science, agronomics, and AI. Our signature activities build the workforce for this new field through formal and informal educational activities. Through these activities, AIIRA creates accessible pathways for underrepresented groups, especially Native Americans and women.This article is published as Ganapathysubramanian, Baskar, Jessica MP Bell, George Kantor, Nirav Merchant, Soumik Sarkar, Patrick S. Schnable, Michelle Segovia, Arti Singh, and Asheesh K. Singh. "AIIRA: AI Institute for Resilient Agriculture." AI Magazine (2024). doi: https://doi.org/10.1002/aaai.12151. © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial Licens

Unveiling the racism within: Black Chief Diversity Officers’ experiences with race and racism at Predominantly White Institutions

Institutions of higher education continue to grapple with how to most effectively meet their diversity, equity, and inclusion (DEI) priorities. Colleges and universities have turned to Chief Diversity Officer (CDO) as the institutional leaders to champion to provide leadership for creating more diverse, equitable and inclusive experiences and outcomes for their campus constituents. However, little is known about the inclusion and equity that CDOs experience within their roles. This study aims to expand the literature on Chief Diversity Officers (CDOs)in higher education (see for example, Nixon, 2017; Williams & Wade-Golden, 2013). This study seeks to contribute to the CDO literature by providing insights into what is known about Black CDOs’ experiences at PWI, and how their racial identities shape those experiences. I argue that Black CDOs’ experiences are shaped by racism. To build on the existing literature on CDOs, I investigated how Black CDOs’ made sense of their experiences at PWIs, as well as the role that race played in shaping those experiences. In this study, I used semi-structured interviews and interpretivist phenomenological analysis (IPA) to gain insights into the experiences of 14 Black CDOs, who work across the United States at institutions of varying sizes, about their experiences. I used Critical Race Theory (CRT) to center and investigate the role of race throughout the study. Six findings emerged from this study. First, CDOs personal experiences with racism influenced their career trajectories into DEI work and ultimately into the CDO role. Second, the sociopolitical climate influences CDOs’ experiences by dictating the type of work they do within their roles. Third, CDOs experience racism within their roles. The fourth finding involves the toll that racism takes on CDOs’ mental and physical health. Fifth, CDOs’ greatest success involves contributing to dismantling systems of racial oppression on their campuses. Lastly, the six theme that emerged was that to sustain themselves within their experiences, CDOs form relationships with one another. These findings demonstrate that although CDOs have successes in their roles, they experience many challenges, based on racism. Institutions of higher education must content with racism and find ways to support their CDOs or risk these leaders burning out. Additionally, the findings highlight that based on legal environment, the future of DEI and the CDO role are uncertain. Future research should explore how the endemic racism within CDOs’ experiences contributes to the complexities of an unknown future of DEI as well as to their burnout

Improve the detection of key nitrogen cycling genes from agricultural soil systems

Nitrification is a crucial process in the global nitrogen cycle, converting ammonia into nitrite and subsequently into nitrate. Its significance is particularly evident in agricultural settings, where it links the use of ammonia-based fertilizers to nitrate leaching into water bodies, potentially causing eutrophication. Ammonia oxidation, the initial and rate-limiting step in nitrification, transforms ammonia into hydroxylamine. Ammonia-oxidizing bacteria (AOB) and their ammonia monooxygenase subunit A (amoA) genes are one of the key drivers of this process. Despite advancements in sequencing techniques that have facilitated the discovery of a broader range of diverse amoA sequences, in-depth exploration of the known amoA diversity, particularly within agricultural soils, remains constrained. Polymerase chain reaction (PCR), a widely used technique for quantifying and studying microbial functional genes in environmental samples, heavily relies on primer pairs. The choice of primers is critical, as some may only capture partial gene sequences within a function group, potentially introducing bias. In my thesis, I aim to evaluate the overall known diversity of bacterial amoA and identify specific diversity patterns in different soil types, with a focus on agricultural soils. I also seek to enhance PCR-based methods for capturing important amoAs in these settings, with a focus on optimizing primer pairs. This thesis comprises five chapters designed to accomplish the defined objectives. In Chapter 1, I conducted a comprehensive review of existing literature pertaining to the functional role of microbial communities in nitrogen cycling and the commonly employed methods for studying microbial functional genes within environmental contexts. To gain insights into the full scope of known amoA-AOB diversity, I grouped all the publicly accessible amoA genes into 1,242 clusters and used representative genes from these clusters to represent the overall known amoA diversities. It's noteworthy that less than 3% of these amoA genes were associated with well-studied bacteria with reference genomes. Furthermore, while certain amoAs persisted across various soil types, agricultural soils contained unique amoA genes distinct from those found in grassland or forest soils. This work is summarized in Chapter 2. Considering the diverse range of amoA genes observed in various soil environments, there arose a necessity to pinpoint the genes strongly associated with the specific environment and to subsequently refine primer design for their quantification using PCR-based methods. In Chapter 3, I introduced MetaFunPrimer, a software designed for primer development tailored to target gene sequences based on environmental metagenomic data. The details of this work are summarized in Chapter 3. In Chapter 4, I delved deeper into the study of amoAs in agricultural soils. From the analysis of 244 typical agricultural soil metagenomes and 11 in-house bioenergy site soil metagenomes, I identified one set of predominant amoAs in each setting that could not be amplified with previously published primers through in-silico PCR. After designing two sets of primers for the predominant amoAs in the 244 typical agricultural and 11 bioenergy soils using MetaFunPrimer, in-silico PCR demonstrated successful capture of the majority of these amoAs in these environments. These results highlight the potential of using metagenomes to guide the selection of target gene sequences for primer design as an effective approach to improve the detection of functional genes in PCR-based methods. In the final chapter, I consolidate the conclusions drawn from all the studies presented in this thesis and provide recommendations for future directions to further expand upon this research

Application of improved biphasic gel systems in beef and plant-based burger patties

Fats have been a major concern among consumers for decades. Due to this concern, products with label claim such as “reduced fat” have increased in popularity within the shelves of retail stores across the world. In addition, consumers are also opting for meat products with higher amount of unsaturated fats, which would include products such as fish, poultry, and pork. Furthermore, in plant-based protein products, commercial producers are straying away from fat mimetics such as coconut oil and palm oil because of their saturated fat content, and its association with cardiovascular disease. Current research, has shown the development and application of a semi-solid system using containing immiscible phases called biphasic gels (BPG) in meat products. The gel is typically comprised of an aqueous phase, hydrogel (HG), and a lipid phase, oleogel (OG). BPGs have been tested very heavily in pharmaceutical application and in some food applications, thus, the purpose of this research was to evaluate the performance of BPGs in coarse-ground beef burger patties and plant-based burger patties under various storage conditions. The objective was to apply the biphasic gels in beef and soy-based burger patties that resemble commercial formulations and assess their performance in these products under refrigerated and frozen storage conditions

Leveraging connected vehicle telematics for high-speed signalized intersection safety: A quantitative analysis of hard-braking events and crash occurrences

This study utilizes connected vehicle (CV) data to capture vehicle hard braking at high-speed signalized intersections in Iowa. The temporal stability of eight-week period of CV data was obtained by comparing the correlation between hard braking and crashes, and it is worth noting that hard braking events occur more frequently at intersections near schools. At the same time, eight weeks of CV data are compared with crash data from previous years to provide the ability to predict crashes over the next year. These results demonstrate the predictive power of CV data for crashes and its important role in traffic safety, thereby helping to improve overall road safety

An investigation of IoT device vulnerabilities and how to prevent them in the future

The Internet of Things (IoT) is an increasingly widespread phenomenon involving computing devices that interact with the real world and also network with other computers. These types of devices introduce new security risks, such as cyberattacks that impact the physical world. In spite of these risks, IoT security has remained underdeveloped, leading to the rise of immense botnets and footholds in household and corporate networks the world over. Meanwhile, researchers often use IoT security as a trial ground for blockchain and machine learning, rather than tracking the attacks happening in the world today and figuring out how to stop them in the future. This dissertation addresses that gap. It begins with a concrete definition of an IoT device, then exhaustively surveys IoT device vulnerabilities published over an eighteen-month period. A thorough analysis of those vulnerabilities guides three paths toward IoT security. The first is an experimental evaluation of static analyzers based on their ability to detect known vulnerabilities and their usability by software developers. Next is a set of simple but effective guidelines for IoT device software developers to prevent or mitigate the most common weaknesses. Last is a series of hands-on IoT security lab exercises for an undergraduate class to train future security professionals in how to secure networks that include IoT devices

Novel ultrasonic bat deterrents based on aerodynamic whistles

Novel ultrasonic deterrents to mitigate bat mortality at wind farms are proposed, designed, fabricated, and examined. The deterrents are founded on Class II and III aerodynamic whistles. In a Class II whistle, the acoustic feedback is provided by the sound generator, whereas in a Class III whistle, a resonating chamber or sound reflector is incorporated into the acoustic feedback loop to amplify the sound generated. A comprehensive experimental and numerical study is performed to assess the ultrasound generation capacity and to understand the sound generation mechanisms of the deterrents. Two categories of deterrents are developed: active and passive. The active deterrents/whistles rely on externally supplied pressurized air, while the passive deterrents are designed to be mounted on wind turbine blades and harness the kinetic energy of the blade-relative airflow to excite resonance. The active whistles use the principle of Helmholtz resonance. The baseline active whistle design comprises two cavities (resonating chambers) that are located opposite each other. The chambers operate out-of-phase and cancel out the fundamental (Helmholtz resonance) frequency; the lowest (peak) frequency of radiated sound is, therefore, the second harmonic of the Helmholtz resonance frequency. The peak frequency is adjusted by modifying the whistle geometry. The flow instability and resonance in the whistle are numerically simulated by solving the unsteady Reynolds-averaged Navier-Stokes (RANS) equations, and the radiated noise is obtained by employing the Ffowcs Williams-Hawkings acoustic analogy. The numerical simulations are complemented and verified with experimental measurements of farfield radiated sound measured in the anechoic chamber at Iowa State University. Since different bat species use different frequencies for echolocation, a deterrent covering a wide range of frequencies is desirable. A six-whistle ultrasound deterrent, targeting six frequencies in the 20−50 kHz range, is designed, fabricated, and experimentally evaluated. The deterrent successfully produces a multi-tone spectrum covering the desired frequency range. Modulating the frequency of the ultrasonic deterrence signal is believed to reduce the chance of bats getting habituated. Frequency modulation of the active whistle by temporally varying the pressure of the supply air is explored. The temporal variation in the radiated sound follows the imposed inlet pressure modulation at small values of the modulation frequency (< 400 Hz); the process is nearly quasi-steady because of the significant separation between the time scales of the modulation signal and Helmholtz resonance. However, nonlinear effects become prominent at higher frequencies, resulting in a nearly steady spectrum with multiple tones around the peak frequency. Two concepts of passive deterrents are explored: one featuring a converging-diverging (C-D) flow channel and the other exposing the resonator directly to the airflow over the blade. Numerical investigation of the C-D nozzle shows that the small size restricts the flow rate and, hence, the ultrasound generation capacity. The second design does not suffer from this limitation. However, it has resonators only on one side, and phase cancellation cannot be used to remove the fundamental frequency. The resonator size is, therefore, geometrically scaled down to push the fundamental frequency into the ultrasonic range. Numerical investigations of this passive whistle concept reveal that the noise generation mechanism is cavity resonance, with the first Rossiter mode being primarily excited. The passive whistle is experimentally investigated in the Stability Wind Tunnel at Virginia Tech. The whistle is mounted on an airfoil model and tested at various wind speeds. Several interesting phenomena, such as cut-on wind speed, etc., are observed and explained using a combination of experimental data and numerical simulations. The proposed passive whistle shows promise as a bat deterrent when mounted on wind turbine blades