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Autonomous mowing system response to simulated traffic and sod production advancement through fertility, growth regulator and pre-emergent integration
No full textSports field managers want to provide a safe and aesthetically pleasing playing surface. Limits on budgets and a tight labor market make this a challenge. Recently, autonomous mowers have entered the market. Their lightweight body, electric battery and automated run times make them a potential replacement for traditional rotary mowers. A study conducted at the Iowa State Horticulture Research Station in Ames, Iowa in 2023 and 2024 investigated if there were any benefits or detriments that come with the use of autonomous mowers on sports fields in performance and safety of the playing surface. Studies were conducted on two different species of turfgrasses, ‘Rush’ Kentucky bluegrass (Poa pratensis L.) and ‘Snap Back’ tall fescue (Schedonorus arundinaceus [Schreb.]). Results from these studies showed a few differences on select rating dates but no consistent differences, as a result, it can be concluded that autonomous mowers will provide a similar playing surface and performance compared to traditional rotary mowers under simulated traffic.
Repeated foot traffic can reduce cover on a playing surface, even with proper care and maintenance. When a sports field manager renovates an athletic field to improve turfgrass cover, there is a need for the best sod product possible. Kentucky bluegrass sod was grown on a sand-capped field for twelve months and subsequently cut at the Iowa State Horticulture Research Station in Ames, Iowa in 2024 and 2025. Sod tensile strength was measured for each plot to find a combination of products that created a stronger sod roll. Treatments included a granular fertilizer (GF) and a liquid fertilizer (LF), a product including gibberellic acid, Indole-3-Butryic acid and kinetin (HM) and gibberellic acid inhibitor (TE), pre-emergence herbicide (PRE) and no PRE. The percentage of green cover data increased as the study progressed, with GF treatments providing greater cover on many dates. The results also indicated that the application of HM can increase PGC, TE has minimal PGC difference as opposed to no TE and PRE did not improve PGC. Sod strength differed between years. In 2024, there was no difference in sod strength. In 2025, TE (49 kg), TE+HM (49 kg), HM (49 kg), PRE+TE (48.6 kg), GF (48.1 kg), LF (46.9 kg), PRE (45.4 kg), PRE+TE+HM (44.5 kg), LF+PRE+TE (43.6 kg) had greater sod tensile strength than LF+PRE (29.5 kg). There was almost a 1.6-times greater sod strength for those treatments than LF+PRE. For volumetric water content (VWC), HM (37.1%) had greater soil moisture than LF+TE (33.8%), PRE+HM (33.7%) and TE (33.3%), while LF+PRE+TE (36.1%) had greater soil moisture than TE (33.3%). There were no differences for VWC in 2025. While these VWC values are statistically significant, they are small numerically. A sod producer should apply GF with the use of TE and HM to increase sod strength and PGC while not worrying about the loss of sod strength from applications of PRE. These studies can help sports field managers better manage athletic fields for safety and performance by providing the most turfgrass cover possible
An input-aware dynamic program graph representation for optimizing code performance using heterogeneous GNNs
No full textDue to the surge of heterogeneous High-Performance Computing (HPC) platforms, there has been a growing need to develop performant programs. More specifically, there has been a growing demand for identifying the right device-type (e.g., CPU or GPU) and also the optimal configurations (e.g., Vectorization Factor, Interleave Count) for a specific program. Low-level program representation (e.g., LLVM-IR) has been used to tackle some code optimization tasks; however, there has not been much effort to integrate dynamic analysis information with LLVM-IR for such cases. In this work, we propose an input-aware dynamic program graph `PerfoGraph+': which leverages Dynamic analysis information to construct a flow-aware Program Graph for identifying optimal configurations within a source program. The program graph is constructed using fine-grained LLVM-based Intermediate Representation (IR) along with dynamic analysis information. We evaluate PerfoGraph+ on code performance optimization tasks, including Vectorization and Interleave Factor prediction and CPU/GPU based parallelism detection. Our empirical findings suggest that PerfoGraph+ achieves around 12.3% and 50.4% better performance gain compared to other state-of-the-art models on these code optimization tasks, respectively
A therapeutic antisense oligonucleotide encompassing 2′-O-methoxyethyl modification triggers unique perturbation of the transcriptome
No full textNusinersen, an antisense oligonucleotide (ASO) encompassing a phosphorothioate backbone and 2′-O-methoxyethyl (MOE) modifications, is commonly used for the treatment of spinal muscular atrophy (SMA), the leading genetic cause of infant mortality. Nusinersen acts through prevention of skipping of exon 7 of Survival Motor Neuron 2 (SMN2) by sequestering Intronic Splicing Silencer N1 (ISS-N1), located within SMN2 intron 7. Here, we report transcriptome-wide perturbations triggered by ISS-N1-targeting ASOs incorporating diverse modifications, including F18MOE, an 18mer ASO with identical sequence and chemical composition to that of nusinersen. Among cellular processes most impacted by F18MOE were cell cycle, cell growth, cell signaling, and maintenance of the cytoskeleton, chromosomes, and organelles. We demonstrate sequence-dependent and MOE modification-specific off-target effects of F18MOE on transcription and splicing. Owing to unique tolerance for mismatch base pairing with exonic targets, F18MOE triggered skipping of multiple exons, supporting the unexpected role of ISS-N1-like sequences as exonic splicing enhancers. We show that shortening of an ASO suppresses its effect on off-target splicing. Further, we demonstrate using ASOs of mixed chemistry that different MOE-modified regions drive the effect of F18MOE on off-target splicing of different exons. Our findings are instructive in designing future ASO-based therapies and for uncovering novel splicing cis-elements.This article is published as Ottesen, Eric W., Wren A. Murzyn, Robert L. Kaas, Keaton J. Bertrand, Jessica L. Payne, and Ravindra N. Singh. "A therapeutic antisense oligonucleotide encompassing 2′-O-methoxyethyl modification triggers unique perturbation of the transcriptome." NAR Molecular Medicine 3, no. 1 (2026): ugag002. doi: https://doi.org/10.1093/narmme/ugag002.National Institutes of Health [R01 NS055925 and R03 NS136717]; Funding to pay the Open Access publication charges for this article was provided by Iowa State University Library
Two-Stage Wiener-Physically-Informed-Neural-Network (W-PINN) AI Methodology for Highly Dynamic and Highly Complex Static Processes
No full textOur new Theoretically Dynamic Regression (TDR) modeling methodology was recently applied in three types of real data modeling cases using physically based dynamic model structures with low-order linear regression static functions. Two of the modeling cases achieved the validation set modeling goal of r f it,vₐl ≥ 0.9. However, the third case, consisting of eleven (11) type one (1) sensor glucose data sets, and thus, eleven individual models, all fail considerably short of this modeling goal and the average r f it,vₐl, r f it,vₐl = 0.68. For this case, the dynamic forms are highly complex 60 min forecast, second-order-plus-dead-time-plus-lead (SOPDTPL) structures, and the static form is a twelve (12) input first-order linear regression structure. Using these dynamic structure results, the objective is to significantly increase r f it for each of the eleven (11) modeling cases using the recently developed Wiener-Physically-Informed-Neural-Network (W-PINN) approach as the static modeling structure. Two W-PINN stage-two static structures are evaluated–one developed using the JMP® Pro Version 16, Artificial Neural Network (ANN) toolbox and the other developed using a novel ANN methodology coded in Python version, 3.12.3. The JMP r f it,vₐl = 0.74 with a maximum of 0.84. The Python r f it,vₐl = 0.82 with a maximum of 0.93. Incorporating bias correction, using current and past SGC residuals, the Python estimator improved the average r f it,vₐl from 0.82 to 0.87 with the maximum still 0.93.This article is published as Hurd, Dillon G., Yuderka T. González, Jacob Oyler, Spencer Wolfe, Monica H. Lamm, and Derrick K. Rollins. 2026. "Two-Stage Wiener-Physically-Informed-Neural-Network (W-PINN) AI Methodology for Highly Dynamic and Highly Complex Static Processes" Stats 9, no. 1: 6. https://doi.org/10.3390/stats901000
Health Care Journeys of Veterans With Gulf War Illness
No full textBackground:
There is an acknowledged need to improve care for patients with persistent physical symptoms. Veterans who served in the 1990–91 Gulf War are a subpopulation of U.S. military Veterans who have been struggling with persistent physical symptoms for decades. The current study sought to characterize Veterans’ historic path through the health care system and current experience of care to identify opportunities to improve care.
Methods:
Analysis of interviews conducted with 31 Veterans who met criteria for Gulf War Illness (GWI) was conducted to understand Veterans’ health care journeys, from symptom onset to the present.
Results:
Early in their journey, Veterans felt uncertain about the nature of their condition and how to explain it to clinicians. Veterans described a cycle of referrals to specialists to pursue individual symptoms and subsequent return to primary care with few actionable findings. During this cycle, Veterans often felt dismissed or invalidated by clinicians. Over time, most Veterans felt care became increasingly fragmented, with multiple clinicians caring for them without a plan to manage GWI and little acknowledgement of GWI as a discrete illness. Further in their journey, some Veterans were referred to tertiary centers where they encountered a more holistic approach.
Conclusions:
Findings point to the need to shift care for Veterans with GWI, and similar conditions, away from overly focusing on individual symptoms. Instead, primary care clinicians need training and support, potentially from tertiary care experts, to develop and implement holistic care plans that recognize GWI as a complex chronic condition.This article is published as Bloeser, Katharine, Hyde, Justeen K., Helmer, Drew A., Bolton, Rendelle E., Lesnewich, Laura M., Phillips, L. Alison, Bayley, Peter J., Chandler, Helena K., Santos, Susan L., McFarlin, Mikhaela L., Reinhard, Matthew J., Stewart, Rachel S., McAndrew, Lisa M., Health Care Journeys of Veterans With Gulf War Illness. Medical Care 64(2S);S130-S136, January 2026. https://doi.org/10.1097/MLR.000000000000224
When beat meets bit: The role of acoustics in L2 intelligibility
No full textAchieving intelligible pronunciation in a second language (L2) is a primary goal for learners, yet the acoustic-phonetic factors that determine whether an L2 English vowel is understood remain underspecified. While theoretical models like the Speech Learning Model (SLM-r) and Perceptual Assimilation Model (PAM-L2) predict L1-specific difficulties, there is a need for research that quantitatively links learners' specific acoustic deviations to intelligibility outcomes as judged by both human listeners and automated systems. This dissertation investigated the acoustic predictors of L2 English vowel intelligibility by triangulating L2 production, L1 perception, and automatic speech recognition (ASR) performance. It aimed to (1) establish an L1 English-speaker acoustic-perceptual baseline, (2) identify the specific acoustic features that predict intelligibility for L2 speakers from four L1 backgrounds (Mandarin, Korean, Spanish, Arabic), and (3) evaluate the extent to which a transformer-based ASR like Whisper aligns with human perceptual judgments. Twelve L1 English and 48 L2 English speakers produced 96 CVC words containing tense-lax vowel contrasts. An acoustic analysis measured first and second formant frequencies (F1, F2) and vowel duration. The 5,760 tokens were presented to 120 L1 English listeners in a transcription task to gather 86,400 ratings of intelligibility and reaction time data. OpenAI’s Whisper system processed the same unique tokens. A series of mixed-effects models, ANOVAs, and a Linear Discriminant Analysis were fitted to analyze the data. The findings revealed a fundamentally different relationship between acoustic accuracy and intelligibility for L1 versus L2 speech. For L2 speakers, formant accuracy was the most stable predictor of success, whereas for L1 speakers, this relationship was not observed. A significant L1 and vowel-category interaction suggested that intelligibility patterns were systematically shaped by the speaker's L1 background, aligning with SLM-r and PAM-L2 predictions. The ASR system's weighting of acoustic cues appeared to converge with that of human listeners, though it was less tolerant of phonetic deviation for vowel category boundaries. The study indicated that L2 vowel unintelligibility may be rooted in the 'inherent subtlety' of the L1 English vowel system and is systematically predicted by learners' acoustic deviations from L1 targets. The strong human-ASR convergence offers validation for the potential use of automated systems for pronunciation diagnosis. Collectively, these results provide an empirical basis for developing acoustically informed Computer-Assisted Pronunciation Training (CAPT) systems designed to target specific high Functional Load vowel contrasts most critical for intelligible communication
FocFormer-UNet: UNet With Focal Modulation and Transformers for Ultrasound Needle Tracking Using Photoacoustic Ground Truth
No full textUltrasound (US)-guided needle tracking is a critical procedure for various clinical diagnoses and treatment planning, highlighting the need for improved visualization methods to enhance accuracy. While deep learning (DL) techniques have been employed to boost needle visibility in US images, they often rely heavily on manual annotations or simulated datasets, which can introduce biases and limit real-world applicability. Photoacoustic (PA) imaging, known for its high contrast capabilities, offers a promising solution by providing superior needle visualization compared to conventional US images. In this work, we present FocFormer-UNet, a DL network that leverages PA images of the needle as ground truth for training, eliminating the need for manual annotations. This approach significantly improves needle localization accuracy in US images, reducing the reliance on time-consuming manual labeling. FocFormer-UNet achieves excellent needle localization accuracy, demonstrated by a modified Hausdorff distance of 1.43 1.23 and a targeting error of 1.22 1.14 on human clinical dataset, indicating minimal deviation from actual needle positions. Our method offers robust needle tracking across diverse US systems, improving the precision and reliability of US-guided needle insertion procedures. It holds great promise for advancing AI-driven clinical support tools in medical imaging. The following is the source code: https://github.com/DeeplearningBILAB/FocFormer-UNet. Open Science Framework (OSF) provides datasets and checkpoints at: https://osf.io/yxt9v/.This is a manuscript of an article published as Mahim, S. M., Md Emamul Hossen, and Manojit Pramanik. "FocFormer-UNet: UNet With Focal Modulation and Transformers for Ultrasound Needle Tracking Using Photoacoustic Ground Truth." IEEE Transactions on Biomedical Engineering (2026). doi: https://doi.org/10.1109/TBME.2026.3652428
Mammalian-conserved Drosophila miR-6 regulation of LRP1 in the heart protects against normal cardiac aging
No full textMicroRNAs (miRNAs), short noncoding RNAs that posttranscriptionally regulate gene expression, have emerged as critical regulators of cardiac genes. Although circulating miRNAs have been implicated in cardiovascular disease, their precise functional roles remain poorly understood. Using Drosophila as a model, we applied miRNA sponge technology to competitively inhibit miR-6 (the mammalian homolog, miR-27), enabling us to systematically assess its impact on heart function, morphology, and lifespan. Functional and structural cardiac changes were analyzed with semiautomatic optical heartbeat analysis (SOHA) software and immunohistochemistry. In silico target analysis revealed 149 conserved predicted gene targets shared by this miRNA family, highlighting its potential regulatory scope. Our findings uncover a novel cardioprotective role for miR-6 inhibition, demonstrating that heart-specific miR-6 suppression mitigates age-related changes to heart size and function, significantly extends lifespan, and leads to increased lipid accumulation in cardiomyocytes. Importantly, we observed elevated expression of the conserved target gene low-density lipoprotein receptor-related protein 1 (LRP1) in miR-6-inhibited hearts, and genetic disruption of LRP1 expression in miR-6 inhibition decreased lipid accumulation in the heart. Conservation of miR-27b and LRP1B expression in mammalian cardiac tissue further validates the translational relevance of these findings.This article is published as Mammalian-conserved Drosophila miR-6 regulation of LRP1 in the heart protects against normal cardiac aging Alyssa M. Hohman, Jackson Komp, Beatriz Elliott, Swathy Krishna, M. Estefania Gonzalez-Alvarez, Aileen F. Keating, Joshua T. Selsby, and Elizabeth M. McNeill American Journal of Physiology-Heart and Circulatory Physiology 2026 330:1, H305-H316. https://doi.org/10.1152/ajpheart.00364.2025This study was supported by the American Heart Association Scientist Development Grant 16SDG27640000 (to E.M.M.); American Heart Association, Predoctoral Fellowship 916821 (to
A.M.H.); and College of Human Sciences Iowa State University Faculty Seed Grant (to E.M.M.)
Sidorenko property and forcing in regular tournaments
No full textWe give a complete characterization of tournaments H that have the Sidorenko property with respect to nearly regular tournaments, i.e., the homomorphism density of H among all nearly regular tournaments is minimized by a random tournament. Corollaries of our result are a positive answer to the question of Noel, Ranganathan and Simbaqueba whether there exist infinitely many non-transitive tournaments that are quasirandom forcing for nearly regular tournaments, and a negative answer to their question whether almost every tournament is quasirandom forcing for nearly regular tournaments.This preprint is from Král, Daniel, Matjaž Krnc, Filip Kučerák, Bernard Lidický, and Jan Volec. "Sidorenko property and forcing in regular tournaments." arXiv preprint arXiv:2602.12551 (2026). https://doi.org/10.48550/arXiv.2602.1255
Gpp34/Tpp35Ab1 resistance in the western corn rootworm: Inheritance, fitness costs, and quantitative trait loci mapping
No full textThe western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is a serious pest of corn and is currently managed with corn hybrids that produce insecticidal proteins derived from the bacterium Bacillus thuringiensis (Bt). Resistance to Bt corn that produces Gpp34/Tpp35Ab1 occurs throughout the Midwest United States, threatening the efficacy Bt corn to manage rootworm. In the United States, the refuge strategy is used to delay Bt resistance, with refuges of non-Bt host plants serving as a source of Bt-susceptible individuals. The inheritance of resistance strongly influences the rate of resistance evolution, with the greatest delays in evolution predicted when resistance is recessive. A second factor, fitness costs of resistance, acts to delay the evolution of resistance. The inheritance and associated fitness costs of Gpp34/Tpp35Ab1 resistance were assessed in one western corn rootworm strain with lab-selected resistance and two strains with field-evolved resistance. We found that Gpp34/Tpp35Ab1 resistance was non-recessive and accompanied by fitness costs. The non-recessive inheritance of Gpp34/Tpp35Ab1 may diminish the capacity of refuges to delay resistance and heighten the risk of resistance evolution. The genetic mechanisms of Gpp34/Tpp35Ab1 resistance remain unknown and were investigated in this dissertation using two strains of western corn rootworm with field-evolved Gpp34/Tpp35Ab1 resistance. Quantitative trait loci (QTL) mapping identified eight QTL intervals associated with survival on Gpp34/Tpp35Ab1 corn. Predicted co-localization of candidate Bt resistance genes included two ATP binding cassette transporter subfamily C members and a mitogen-activated protein kinase regulating gene. This dissertation suggests that future insect resistance management strategies for western corn rootworm could include the use of larger refuges and more diversified management approaches to reduce the risk of resistance evolution