University of Mount Union

Needle pathogens cause the discoloration, death, or premature abscission of conifer foliage, reducing growth and vigor, and repeated defoliation may eventually result in tree mortality. Since 2016, forest managers in the southeast United States have reported an increasing scale, frequency, and severity of needle disease outbreaks on the region’s principal timber species, loblolly pine (Pinus taeda L.). These recent outbreaks are raising concern throughout the region, as needle diseases are not traditionally considered a threat to P. taeda. Lecanosticta acicola (Thum.) Syd., the native causal agent of brown-spot needle blight, has been recovered from some outbreaks, however, the full array of fungi associated with symptoms has not been explored. In this research, P. taeda foliage was collected from affected stands throughout the region and analyzed to identify fungi associated with needle disease symptoms. We employed targeted molecular diagnostics, to confirm the presence or absence of L. acicola, and DNA metabarcoding, to characterize the foliar mycobiome and screen for other potential pathogens. Lecanosticta acicola was detected among symptomatic needles from multiple states, particularly in western portions of the P. taeda range but rarely from stands in eastern states. Metabarcoding revealed pathogens in needles and identified associations among pathogenic fungi, differing symptoms, including needle discoloration and necrosis, and signs of fungal fruiting bodies. Additionally, the fungal community of needles varied with patterns of symptom presentation. This study is the first regionwide assessment of fungi associated with recent large-scale needle disease outbreaks on P. taeda and identifies multiple pathogens that warrant further study.

Background: Gait speed or 6-minute walk test are frequently used to project community ambulation abilities post-stroke by categorizing individuals as household ambulators, limited, or unlimited community ambulators. However, whether improved clinically-assessed gait outcomes truly translate into enhanced real-world community ambulation remains uncertain. Objective This cross-sectional study aimed to examine differences in home and community ambulation between established categories of speed- and endurance-based classification systems of community ambulation post-stroke and compare these with healthy controls. Methods: Sixty stroke survivors and 18 healthy controls participated. Stroke survivors were categorized into low-speed, medium-speed, or high-speed groups based on speed-based classifications and into low-endurance, medium-endurance, or high-endurance groups based on the endurance-based classification. Home and community steps/day were quantified using Global Positioning System and accelerometer devices over 7 days. Results: The low-speed groups exhibited fewer home and community steps/day than their medium- and high-speed counterparts (P < .05). The low-endurance group took fewer community steps/day than the high-endurance group (P < .05). Despite vast differences in clinical measures of gait speed and endurance, the medium-speed/endurance groups did not differ in their home and community steps/day from the high-speed/endurance groups, respectively. Stroke survivors took 48% fewer home steps/day and 77% fewer community steps/day than healthy controls. Conclusions: Clinical classification systems may only distinguish home ambulators from community ambulators, but not between levels of community ambulation, especially beyond certain thresholds of gait speed and endurance. Clinicians should use caution when predicting community ambulation status through clinical measures, due to the limited translation of these classification systems into the real world.

SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km²), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

High impact practices (HIP) are evidence-based teaching and learning tools which improve learning and student engagement. The objective of this study was to investigate the use of HIP in undergraduate introduction to exercise science classes. The student experience in introductory major courses can impact their persistence and success in the discipline, thus the use of HIP in these courses can be formative. It was hypothesized that the number of HIP used would differ based on institution type, instructor type, and class size. It was also hypothesized that collaboration would be the most frequently used HIP due to ease of implementation in the classroom. Faculty were electronically surveyed about the type and number of HIP used in their introductory course and information relative to instructor status and class size. The sample included 182 courses. Institution types were classified as either four-year public institutions ( n = 68), four-year private institutions ( n = 97), or community colleges ( n = 17). The most common instructor type was tenured/tenure-track ( n = 100), followed by full time term ( n = 39), adjunct ( n = 2), and different instructor types teaching the class ( n = 41). Class sizes were grouped in the following categories: 0-25 students ( n = 84), 26-50 ( n = 71), 51-75 ( n = 11), 76-100 ( n = 3), 101+ ( n = 13). Instructors reported an average of 1.3 + 1.2 HIP were used in the courses. There was a significant difference between the number of HIP used and institution type (F (2, 41.73) = 3.98, p = 0.026). Instructors at four-year public (1.03 + 0.98; p = <0.001) and four-year private (1.26 + 1.14; p = 0.005) institutions reported incorporating significantly fewer HIP than instructors at community colleges (2.12 + 1.58). There was no significant difference between number of HIP used and instructor type (F (3, 5.106) = 2.56, p = 0.166). There was a significant difference between number of HIP used and students per section (F (4, 13.37) = 4.54, p = 0.016) with 0-25 student sections (1.4 + 1.18; p = 0.024) and 26-50 student sections (1.32 + 1.20; p = 0.043) having significantly more HIP used in the courses than courses with 101+ student per section (0.62 + 1.17). At the 123 institutions where it was reported that HIP were used, 79.7% used collaboration ( n = 98). These data identify the use of HIP more frequently in introduction to exercise science classes with fewer than 50 students and those taught at community colleges. As many students may begin their degrees at community colleges, this use of HIP can be supportive of students remaining in the degree track. Further, the use of HIP, such as collaboration, in smaller classes may encourage relationships that may also help students feel more positively about the degree. No funding or support mechanism was used for this project. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Video streaming has become increasingly popular with the proliferation of online platforms and the widespread availability of high-speed Internet connections. However, delivering high-quality video content over limited network resources remains a challenge. In this paper, we propose a novel approach to boost video streaming efficiency through machine learning-based resource allocation. Our approach leverages the power of machine learning algorithms to dynamically allocate network resources based on various factors such as network conditions, video content characteristics, and user preferences. By intelligently adapting the resource allocation in real-time, we aim to optimize video streaming performance and enhance the overall user experience. The experimental results demonstrate that our machine learning-based resource allocation approach outperforms existing methods in terms of key performance metrics such as video quality, buffering time, and overall user satisfaction. Through intelligent resource allocation, our approach effectively mitigates video stalling and buffering issues, leading to smoother video playback and reduced quality degradation during adverse network conditions.

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.

Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.

In this article, we analyze the downlink transmission of a wirelessly powered hybrid cooperative non-orthogonal multiple access (C-NOMA) system relying on reconfigurable intelligent surfaces (RIS), where self-interference related to full-duplex (FD) and power efficiency are also considered. The network contains a single-antenna base station (BS), a RIS with many reflecting elements, a power beacon, and two users that can function in FD in comparison with the benchmark, i.e. half-duplex mode. In the proposed system, there are two communication paths from the BS to the far-user either through the RIS or via a near-user employing energy-harvesting from a power beacon. To comprehend the performance of the proposed system, we study and compare the outage probability under various parameters of interest such as power allocation coefficients, energy harvesting coefficients, number of RIS reflecting elements, and transmission rates. We aim to show the trade-off between these main parameters and system outage performance. Moreover, to highlight the advantages of the wireless-powered RIS-aided CNOMA system, we compare it against with benchmark, i.e. a wireless-powered RIS-aided cooperative orthogonal multiple access (C-OMA) system. All derived closed-form outage expressions are verified by employing Monte-Carlo simulations. Numerical results show that: (1) wireless powered RIS-aided FD C-NOMA is superior to the wireless-powered RIS-aided FD C-OMA in terms of outage probability in the high signal-to-noise ratio (SNR) region; and (2) in delay-to-tolerant transmission scenarios, the energy efficiency of the proposed wireless powered RIS FD C-NOMA outperforms the wireless powered RIS FD C-OMA in the high SNR region.

This paper provides a study of the latest target (object) detection algorithms for underwater wireless sensor networks (UWSNs). To ensure selection of the latest and state-of-the-art algorithms, only algorithms developed in the last seven years are taken into account that are not entirely addressed by the existing surveys. These algorithms are classified based on their architecture and methodologies of operation and their applications are described that are helpful in their selection in a diverse set of applications. The merits and demerits of the algorithms are also addressed that are helpful to improve their performance in future investigation. Moreover, a comparative analysis of the described algorithms is also given that further provides an insight to their selection in various applications and future enhancement. A depiction of the addressed algorithms in various applications based on publication count over the latest decade (2023-2013) is also given using the IEEE database that is helpful in knowing their future application trend. Finally, the challenges associated with the underwater target detection are highlighted and the future research paradigms are identified. The conducted study is helpful in providing a thorough analysis of the underwater target detection algorithms, their feasibility in various applications with future challenges and defined strategies for further investigation.

This paper considers an electro-thermo-geometrical Multiphysics analysis of electromagnetic compatibility (EMC) resonance problem solution by using bandpass (BP) type negative group delay (NGD) equalization method. The rectangular cavity electric model based on EMC frequency domain S-parameter analysis is introduced. The unfamiliar BP-NGD function is specified in order to size the lumped electrical components of the suitable RLC-network based topology. The BP-NGD equalization principle is described including the Multiphysics synoptic analysis by means of electro- thermo-geometrical approach of the problem. The BP-NGD equalization methodology is proposed. The feasibility study of the EMC resonance equalization method is validated by considering a proof-of-concept constituted by 232.9×28×3.8 cm-size rectangular cavity. The BP-NGD active circuit is designed as equalizer by using RLC-series network. The EMC solution is verified by the BP-NGD POC specified by -4 ns NGD value at 0.644 MHz center frequency stating resonance effect reduction with 1-dB flatness. Furthermore, time-domain signal integrity (SI) analysis confirms the EMC cavity resonance resolution by showing output delay, over/under shoot reduction and also input-output cross correlation improvement from 89% to 99%.

This paper explores an original research work on multiband frequency metallized-dielectric permittivity measurement method for copper-plated substrates based on bandpass (BP) negative group delay (NGD) ring circuit. The BP-NGD topology is modelled by S-parameter. The unfamiliar specifications of the BP-NGD function are recalled. The extraction formula of permittivity within multiband frequency is established from NGD center frequency harmonics. The BP-NGD function specifications are defined. The BP-NGD ring resonator (RR) proof-of-concept (PoC) consists of linearly coupled loops implemented in microstrip technology. The multiband permittivity measurement NGD-method validity is proved by well-agreed full wave simulations and measurements of NGD RR prototype. An application of the multiband permittivity measurement NGD-method using Rogers® material-based commercial substrate is presented. The test results of dielectric sample are validated at 1-10 GHz. Furthermore, uncertainty analyses under the maximum processing error are performed. The effectiveness of the NGD-method is confirmed by showing a relative permittivity measurement error of less than 0.5%.

Recently, intelligent reflective surface (IRS)-aided systems are becoming a prospective technology in realizing for sixth generation (6G) wireless communication era because of extremely low power transmission, seamless coverage and their superiority. These network systems can allow many users and devices to connect to each other, extending the coverage. To empower IRS-aided systems, non-orthogonal multiple access (NOMA) can be leveraged to work with IRS technique enabling further benefits such as mass connectivity, flexible resource allocation and improved performance. Increasing connected devices and expanding coverage means devices have the potential to interfere with each other. Recent studies focusing on researching and analyzing the performance of the IRS-supported NOMA network have not taken into account or not fully calculated the impact of interference on system performance. In this study, we first analyze the effect of co-channel interference (CCI) at users in downlink IRS-NOMA systems. In particular, the CCIs generated by the terminals deployed randomly in the coverage area affect the signal reception at the user in the downlink. In this network model, the channel conditions that follow the Rayleigh distribution and the CCI statistical model are independent and identically distributed. We analyze and evaluate network performance by extracting closed-form expressions of outage probability, ergodic capacity, total achievable rate then highlighting the adverse effects of CCI on IRS-NOMA. In addition, to improve the performance of the IRS-NOMA downlink, we present a framework of theorical analysis to look more insights of users’ performance, i.e. diversity order. Our analytical derivatives are verified through computer simulations based on Monte-Carlo and intuitive comparisons with the benchmarks.

Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide and 1.5 times more males than females. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. Here we explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional networks identified by a recent study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis.