Showing papers by "Brigham Young University published in 2022"

Social isolation: An underappreciated determinant of physical health.

Abstract: While a sizable body of research demonstrates the associations between social connection and health, much of the recent focus in the broader public and to some extent among academics has been on loneliness, with more objective/structural aspects often assumed to be proxies for more influential relationship factors such as relationship functions and quality. However, evidence suggests the actual presence of others (proximity and regular contact) is essential, and many studies document these structural indicators have just as powerful and, in some cases, more potent effects on indicators of health and well-being. This paper summarizes the evidence on social isolation and health and provides a framework for why social isolation may be a powerful predictor of health and mortality.

Limitations to Propagule Dispersal Will Constrain Postfire Recovery of Plants and Fungi in Western Coniferous Forests

Abstract: Abstract Many forest species are adapted to long-interval, high-severity fires, but the intervals between severe fires are decreasing with changes in climate, land use, and biological invasions. Although the effects of changing fire regimes on some important recovery processes have previously been considered, the consequences for the dispersal of propagules (plant seeds and fungal spores) in forest communities have not. We characterize three mechanisms by which changing fire regimes disrupt propagule dispersal in mesic temperate, boreal, and high-elevation forests: reduced abundance and altered spatial distributions of propagule source populations, less effective dispersal of propagules by wind, and altered behavior of animal dispersers and propagule predators. We consider how disruptions to propagule dispersal may interact with other factors that are also influenced by fire regime change, potentially increasing risk of forest conversion. Finally, we highlight urgent research topics regarding how dispersal limitation may shape twenty-first century forest recovery after stand-replacing fire.

The purple mangosteen (Garcinia mangostana): Defining the anticancer potential of selected xanthones

Abstract: The purple mangosteen (Garcinia mangostana) is a popular Southeast Asian fruit that has been used traditionally for its health promoting benefits for years. Unique to the mangosteen are a class of phytochemicals known as xanthones that have been reported to display significant anti-cancer and anti-tumor activities, specifically through the promotion of apoptosis, targeting of specific cancer-related proteins, or modulation of cell signaling pathways. α-Mangostin, the most abundant xanthone isolated from the mangosteen, has received substantial attention as it has proven to be a potent phytochemical, specifically as an anticancer agent, in numerous different cancer cell studies and cancer animal models. While the mechanisms for these anticancer effects have been reported in many studies, lesser xanthones, including gartanin, β-mangostin, γ-mangostin, garcinone C, and garcinone E, and mangosteen extracts from the pericarp, roots, rind, and stem show promise for their anticancer activity but their mechanisms of action are not as well developed and remain to be determined. Mangosteen products appear safe and have been well tolerated in human clinical trials where they show antioxidant activity, though their clinical anticancer activity has not yet been evaluated. This review summarizes the work that has been done to explore and explain the anticancer and antitumor activities of α-mangostin, lesser xanthones, and mangosteen extracts in vitro, in vivo, and in humans in various cancers.

Data Mining for Terahertz Generation Crystals

Abstract: A data mining approach to discover and develop new organic nonlinear optical crystals that produce intense pulses of terahertz radiation is demonstrated. The Cambridge Structural Database is mined for non-centrosymmetric materials and these structural data are used in tandem with density functional theory calculations to predict new materials that efficiently generate terahertz radiation. This enables us to (in a relatively short time) discover, synthesize, and grow large, high-quality crystals of four promising materials and characterize them for intense terahertz generation. In a direct comparison to the current state-of-the-art organic terahertz generation crystals, these new materials excel. The discovery and characterization of these novel terahertz generators validate the approach of combining data mining with density functional theory calculations to predict properties of high-performance organic materials, potentially for a host of exciting applications.

Boundary domain genes were recruited to suppress bract growth and promote branching in maize

Abstract: Grass inflorescence development is diverse and complex and involves sophisticated but poorly understood interactions of genes regulating branch determinacy and leaf growth. Here, we use a combination of transcript profiling and genetic and phylogenetic analyses to investigate tasselsheath1 (tsh1) and tsh4, two maize genes that simultaneously suppress inflorescence leaf growth and promote branching. We identify a regulatory network of inflorescence leaf suppression that involves the phase change gene tsh4 upstream of tsh1 and the ligule identity gene liguleless2 (lg2). We also find that a series of duplications in the tsh1 gene lineage facilitated its shift from boundary domain in nongrasses to suppressed inflorescence leaves of grasses. Collectively, these results suggest that the boundary domain genes tsh1 and lg2 were recruited to inflorescence leaves where they suppress growth and regulate a nonautonomous signaling center that promotes inflorescence branching, an important component of yield in cereal grasses.

Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis

Abstract: Rate of change, second derivative and volatility of gamma-ray (GR) well-log curves provide useful indicators with which to characterize lithofacies in clastic sedimentary sequences. Rolling averages of these variables, as they change with depth, are also able to distinguish certain lithofacies features. These attributes make it possible to accurately distinguish key facies by using only gamma-ray data, both with formulaic calculations and employing machine-learning (ML) algorithms. This is useful in the many wellbores for which only basic logging suites are available. As well as enhancing lithofacies classification more generally using well-log variables, these GR attributes can be used to forecast facies in real time based on logging-while-drilling data. The application is demonstrated with simple formula using synthetic GR logs featuring common clastic lithofacies and their transitions. Seven widely used ML methods are each trained and validated with a synthetic GR curve (1450 data points) displaying six distinct facies. The ability of the ML model to distinguish those facies using seven GR attributes is compared and further tested with an independent GR data set (800 data points). The random forest algorithm outperforms the other ML models in this facies prediction task, achieving a mean absolute error of 0.25 (on a facies class range of 1 to 6) for the independent testing dataset. The results highlight the benefit of this technique in providing reliable facies analysis based only on GR data. Random forest, support vector classification and eXtreme Gradient Boost are the ML models that provide the most reliable facies classification from the GR attributes defined. Annotated confusion matrices assist in revealing the details of facies class prediction accuracy and precision achieved by the ML and models and classification formulas. Cited as: Wood, D.A. Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Advances in Geo-Energy Research, 2022, 6(1): 69-85. https://doi.org/10.46690/ager.2022.01.06

Laser machined micropatterns as corrosion protection of both hydrophobic and hydrophilic magnesium.

Abstract: Magnesium and its alloys are promising candidate materials for medical implants because they possess excellent biocompatibility and mechanical properties comparable to bone. Furthermore, secondary surgical operations for removal could be eliminated due to magnesium's biodegradability. However, magnesium's degradation rate in aqueous environments is too high for most applications. It has been reported that hydrophobic textured surfaces can trap a surface gas layer which acts as a protective barrier against corrosion. However, prior studies have not investigated separately the role of the texture and hydrophobic treatments on magnesium corrosion rates. In this study, pillar-shaped microstructure patterns were fabricated on polished high purity magnesium surfaces by ablation with a picosecond laser. Some micropatterned samples were further processed by stearic acid modification (SAM). Micropatterned surfaces with SAM had hydrophobic properties with water droplet contact angles greater than 130 ° , while the micropatterned surfaces without SAM remained hydrophilic. The corrosion properties of textured and smooth magnesium surfaces in saline solution were investigated using electrochemical impedance spectroscopy (EIS) and optical microscopy. Corrosion rates on both hydrophobic and hydrophilic laser machined surfaces were reduced ∼90% relative to polished surfaces. Surprisingly, corrosion rates were similar for both hydrophobic and hydrophilic surfaces. Indirect evidence of local alkalization near microstructures was found and was hypothesized to stabilize the Mg(OH)2 layer, thereby inhibiting corrosion on hydrophilic surfaces. This is different than the corrosion resistance mechanism for superhydrophobic surfaces which makes use of gas adhesion at the liquid solid interface. These results suggest additional processing to render the magnesium hydrophobic is not necessary since it does not significantly enhance the corrosion resistance beyond what is conferred by micropatterned textures.

Presence and stability of SARS-CoV-2 on environmental currency and money cards in Utah reveals a lack of live virus

Abstract: The highly contagious nature of SARS-CoV-2 has led to several studies on the transmission of the virus. A little studied potential fomite of great concern in the community is currency, which has been shown to harbor microbial pathogens in several studies. Since the onset of the COVID-19 pandemic, many businesses in the United States have limited the use of banknotes in favor of credit cards. However, SARS-CoV-2 has shown greater stability on plastic in several studies. Herein, the stability of SARS-CoV-2 at room temperature on banknotes, money cards and coins was investigated. In vitro studies with live virus suggested SARS-CoV-2 was highly unstable on banknotes, showing an initial rapid reduction in viable virus and no viral detection by 24 hours. In contrast, SARS-CoV-2 displayed increased stability on money cards with live virus detected after 48 hours. Environmental swabbing of currency and money cards on and near the campus of Brigham Young University supported these results, with no detection of SARS-CoV-2 RNA on banknotes, and a low level on money cards. However, no viable virus was detected on either. These preliminary results suggest that the use of money cards over banknotes in order to slow the spread of this virus may be ill-advised. These findings should be investigated further through larger environmental studies involving more locations.

Spatial variability of soil quality within management zones: Homogeneity and purity of delineated zones

Abstract: Fields are the original management zones used in agricultural ecosystems. Uniformity of soil within management zones (MZ) is crucial for sustainable soil management, long-term productivity, and avoiding environmental problems. When considering a new area for agricultural expansion or for improving the efficiency of existing agricultural practices, it is useful to identify homogeneous areas or MZs so that the land can be more sustainably used in the future. One way to identify MZs could be through soil quality assessment. Management zones were determined for an agroecosystem region in southern Iran with an area of 452 km2, and the homogeneity and purity of delineated zones were examined by soil quality assessment. Soil quality grades were calculated using 421 top-soil samples and two methods: i) the total data set (TDS) and ii) the minimum data set (MDS). The spatial distribution of soil quality grades was mapped using a random forest model. MZs were delineated using a fuzzy k-means classification algorithm based on the MDS. The random forest model mapped the spatial distribution of the soil quality well (R2 > 0.871). Among five soil quality grades, three soil quality grades, high (II), moderate (III), and low (IV), were found to cover 90.74 and 93.11% of the total studied area as predicted by the TDS and MDS, respectively. The subsequent classification of the soil quality data into MZs using fuzzy k-means identified two different MZs (p

Uncovering Hidden Members and Functions of the Soil Microbiome Using <i>De Novo</i> Metaproteomics

Abstract: Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Sensitivity of the shear wave speed-stress relationship to soft tissue material properties and fiber alignment.

Abstract: The use of shear wave propagation to noninvasively measure material properties and loading in tendons and ligaments is a growing area of interest in biomechanics. Prior models and experiments suggest that shear wave speed primarily depends on the apparent shear modulus (i.e., shear modulus accounting for contributions from all constituents) at low loads, and then increases with axial stress when axially loaded. However, differences in the magnitudes of shear wave speeds between ligaments and tendons, which have different substructures, suggest that the tissue's composition and fiber alignment may also affect shear wave propagation. Accordingly, the objectives of this study were to (1) characterize changes in the apparent shear modulus induced by variations in constitutive properties and fiber alignment, and (2) determine the sensitivity of the shear wave speed-stress relationship to variations in constitutive properties and fiber alignment. To enable systematic variations of both constitutive properties and fiber alignment, we developed a finite element model that represented an isotropic ground matrix with an embedded fiber distribution. Using this model, we performed dynamic simulations of shear wave propagation at axial strains from 0% to 10%. We characterized the shear wave speed-stress relationship using a simple linear regression between shear wave speed squared and axial stress, which is based on an analytical relationship derived from a tensioned beam model. We found that predicted shear wave speeds were both in-range with shear wave speeds in previous in vivo and ex vivo studies, and strongly correlated with the axial stress (R2 = 0.99). The slope of the squared shear wave speed-axial stress relationship was highly sensitive to changes in tissue density. Both the intercept of this relationship and the apparent shear modulus were sensitive to both the shear modulus of the ground matrix and the stiffness of the fibers' toe-region when the fibers were less well-aligned to the loading direction. We also determined that the tensioned beam model overpredicted the axial tissue stress with increasing load when the model had less well-aligned fibers. This indicates that the shear wave speed increases likely in response to a load-dependent increase in the apparent shear modulus. Our findings suggest that researchers may need to consider both the material and structural properties (i.e., fiber alignment) of tendon and ligament when measuring shear wave speeds in pathological tissues or tissues with less well-aligned fibers.

Compensating for Soft-Tissue Artifact Using the Orientation of Distal Limb Segments During Electromagnetic Motion Capture of the Upper Limb

Abstract: Abstract Most motion capture measurements suffer from soft-tissue artifacts (STA). Especially affected are rotations about the long axis of a limb segment, such as humeral internal-external rotation (HIER) and forearm pronation-supination (FPS). Unfortunately, most existing methods to compensate for STA were designed for optoelectronic motion capture systems. We present and evaluate an STA compensation method that (1) compensates for STA in HIER and/or FPS, (2) is developed specifically for electromagnetic motion capture systems, and (3) does not require additional calibration or data. To compensate for STA, calculation of HIER angles relies on forearm orientation, and calculation of FPS angles rely on hand orientation. To test this approach, we recorded whole-arm movement data from eight subjects and compared their joint angle trajectories calculated according to progressive levels of STA compensation. Compensated HIER and FPS angles were significantly larger than uncompensated angles. Although the effect of STA compensation on other joint angles (besides HIER and FPS) was usually modest, significant effects were seen in certain degrees-of-freedom under some conditions. Overall, the method functioned as intended during most of the range of motion of the upper limb, but it becomes unstable in extreme elbow extension and extreme wrist flexion–extension. Specifically, this method is not recommended for movements within 20 deg of full elbow extension, full wrist flexion, or full wrist extension. Since this method does not require additional calibration of data, it can be applied retroactively to data collected without the intent to compensate for STA.

Pitch and Thrust Allocation for Full-Flight-Regime Control of Winged eVTOL UAVs

Abstract: Trajectory tracking control for winged eVTOL aircraft is complicated by the high-angle-of-attack aerodynamics experienced during navigational flight occurring immediately after takeoff and immediately before landing. The total energy use of the vehicle can be reduced and the control performance can be improved by appropriately considering the pitch angle of the vehicle in varying flight conditions. We present a review of high-angle-of-attack aerodynamic models as well as an algorithm for finding the optimal pitch and thrust of a winged eVTOL throughout its flight regime. We show simulation results demonstrating a 75% reduction in tracking error over our previous work while maintaining a similar average thrust and an 85% reduction in tracking error over using a multirotor-like controller. The results show that the method is robust to modeling error in the stall characteristics.

Adrenergic Receptor Regulation of Mitochondrial Function in Cardiomyocytes

Abstract: Adrenergic receptors (ARs) are G protein–coupled receptors that are stimulated by catecholamines to induce a wide array of physiological effects across tissue types. Both α1- and β-ARs are found on cardiomyocytes and regulate cardiac contractility and hypertrophy through diverse molecular pathways. Acute activation of cardiomyocyte β-ARs increases heart rate and contractility as an adaptive stress response. However, chronic β-AR stimulation contributes to the pathobiology of heart failure. By contrast, mounting evidence suggests that α1-ARs serve protective functions that may mitigate the deleterious effects of chronic β-AR activation. Here, we will review recent studies demonstrating that α1- and β-ARs differentially regulate mitochondrial biogenesis and dynamics, mitochondrial calcium handling, and oxidative phosphorylation in cardiomyocytes. We will identify potential mechanisms of these actions and focus on the implications of these findings for the modulation of contractile function in the uninjured and failing heart. Collectively, we hope to elucidate important physiological processes through which these well-studied and clinically relevant receptors stimulate and fuel cardiac contraction to contribute to myocardial health and disease.

Single-Cell RNA Sequencing Characterizes the Molecular Heterogeneity of the Larval Zebrafish Optic Tectum

Abstract: The optic tectum (OT) is a multilaminated midbrain structure that acts as the primary retinorecipient in the zebrafish brain. Homologous to the mammalian superior colliculus, the OT is responsible for the reception and integration of stimuli, followed by elicitation of salient behavioral responses. While the OT has been the focus of functional experiments for decades, less is known concerning specific cell types, microcircuitry, and their individual functions within the OT. Recent efforts have contributed substantially to the knowledge of tectal cell types; however, a comprehensive cell catalog is incomplete. Here we contribute to this growing effort by applying single-cell RNA Sequencing (scRNA-seq) to characterize the transcriptomic profiles of tectal cells labeled by the transgenic enhancer trap line y304Et(cfos:Gal4;UAS:Kaede). We sequenced 13,320 cells, a 4X cellular coverage, and identified 25 putative OT cell populations. Within those cells, we identified several mature and developing neuronal populations, as well as non-neuronal cell types including oligodendrocytes and microglia. Although most mature neurons demonstrate GABAergic activity, several glutamatergic populations are present, as well as one glycinergic population. We also conducted Gene Ontology analysis to identify enriched biological processes, and computed RNA velocity to infer current and future transcriptional cell states. Finally, we conducted in situ hybridization to validate our bioinformatic analyses and spatially map select clusters. In conclusion, the larval zebrafish OT is a complex structure containing at least 25 transcriptionally distinct cell populations. To our knowledge, this is the first time scRNA-seq has been applied to explore the OT alone and in depth.

What We Know and Need to Know about Literacy Interventions for Elementary Students with Reading Difficulties and Disabilities, including Dyslexia

Abstract: The purpose of this paper is to describe what we know and what we still need to learn about literacy intervention for children who experience significant difficulties learning to read. We reviewed 14 meta-analyses and systematic reviews of experimental and quasi-experimental studies published in the last decade that examined the effects of reading and writing interventions in the elementary grades, including research focused on students with reading difficulties and disabilities, including dyslexia. We attended to moderator analyses, when available, to further refine what we know and need to learn about interventions. Findings from these reviews indicate that explicit and systematic intervention focusing on the code and meaning dimensions of reading and writing, and delivered one-to-one or in small groups, are likely to improve foundational code-based reading skills, and to a lesser extent, meaning-based skills, across elementary grade levels. Findings, at least in the upper elementary grades, indicate that some intervention features including standardized protocols, multiple components, and longer duration can yield stronger effects. And, integrating reading and writing interventions shows promise. We still need to learn more about specific instructional routines and components that provide more robust effects on students' ability to comprehend and individual differences in response to interventions. We discuss limitations of this review of reviews and suggest directions for future research to optimize implementation, particularly to understand for whom and under what conditions literacy interventions work best.