Boise State University

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    Examination of Urinary Pesticide Concentrations, Protective Behaviors, and Risk Perceptions Among Latino and Latina Farmworkers in Southwestern Idaho

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    Introduction: Studies have documented high levels of pesticide exposure among men farmworkers; however, few have examined exposures or the experiences of women farmworkers. Data gaps also exist regarding farmworkers’ perceived risk and control related to pesticides, information that is critical to develop protective interventions. Objective: We aimed to compare urinary pesticide biomarker concentrations between Latino and Latina farmworkers and examine associations with occupational characteristics, risk perceptions, perceived control, and protective behaviors. Methods: We enrolled a convenience sample of 62 farmworkers (30 men and 32 women) during the pesticide spray season from April–July 2022 in southwestern Idaho. Participants were asked to complete two visits within a seven-day period; at each visit, we collected a urine sample and administered a questionnaire assessing demographic and occupational information. Urine samples were composited and analyzed for 17 biomarkers of herbicides and of organophosphate (OP) and pyrethroid insecticides. Results: Ten pesticide biomarkers (TCPy, MDA, PNP, 3-PBA, 4-F-3-PBA, cis- and trans-DCCA, 2,4-D, Glyphosate, AMPA) were detected in \u3e80% of samples. Men and women had similar urinary biomarker concentrations (p = 0.19–0.94); however, women worked significantly fewer hours than men (p = 0.01), wore similar or greater levels of Personal Protective Equipment (PPE), and were slightly more likely to report having experienced an Acute Pesticide Poisoning (26% of women vs. 14% of men; p = 0.25). We observed inconsistencies in risk perceptions, perceived control, and protective behaviors among men. Discussion: Our study is one the first to examine pesticide exposure and risk perceptions among a cohort of farmworkers balanced on gender. Taken with previous findings, our results suggest that factors such as job tasks, biological susceptibility, or access to trainings and protective equipment might uniquely impact women farmworkers’ exposure and/or vulnerability to pesticides. Women represent an increasing proportion of the agricultural workforce, and larger studies are needed to disentangle these findings

    Dataset for Fine Fuels and Vegetation Point Clouds from Close-Range Structure-from-Motion

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    Rangelands and semi-arid ecosystems are subject to increasing changes in ecologic makeup from a collection of factors. In much of the northern Great Basin of the western United States, rangelands invaded by exotic annual grasses such as cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae) are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes, and better harmonize with other remote sensing techniques. Developments and increased adoption of uncrewed aerial vehicles and instrumentation for vegetation monitoring are enabling greater understanding of vegetation in many ecosystems. Research towards understanding the relationship of traditional field measurements with newer aerial platforms in rangeland environments is growing rapidly, and there is increasing interest in exploring the potential use both to quantify AGB and fine fuel load at pasture and landscape scales. Our study here uses relatively inexpensive handheld photography with custom sampling frames to collect and automatically reconstruct 3D-models of the vegetation within 0.2 m2 quadrats (n = 288). Next, we examine the relationship between volumetric estimates of vegetation to compare with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm3) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared to using the true ground surface. Overall, our reconstruction workflow had an R2 of 0.42, further emphasizing the importance of high-resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine fuel litter

    An Early Miocene (Aquitanian) Mangrove Fossil Forest Buried by a Volcanic Lahar at Barro Colorado Island, Panama

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    We describe remains on an Early Miocene mangrove forest, based on a fossil wood assemblage, discovered on Barro Colorado Island, Panama. Sedimentological and stratigraphic analysis suggests that the fossil trees grew in marginal marine to coastal fluvial settings and were buried under a volcanic lahar flow in a single event. Radiometric analyses of a tuff associated with the fossils gives a date of ∼22.79 Ma indicating an assignment to the Aquitanian stage of Early Miocene. At this time, central Panama was part of a long and narrow peninsula with intense volcanic activity that connected it with North America and was separated from South America by the Central American Seaway. A total of 121 fossil wood specimens were located. Wood anatomy indicates that most of the identifiable specimens belonged to the same morphotype, which has anatomical traits similar to Sonneratia (Lythraceae), a mangrove tree that is native to Southeast Asia. We named this morphotype as a new fossil species: Sonneratioxylon barrocoloradoensis Pérez-Lara., sp. nov. Biomechanical estimates indicate that S. barrocoloradoensis had a mean tree height of 25 m with some specimens reaching 40 m, in contrast to modern Sonneratia and other extant mangrove forests, which generally have lower mean heights. The dominance of S. barrocoloradoensis, its similarity to Sonneratia, and the depositional setting suggest that the fossil wood assemblage on Barro Colorado Island comprised a mangrove forest growing along the coast of the volcanic chain of central Panama

    Leveraging High Resolution Classifications and Random Forests for Hindcasting Decades of Mesic Ecosystem Dynamics in the Landsat Time Series

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    Mesic ecosystems are fundamental to conservation efforts in semi-arid systems, but are threatened by climate change and development. Newer earth observation datasets, including Sentinel-1 and −2, provide opportunities to monitor mesic ecosystems at meaningful spatial scales, but are insufficient for measuring decadal-scale changes. Conversely, the Landsat time series has decades of data, but images are spatially coarse relative to many of the mesic ecosystem areas that sustain dryland systems, resulting in classifications with mixed pixels inadequate for effective monitoring. We developed a workflow that uses 10-m classifications produced from fusion of the Sentinel-1 and −2 time series (2017–2020) to estimate sub-pixel proportions of Landsat time series observations (2004–2020). Using random forest regression models, we quantified water resource proportions (WRP) of surface water, mesic vegetation, and upland land covers within each 30-m Landsat pixel. We incorporated ancillary covariates to account for varying topographic conditions, land cover, and climate. Results indicate that our approach consistently estimates sub-pixel proportions of Landsat pixels more accurately compared to spectral mixture analysis (SMA). The WRP product for surface water had up to 8% less error than SMA as measured by Mean Absolute Error (MAE) and up to 17% less error as measured by Root Mean Squared Error (RMSE). For mesic vegetation, the WRP product outperformed SMA by up to 4% (MAE) and 7% (RMSE). Finally, we demonstrated the ability of our time series to characterize historical water resource availability at a case study site with a well documented restoration history by qualitatively examining the mesic vegetation dynamics time series to identify system responses to restoration efforts. Our approach allows us to hindcast observations of Sentinel products and measure water resource dynamics with greater precision over larger temporal scales. We envision these WRP data to be useful for measuring the impacts of conservation interventions, disturbance recovery, or land use changes that pre-date the Sentinel time series

    A Meta-Analysis and Quality Review of Mathematics Interventions Conducted in Informal Learning Environments with Caregivers and Children

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    The purposes of this study included conducting a meta-analysis and reviewing the study reporting quality of math interventions implemented in informal learning environments (e.g., the home) by children’s caregivers. This meta-analysis included 25 preschool to third-grade math interventions with 83 effect sizes that yielded a statistically significant summary effect (g = 0.26, 95% CI [0.07, 0.45) on children’s math achievement. Significant moderators of the treatment effect included the intensity of caregiver training and type of outcome measure. There were larger average effects for interventions with caregiver training that included follow-up support and for outcomes that were comprehensive early numeracy measures. Studies met 58.0% of reporting quality indicators, and analyses revealed that quality of reporting has improved in recent years. The results of this study offer several recommendations for researchers and practitioners, particularly given the growing evidence base of math interventions conducted in informal learning environments

    9S1R Nullomer Peptide Induces Mitochondrial Pathology, Metabolic Suppression, and Enhanced Immune Cell Infiltration, in Triple-Negative Breast Cancer Mouse Model

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    Nullomers are the shortest strings of absent amino acid (aa) sequences in a species or group of species. Primes are those nullomers that have not been detected in the genome of any species. 9S1R is a 5-aa peptide prime sequence attached to 5-arginine aa, used to treat triple negative breast cancer (TNBC) in an in vivo mouse model. This unique peptide, administered with a trehalose carrier (9S1R-NulloPT), offers enhanced solubility and exhibits distinct anti-cancer effects against TNBC. In our study, we investigated the effect of 9S1R-NulloPT on tumor growth, metabolism, metastatic burden, tumor immune-microenvironment (TME), and transcriptome of aggressive mouse TNBC tumors. Notably, treated mice had smaller tumors in the initial phase of the treatment, as compared to untreated control, and diminished in vivo and ex vivo bioluminescence at later-stages - indicative of metabolically quiescent, dying tumors. The treatment also caused changes in TME with increased infiltration of immune cells and altered tumor transcriptome, with 365 upregulated genes and 710 downregulated genes. Consistent with in vitro data, downregulated genes were enriched in cellular metabolic processes (179), specifically mitochondrial TCA cycle/oxidative phosphorylation (44), and translation machinery/ribosome biogenesis (45). The upregulated genes were associated with the developmental (13), ECM organization (12) and focal adhesion pathways (7). In conclusion, our study demonstrates that 9S1R-NulloPT effectively reduced tumor growth during its initial phase, altering the TME and tumor transcriptome. The treatment induced mitochondrial pathology which led to a metabolic deceleration in tumors, aligning with in vitro observations

    Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management

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    Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis. Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition

    Professional Identity in Nursing Scale 2.0: A National Study of Nurses’ Professional Identity and Psychometric Properties

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    Background: Professional identity is a relatively new concept in the nursing and health care literature. Using the definition of Professional Identity in Nursing (PIN) as its main construct, the authors developed and tested the second iteration of the Professional Identity in Nursing Scale (PINS 2.0) used to measure PIN from two perspectives, self and environment. Purpose: The purpose of this study was to evaluate the psychometric properties of the PINS 2.0. Methods: To assess psychometric validity and reliability, a split-sample analysis was conducted. An exploratory factor analysis (EFA) was conducted on one half of the sample (n = 322) and a confirmatory factor analysis (CFA) was conducted on the other half of the sample (n = 312). Descriptive statistics were also performed and analyzed. Results: According to the EFA pattern of parameter coefficients and CFA fit statistics (PINS-self: χ2(399) =1059.495, p \u3c .001, CFI = 0.934, RMSEA = 0.072, SRMR = 0.032; PINS-environment: χ2(399) =929.019, p \u3c .001, CFI = 0.946, RMSEA = 0.065, SRMR = 0.029), the PINS 2.0 shows adequate psychometric properties for measuring the concept of PIN with the following 4 constructs: 1) values and ethics, 2) knowledge, 3) leadership, and 4) professional comportment. Cronbach\u27s alpha coefficients were: PINS 2.0-self = 0.97 and PINS 2.0-environment =0.98. Conclusion: We further advance the assessment of the psychometric properties of the PINS 2.0 to measure PIN from the perspective of self and environment

    Applied Soft Classes and Fuzzy Confusion in a Patchwork Semi-Arid Ecosystem: Stitching Together Classification Techniques to Preserve Ecologically-Meaningful Information

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    Dryland ecosystems have complex vegetation communities, including subtle transitions between communities and heterogeneous coverage of key functional groups. This complexity challenges the capacity of remote sensing to represent land cover in a meaningful way. Many remote sensing methods to map vegetation in drylands simplify fractional cover into a small number of functional groups that may overlook key ecological communities. Here, we investigate a remote sensing process that further advances our understanding of the link between remote sensing and ecologic community types in drylands. We propose a method using k-means clustering to establish soft classes of vegetation cover communities from detailed field observations. A time-series of Sentinel-2 satellite imagery and a random forest classification leverages the mixing of different phenologies over time to impute such soft community classes over the landscape. Next, we discuss the advantages of using a fuzzy confusion approach for soft classes in cases such as understanding subtle transitions in ecotones, identifying areas for targeted remediation or treatment, and in ascertaining the spatial distribution of non-dominant covers such as biological soil crusts and small native bunchgrasses which have typically been difficult to map with traditional remote sensing classifications. Our pixel-level analysis is relevant to the scale of management decisions and represents the complexity of the landscape. The combination of cloud computing with the spatial, temporal, and spectral observations from Sentinel-2 allow us to develop these ecologically-meaningful observations at large spatial extents, including complete coverage at landscape scales. Re-interpretation of large extent maps of soft classes may be helpful to land managers who need community-level information for fuel breaks, restoration, invasive plant suppression, or habitat identification

    Shifting the Practice of Coercive Penal Care Over Time in a Problem-Solving Court

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    While problem-solving courts represent one area in which rehabilitative efforts have expanded within correctional settings as “coercive penal care,” still unexplored is how the blend of rehabilitative and punitive practices might evolve over time. By conducting interviews and observing a new reentry court, we explore how the court\u27s navigation of coercive penal care transforms over time. We argue that initially, the introduction of rehabilitative goals was mostly subverted by the court\u27s existing punitive criminal legal system and organizational structure. This occurred through court actors prioritizing internal over external goals and metrics in the program, and articulating self-responsibilization narratives for success. As the court progressed, court actors shifted toward emphasizing individualism. Increased individualism occurred in recognition of the complex barriers that participants faced, but presented a double-edged sword: actors focused more on the individual needs of participants beyond program requirements, but also increased individual accountability by participants. This greater emphasis on individualization also allowed court actors to resolve sometimes competing rehabilitative and punitive goals through increased discretion

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