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Drought-induced shifts in cowpea rhizoplane bacterial communities across different vegetative and reproductive stages
No full textThe increasing prevalence of drought poses significant challenges to global food security, necessitating a deeper understanding of plant-microbiome interactions which help crop production. This study investigated the dynamics of drought stress-induced changes in rhizosphere-associated bacterial communities of two cowpea (Vigna unguiculata L.) genotypes (EpicSelect4 and UCR369) across four growth stages. Community-level physiological profiling using Biolog EcoPlate analysis revealed that drought reduced rhizosphere microbial metabolic activity (carbon substrate utilization) in both genotypes, but UCR369 maintained higher metabolic capability than EpicSelect4 across growth stages. Further, integration of amplicon metagenomics and physiological data showed that drought significantly altered rhizoplane bacterial communities in cowpea, with distinct genotype-specific responses. There was a decline in Alpha diversity under drought, while community composition shifted based on genotype. Beta diversity results revealed that genotype and drought significantly influenced microbial community structure across growth stages. Proteobacteria dominated the root zone of the EpicSelect4 genotype, while UCR369 showed an increase in Actinobacteria under drought conditions. Redundancy analysis revealed that soil enzyme activities (β-glucosidase and N-acetyl-glucosaminidase) and physiological traits werecorrelated significantly with microbial community shifts. Interpretable machine learning approach identified Actinobacteriota and Cyanobacteria as the key biomarkers enriched under drought, with genera such as Streptomyces and Ensifer potentially contributing to drought tolerance. The Random Forest model coupled with SHapley Additive exPlanations (SHAP) values demonstrated high predictive accuracy for identifying drought-related biomarkers, aligning with DeSeq2 analysis results. These models provided insights into the potential contributions of specific microbial taxa to cowpea drought tolerance, offering a promising avenue for developing microbiome-based strategies to improve crop resilience and sustainability under drought conditions
The Art of Making a Woman in Shakespeare and Shaw
No full textFor centuries, Ovid’s Pygmalion myth has been repeatedly restaged as a narrative of artistic creation and miraculous transformation. Yet beneath its aesthetic surface lies a persistent ethical conflict: the transformation of a woman into a male’s ideal form requires the erasure of her voice, agency, and identity. This thesis examines how the Pygmalion myth structures the coercive dynamics of transformation in William Shakespeare’s The Taming of the Shrew and George Bernard Shaw’s Pygmalion, tracing how each retelling adapts the myth to preserve patriarchal authority under new pretenses of improvement and education. Through close analysis of environmental, psychological, and linguistic manipulation, I argue that Petruchio and Higgins inherit Pygmalion’s role as creators who reshape women according to their own designs. Whereas Petruchio enacts Katherine’s transformation through deprivation, humiliation, and control of perception, Higgins orchestrates Eliza’s transformation through refinement, indulgence, and linguistic discipline. Across both plays, transformation becomes a mechanism of coercive control disguised as benevolence, revealing that the cost of artistic “perfection” is the woman’s selfhood. In exposing how critics and adaptations frequently sentimentalize these endings to reassert male authority, this thesis explores a continuing cultural impulse to romanticize female submission. Ultimately, the Pygmalion story remains compelling not because it celebrates creation, but because it idealizes domination, positioning the erasure of a woman’s identity and voice as a spectacle worthy of praise
Advancing Telemedicine Adoption: Insights From Health Behavior Models With a Focus on the Multi-Theory Model
Get PDFTelemedicine, the use of digital communication technologies to deliver clinical healthcare remotely, has emerged as a pivotal advancement in modern medicine. By providing virtual consultations, asynchronous data transmission, and remote patient monitoring (RPM), telemedicine enhances accessibility, particularly for underserved populations, while promoting efficiency and continuity of care. As telemedicine transforms healthcare delivery, its adoption hinges on understanding health behaviors—actions influenced by psychological, sociocultural, and environmental factors. These behaviors can be categorized into preventative, illness, and risk-related actions, with established models like the Health Belief Model (HBM), Social Cognitive Theory (SCT), and Transtheoretical Model (TTM) offering insights into behavior change mechanisms. The Multi-Theory Model (MTM), integrating constructs from various frameworks, is particularly well-suited to guide telemedicine adoption. MTM addresses both initiation and maintenance of behaviors through components such as participatory dialogue, behavioral confidence, and environmental modifications. Emotional transformation and social support further sustain long-term engagement with telemedicine. This comprehensive approach positions MTM as a valuable tool for overcoming barriers like privacy concerns, technological literacy, and infrastructure gaps. This review explores the application of health behavior models to telemedicine adoption, emphasizing the strengths of MTM in addressing the complexities of behavior change. By leveraging MTM, healthcare systems can enhance telemedicine utilization, ultimately improving health outcomes and equity in care delivery
The Rural Affinity Advantage: Reimagining Schools as Entrepreneurship Incubators
Get PDFThis article, which combines research with lived experience, discusses how youth entrepreneurship education stands as a promising pedagogy in rural contexts. The author situates the research by sharing experiences teaching in rural schools at a time of declining enrollment and rampant outmigration. The article then explores educational pathways, through entrepreneurship education, that rural educators may follow to contribute to the cause of rural sustainability. Entrepreneurialism is positioned as a useful set of knowledge, skills, and attitudes that rural youth can use to better dictate the terms of their future, and schools are envisioned as entrepreneurship incubators
Alamo Plaza Hotel Courts, Jackson, Mississippi
No full textA color image of the Alamo Plaza Hotel Courts is freatured on this postcard. The Hotel is depicted as a white, Spanish style building with green and white awnings over the windows and green spaces between the driveways. The yellow section of the card beneath the image advertises the hotels amenities and various locations, including one in Jackson, Mississippi.https://scholarsjunction.msstate.edu/mss-lampton-images-ms-capitol/1539/thumbnail.jp
Evaluating the impact of biostimulants at variable nitrogen rates in corn production
No full textBiostimulants have garnered significant interest due to their potential to enhance crop productivity while optimizing nitrogen (N) uptake and nitrogen use efficiency (NUE). However, field research testing their efficacy in corn (Zea mays. L) production remains largely unexplored. Therefore, a field study was conducted in 2022 and 2023 in Mississippi (MS). A split plot design was implemented, with N rates as the main plot including 0 (control), 90, 180, 269 kg N ha−1 at Starkville, while Stoneville included an additional rate of 224 kg N ha−1. The subplot consisted of seven treatments, including a no biostimulant (check) and six microbial biostimulants (Source Corn®, Envita®, iNvigorate®, Blue N®, Micro AZ™, and Bio level phosN®) applied either as foliar at V4-V5 growth stages or in-furrow at planting. Nitrogen rates positively affected grain yield at all three site-years, whereas biostimulants effects on grain yield were only observed at one site (Stoneville 2022). Moreover, these differences only existed between six biostimulants and they were not significantly different from check plot with no biostimulant. Higher N rates reduced the efficiency of grain production in terms of NUE parameters and N uptake, showing a consistent inverse trend across all site years. This study observed minimal synergistic benefits of microbial biostimulants, despite evaluating their effectiveness alongside varied N rates. Further research testing diverse biostimulant categories with varied dosages and application timings is warranted to confirm their potential benefits for higher productivity and agricultural sustainability
Influence of seed-applied biostimulants on soybean germination and early seedling growth under low and high temperature stress
No full textBiostimulants are environment-friendly agricultural inputs that can improve plant health and yield potential under environmental stressors. Soybeans subjected to extreme temperatures during the growing seasons impacts plant health and performance. Uniform emergence and vigorous seedling establishment are the two traits during the early season that directly correlate with the final yield and are sensitive to abiotic stress. This study tested the effectiveness of seed-applied biostimulants in improving seed germination and emergence traits under different temperatures, low (15 °C, LT), optimum (25 °C, OT), and high (35 °C, HT), using three phenotyping methods such as the paper roll, growth pouch, and soil-based pot culture. Germination, emergence, and seedling growth were significantly accelerated under OT and HT compared to LT in both biostimulant-treated and untreated seeds. While seeds treated with biostimulants exhibited minor differences in germination, emergence, and growth traits under LT and HT compared to the OT. In the soil-based pot culture experiment, humic and fulvic acid-containing treatments extended the time to 50% emergence under LT. This delay was associated with a 13% increase in seedling biomass. A bacillus containing biostimulant improved seedling vigor by 7% under LT compared to untreated check. Notably, biostimulants containing bacterial strains, fulvic acid, and humic acid were found to have a role in reducing time to germination or emergence and enhancing seedling growth. However, the results obtained from different phenotyping methods were inconsistent, suggesting that the effects of biostimulants on germination and growth parameters may be more targeted rather than broad-spectrum. Future research is necessary to optimize application rates and fully explore their potential to mitigate the effects of stressors during the growing season
For Neurodivergent Marxism: Between Materialist Analysis and Escape from the Empire of Normality and Capitalism
No full textRobert Chapman’s Empire of Normality: Neurodiversity and Capitalism (2023) possesses extraordinary import for the emancipatory thinking and praxis of neurodivergent folk, but also of those of us who identify as disabled, chronically ill, crip, mad or even neurotypical. This is not only because Chapman’s work offers a fundamental contribution to debates initiated by recent Marxist engagements with the relationship between capitalism and health. It is also thanks to the intense theoretical innovation, historical analysis and inclusive politics that the book radiates. By dissecting the harms wrought by the Empire of Normality, Chapman’s Neurodivergent Marxism is an updated type of Marxism that simultaneously models updated ways of theorising against and beyond the capitalist Empire of Normality, and frames updated modes of class struggle. Despite originating close to neurodivergent power and neurodiversity theory, or because of this, it beckons all of us to follow, whether in workplaces, far from these, or in our own movements
Advanced monitoring of turbidity in the Mississippi Sound: A machine learning-based approach integrating unmanned aircraft systems, satellite observations, and land use analysis
No full textTurbidity is a vital indicator of water quality, influencing light penetration and the ecological function of coastal environments. Traditional monitoring methods often lack the spatial and temporal resolution needed for effective environmental management. This dissertation introduces a multi-scale framework that integrates Uncrewed Aircraft Systems (UAS), Autonomous Surface Vessels (ASV), satellite imagery, and machine learning to produce high-resolution turbidity maps and long-term trend analyses for the Mississippi Sound. Chapter I presents a fine-scale turbidity mapping method using UAS multispectral imagery calibrated with in situ ASV measurements. This hybrid UAS-ASV system enabled the generation of detailed turbidity estimates. Among various machine learning models tested on radiometer-derived sensor-specific remote sensing reflectance, the Support Vector Machine (SVM) performed best (R² = 0.943, RMSE = 0.454 NTU), effectively capturing nonlinear relationships between turbidity and remotely sensed data. Chapter II expands the analysis to broader spatial and temporal scales by developing a deep neural network (DNN) regression model. Trained on datasets from six ASV field campaigns and Landsat 8 and 9 surface reflectance imagery, the DNN outperformed RandomForest, SVM, and XGBoost models, achieving an R² of 0.864 and RMSE of 1.794 NTU. The model was then used to generate a 22-year turbidity time series (2002–2024), revealing seasonal trends and peak turbidity events in 2013, 2016, and 2020—linked to meteorological disturbances and Bonnet Carré Spillway openings. Chapter III investigates the influence of land use and land cover (LULC) changes on turbidity. Using harmonized annual LULC maps and spatial correlation analysis, the study found a significant negative correlation between barren land and turbidity at the HUC-10 watershed scale, while cropland and urban areas had minimal effects. Finer-resolution HUC-10 analysis revealed slightly stronger land–water relationships than coarser HUC-8 scales. As a preliminary study focused on a single LULC factor, this chapter provides a foundation for more comprehensive future research. This research underscores the value of integrating remote sensing with machine learning to monitor complex coastal systems. The proposed framework is adaptable across regions and scales, offering a flexible tool for water quality assessment, habitat restoration, and informed environmental decision-makin