American University for Humanities

The study looks at African food insecurity, with a focus on Nigeria, and evaluates digital technology techniques that utilise big data analytics, AI, and cloud services. The research demonstrates how climate change and population growth and infrastructure gaps and poverty create complex food insecurity problems through a detailed flowchart. Digital tools demonstrate their ability to transform agricultural production while making farming operations more resilient. Big data analytics allows farmers to make immediate decisions through its real-time delivery of weather pattern data and soil health information and market trend analysis which Kenya’s Farmer Platform demonstrates. AI technology enables farmers to monitor crops precisely while detecting diseases and optimizing resources and automating processes through Indian and Kenyan projects. Cloud computing enables Ghana’s GAINS system to create collaborative platforms which enhance resource sharing and market access and knowledge dissemination. Advanced technology integration provides substantial opportunities to combat food insecurity across Africa while supporting Sustainable Development Goals (SDGs) achievement. African nations can achieve modern agricultural practices and enhanced food security for their expanding populations through digital innovation implementation.

Helicoverpa zea (Lepidoptera: Noctuidae) injury to soybean ( Glycine max L.) may result in yield loss by feeding on reproductive tissue. Soybean varieties are classified based on their morphological growth habit, which can be determinate or indeterminate. In varieties with a determinate growth habit, the onset of reproductive growth results in the termination of vegetative growth, while soybean with indeterminate growth habits continue vegetative growth even after entering their reproductive stages. Management recommendations for H. zea in the southeastern US were developed from soybean varieties with determinate growth habits. The objective of this research was to understand the compensatory ability of these growth habits to H. zea feeding. We hypothesized that soybean with an indeterminate growth habit might have a greater ability to compensate yield components following H. zea feeding compared to determinate growth habits, as indeterminates typically flower for a longer time. We designed a field experiment comparing a single determinate and indeterminate soybean variety within the same relative maturity. We removed 0%, 50%, and 100% of reproductive tissue between pre-bloom and beginning pod stages (R1 to R3) to simulate H. zea feeding over two years. Yield components were collected to detect differences between growth habits. We found that indeterminate varieties showed greater compensation by adjusting pod type and seed weight, particularly under partial tissue removal. Indeterminate plants produced more one- and four-seeded pods, while determinate plants increased three-seeded pods. Total tissue removal reduced yield components, but moderate damage allowed for compensation. Differences may be driven by environment and should be tested across more varieties.

Breast cancer continues to be a significant cause of mortality among women worldwide, underscoring the importance of accurate and early detection to enhance survival rates. This research explores the effectiveness of several supervised machine learning models in predicting breast cancer using a specialized dataset. Five algorithms were evaluated: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The dataset includes 569 samples with 30 continuous features derived from digitized images of fine needle aspiration (FNA) of breast tumors. The models were trained and assessed based on key metrics such as accuracy, precision, recall, and F1-score. Among the models, Random Forest and SVM delivered the best results, both achieving a 97.20% accuracy, which highlights the strength of ensemble and kernel-based methods in this application. Logistic Regression also performed commendably, reaching an accuracy of 95.10%, indicating that the feature space exhibits a high degree of linear separability. While Decision Tree and Gradient Boosting models demonstrated good performance, their results were slightly lower in comparison. These findings emphasize the capability of machine learning, particularly Random Forest and SVM, in supporting breast cancer diagnosis. Furthermore, the consistently high accuracy across models reflects the robustness of the features derived from FNA images for breast cancer classification.

In the rapidly evolving multi-cloud environment, organizations are finding it more difficult to safeguard their infrastructures against increasingly sophisticated cyber threats. Traditional, human-driven threat intelligence and incident response processes are often inadequate for the scale and complexity of multi-cloud environments. This research demonstrates how Artificial Intelligence (AI) can aggregate security data from multiple sources—ranging from cloud-native services to third-party threat feeds—and quickly detect anomalies, including zero-day vulnerabilities. Particularly, machine learning approaches of supervised and unsupervised learning offer flexibility compared to fixed signatures, while Convolutional Neural Networks (CNNs) show improved performance, with accuracy, precision, recall, and F1 measures of more than 95%-outperforming Random Forest, Decision Tree, Isolation Forest, and One-Class SVM. Interoperability, standardization, data availability, as well as ethical concerns, however, have to be addressed to realize complete capability of AI in this field. To that end, this paper recommends a comprehensive framework encompassing continuous model training, federated learning for privacy-protective data sharing, and a hybrid combination of automated defense and human monitoring.

In today’s rapidly evolving digital landscape, the explosion of data volume and complexity has necessitated the development of advanced processing frameworks that can scale effectively. Apache Spark emerges as a leading technology in big data processing, offering powerful in-memory computing capabilities and streamlined analytical operations. This paper explores the utilization of Spark for scaling data pipelines and performing comprehensive analytics across diverse datasets. By distributing computational tasks across clusters, Spark significantly reduces processing time and enhances real-time data analysis, making it indispensable for modern enterprises. The study details how Spark integrates with existing data architectures, supporting a broad spectrum of applications ranging from batch processing to interactive analytics. In addition, Spark’s robust ecosystem—featuring libraries for machine learning, graph processing, and stream analysis—enables organizations to address complex data challenges efficiently. Key case studies illustrate successful deployments, emphasizing improvements in performance, fault tolerance, and cost-effectiveness. The research further examines techniques for optimizing Spark configurations to achieve maximum throughput and minimal latency, highlighting best practices for resource management in both on-premises and cloud environments. Ultimately, this paper demonstrates that by leveraging Apache Spark, organizations can transform raw data into actionable insights, drive innovation, and maintain a competitive edge in a data-centric world. Furthermore, the framework’s scalability allows for seamless integration of emerging technologies, ensuring adaptability to evolving business requirements. Future research directions include enhancing security measures, reducing energy consumption, and further refining analytical accuracy. These advancements will continue to empower data scientists and engineers in solving intricate data challenges while setting industry benchmarks.

Obesity is a prevalent metabolic condition and major contributor to the development of cardiovascular disease. Arteries of visceral adipose present with significant endothelial dysfunction whereas subcutaneous adipose arteries remain functional in obese humans and diet-induced obese mice. Obesity is also associated with elevated plasma long chain fatty acids (LCFAs), well-established contributors to endothelial dysfunction, which require a receptor to facilitate their uptake. CD36, a scavenger receptor, facilitates LCFA uptake, which when in excess would contribute to endothelial dysfunction. Therefore, we aimed to determine the role of CD36 in arteries ex vivo in both mouse and human subcutaneous and visceral adipose arteries as well as examine the difference in uptake of LCFAs in arteries from both depots. First, to determine if CD36 contributes to obesity-induced endothelial dysfunction in visceral adipose arteries, mesenteric (visceral) and subcutaneous adipose arteries isolated from lean and diet-induced obese WT and CD36 knockout were mounted for pressure myography. As expected, visceral adipose arteries of obese mice exhibited a blunted response to endothelium-dependent stimuli, whereas subcutaneous adipose arteries remained functional. CD36 ablation restored endothelium-dependent vasodilation in visceral arteries of obese mice, suggesting a critical role for CD36 in obesity-induced endothelial dysfunction. We next aimed to determine if obesity alters endothelial CD36 expression using two distinct approaches: flow cytometry and immunofluorescence staining of en face preparations in isolated mouse and/or human arteries. For flow cytometry, arteries were digested and sorted to identify the endothelial cell population (i.e., CD45-CD31+ cells). En face preparations were stained for VE-cadherin to locate the endothelial cell layer via confocal microscopy. In both experiments, an antibody that detects an extracellular epitope to CD36 was used to detect membrane expression. Obesity did not alter the expression of CD36 expression in visceral adipose arteries relative to lean counterpart controls or subcutaneous arteries from obese mice or humans. We next aimed to determine if LCFA uptake was altered by obesity. Arteries were exposed to a fluorescent fatty acid, and internalized endothelial LCFA uptake was assessed at different time points via confocal microscopy. Visceral arteries from obese mice and humans had elevated LCFA uptake relative to respective controls which may be an underlying mediator for the dichotomy in endothelial dysfunction observed in obesity. Ongoing work in our lab is focused on determining if this is indeed the case and, if so, if this process is mediated by endothelial CD36. National Institutes of Health (NIH) Grant P20GM113125-6564 (IF). 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.

Endoscopic Endonasal Resection Of Infratemporal Giant Cell Tumor. Giant cell tumor of bone (GCTB) or osteoclastoma is a rare, benign, osteolytic neoplasm that most Commonly occurs in early adulthood and often involves the epiphyses of long bones of the body with Peak incidence ages (20–40 years) with Slight female predominance (3:2). Approximately 2% of all giant cell tumors occur in the head and the most common sites are: Sphenoid, ethmoid, Petrous & temporal bones. It Originates from neoplastic nonosteogenic stromal cells of the bone marrow. And its Histology is characterized by the presence of multinucleated osteoclastic giant cells diffusely distributed among a background of mononuclear stromal and macrophage lineage cells. It is locally aggressive disease and surgery is the treatment of choice ± radiotherapy.

The proliferation of distributed data sources, edge computing, and privacy-preserving requirements has necessitated a paradigm shift in data science and machine learning operations (MLOps). This paper presents a comprehensive architectural framework for adaptive federated data science that integrates data mesh principles, automated feature engineering, causal discovery, and end-to-end MLOps capabilities. The proposed architecture addresses critical challenges in modern machine learning systems including data governance, privacy preservation, model drift detection, and continuous deployment. Through a layered approach encompassing data ingestion, storage, processing, modeling, serving, and observability, this framework enables organizations to build scalable, secure, and maintainable machine learning systems while maintaining federated control over distributed data assets.

INTRODUCTION: Lysosomal acid lipase deficiency (LAL-D) is a rare autosomal recessive disease caused by mutations in the lysosomal acid lipase (LIPA) gene and characterized by cholesteryl ester accumulation in the liver and other body parts. Depending on the lipase enzyme residual activity level, severity of LAL-D can be low enough and patients may present with nonspecific symptoms; LAL-D can then be easily confused with more common causes of hepatic steatosis such as nonalcoholic fatty liver disease leading to underdiagnosis or even misdiagnosis well into adulthood. Here we present two cases of patients whose diagnoses of LAL-D were delayed for several years due to initial misdiagnosis. CASE DESCRIPTION/METHODS: A 59-year-old male, initially presented in childhood with hepatomegaly and elevated liver enzymes and was diagnosed with type 3 glycogen storage disease due to findings of liver biopsy done in 1975. He presented to our practice with worsening fatigue, muscle wasting and abdominal distension despite being on cornstarch therapy. His labs showed ALT of 75 U/L (0-44), AST of 73 U/L (0-40), and LDL cholesterol of 302 mg/dL (<110). LAL activity was low at 4 pmol/h/punch and liver biopsy showed diffuse microvesicular steatosis and foamy macrophages with stage 2 fibrosis confirming the diagnosis of LAL-D. His diagnosis was missed for at least 44 years from his initial liver biopsy. The second patient, a 14-year-old male, was not diagnosed for 8 years after repeatedly presenting to the emergency room with abdominal pain, diarrhea, and mild elevation in ALT for years. He required a cholecystectomy at the age of 13 years and the surgeon noted an “orange” liver. A biopsy showed diffuse microvesicular steatosis with stage 3 fibrosis suggestive of lipid storage disorder. His labs at presentation to our clinic were: ALT of 225, AST of 111, LDL cholesterol at 192. Genetic testing confirmed the diagnosis of LAL-D (E8SJM homozygous) and he was started on sebelipase alfa enzyme replacement with normalization of ALT and LDL cholesterol. DISCUSSION: Gastroenterologists should suspect the diagnosis of LAL-D in any patient presenting with hepatomegaly, fatty infiltration of the liver and elevated LDL. Prompt diagnosis of LAL-D could have helped decrease morbidity and improve these patients' quality of life by initiating sebelipase alfa enzyme replacement therapy to reduce the known long-term complications of advanced liver fibrosis such as cirrhosis and liver failure, and the many effects of progressive atherosclerosis.