Benedict College

Abstract Opportunistic pathogens (OPs) within the Legionella and Mycobacterium can persist and sometimes proliferate in drinking water systems and pose a risk to public health. Most prior research has focused on isolated system components of the drinking water treatment and distribution system and has rarely examined spatiotemporal dynamics across the entire source water, treatment process, and distribution system continuum. This study addresses this critical knowledge gap by quantitative profiling of microbial communities with full length 16S rRNA gene sequencing and flow cytometry, and associated water chemistry parameters, including disinfection byproducts (DBPs), across five full-scale utilities. These utilities reflect varying source water types, geographic locations, treatment regimes, and climate zones. Microbial communities, including Legionella and Mycobacterium populations, in distribution system were shaped by source water type and exhibited significant community divergence across utilities. Within the same genus, strain-level analyses revealed highly distinct Legionella and Mycobacterium sequence variants unique to each utility. Interestingly, a substantial proportion of Legionella and Mycobacterium amplicon sequence variants were both utility specific and often specific to locations within the distribution system, indicating strong geographic structuring both across and within drinking water systems. Understanding the mechanistic underpinnings of this geographic structuring is critical to develop robust strategies for managing and monitoring Legionella and Mycobacterium populations in drinking water systems.

This paper explores the application of deep learning architectures in retinal image analysis, with a particular focus on the early detection of diabetic retinopathy (DR). Leveraging large-scale retinal imaging datasets, the study examines the diagnostic capacity of advanced neural models in identifying subtle pathological markers such as microaneurysms, hemorrhages, and exudates at the earliest clinical stages. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid deep learning frameworks are evaluated for their performance in image classification and lesion localization. Results demonstrate that CNN-based architectures achieve an accuracy of 94.2%, with sensitivity and specificity values exceeding 93%, underscoring their robustness in medical image interpretation. Hybrid models integrating CNN and attention mechanisms further enhance feature discrimination, achieving 95.1% accuracy and improved area under the curve (AUC) metrics. RNN-driven architectures exhibit competitive performance, particularly in sequential image feature learning, with overall accuracy surpassing 92%. The findings highlight the transformative role of deep learning in automating ophthalmic diagnostics, enabling scalable, precise, and timely detection of diabetic retinopathy to mitigate preventable vision loss.

Historically Black Colleges and Universities (HBCUs) serve as vital institutions for the empowerment of Black students. They also allow exceptional yet under-the-radar student-athletes to compete nationally in the classroom and on the field. With the evolution of issues like revenue sharing, HBCU leadership can rely on the topics presented to understand how to remain competitive within the landscape of global higher education. This chapter delves into the complex interplay between HBCUs and their athletic programs, tracing the evolution of athletics within these institutions and examining its impact on the dynamics of future HBCUs. Through a qualitative approach, segments reveal innovative administrative practices and uncover future opportunities that lie ahead for HBCUs with nascent leadership. Through exploration, this chapter affirms the role of HBCU athletics departments in shaping Next-Gen HBCUs throughout higher education. The future holds promising opportunities to thrive in both academic and athletic domains for HBCUs embracing a student-centered approach.

Pancreatic ductal adenocarcinoma (PDAC) responds poorly to conventional treatments and immunotherapy. We previously developed a binary oncolytic/helper-dependent adenovirus system (CAdTrio) that facilitated oncolysis and expressed the immunomodulatory molecule interleukin-12 and a programmed death ligand 1 (PD-L1) blocking mini-antibody. Given that CAdTrio enhanced endogenous natural killer (NK) cell anti-tumor activity in humanized mice bearing PDAC tumors and that NK cells can be adoptively transferred to patients safely in the allogeneic setting, we hypothesized that a combination of CAdTrio and allogeneic NK cells expressing a HER2-specific chimeric antigen receptor (HER2.CAR-NK) would be an effective, entirely “off-the-shelf” treatment against PDAC. We found that CAdTrio-derived immunomodulatory molecules prolonged HER2.CAR-NK persistence at tumor sites, allowing long-term tumor growth control and improved survival in both humanized mice and a heterogeneous PDAC patient-derived xenografts (PDX) model. This effect was based on CAdTrio-derived transgene support that shifted HER2.CAR-NK gene expression to that resembling an NK memory-like phenotype. Additionally, this allogeneic combination therapy was tolerated in humanized mice. Together, these data suggest that CAdTrio and HER2.CAR-NK cell combination immunotherapy may be a novel and effective option for the treatment for immunologically “cold” PDAC tumors.

Additive manufacturing offers unmatched design flexibility, but this advantage comes with inherent process uncertainties that can lead to defects affecting mechanical properties. Ensuring consistent quality requires reducing part-to-part variations, yet traditional ex-situ characterizations, such as X-ray computed tomography, are time-consuming and constrained by part complexity. To address this challenge, we propose a low-cost, in-situ monitoring framework utilizing unsupervised learning to detect and classify defects in Laser Powder Bed Fusion (LPBF). Our approach leverages optical imaging data captured during printing to identify anomalies. In this study, image processing techniques, including contrast enhancement, normalization, and noise filtering, are applied to extract critical features such as melt pool, plume, and spatter behavior, which are key indicators of print stability. Principal Component Analysis (PCA) is then used to reduce dimensionality while preserving key process variations. Subsequently, unsupervised clustering techniques, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), are applied to classify printing conditions to detect outliers indicative of defect formation. This study provides valuable insights into the relationship between in-situ monitoring data and defect formation in LPBF, demonstrating the feasibility of machine learning-driven real-time quality assurance. By correlating detected outliers with actual porosity and defects, this approach has the potential to reduce reliance on post-processing inspections, improve defect prediction accuracy, and enhance the reliability of additive manufacturing processes.

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor's amplitude = -17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end.