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CPP-Mediated Delivery of Peptidoglycan Hydrolases as Novel Enzybiotics
No full textCPP-Mediated Delivery of Peptidoglycan Hydrolases as Novel Enzybiotics
Jacob Clawson, Tina Willoughby, Daniel Morris and Jonathan McMurry Department of Biochemistry, Kennesaw State University
In the medical field there has always been a topic of discussion on how to deal with nosocomial diseases. Recently there has been an upturn of pathogens that are nosocomial while also being antibiotic resistant as they hide inside the cell. Peptidoglycan hydrolases (PHs), which degrade the cell wall structures of bacteria, are a promising alternative to traditional chemotherapeutics. However, against intracellular pathogens such as Staphylococcus aureus, they, too, face a significant problem in crossing the cell membrane. Cell-penetrating peptides (CPPs) offer the possibility of overcoming delivery barriers for biomolecules, but protein ‘cargos’ often get trapped in the endosomal pathway, failing to escape which ultimately averts the cargo from getting to its desired destination. The McMurry Group developed TAT-CaM to solve the problem by utilizing non-covalent, Ca2+ -dependent coupling between CCP to a cargo via calmodulin-calmodulin binding site (CBS) interactions. By employing TAT-CaM to deliver PHs with engineered CBSs, we hypothesize that we will be able to efficiently deliver PHs to mammalian cell interiors, resolving intracellular infections. In this study, several CBS-PHs were designed, expressed, purified, characterized and delivered to mammalian BHK cells. First steps in assaying antibacterial activity in a tissue culture infection model will also be described. Success will enable further initial development of a novel, a class of antibacterial therapeutics
What does it take to make a B-body?
No full textNuclear domains are distinct compartments within the nucleus that selectively concentrate specific proteins, yet their regulation remains largely unclear. One such domain, B-body, forms in Drosophila flight muscles and contains the RNA-binding protein Bruno (Bru). Previous work in our lab identified the long non-coding RNA Hsr-omega as a scaffolding molecule for B-body. Here, we investigate which regions of Hsr-omega and Bru are essential for B-body formation. Using bioinformatics, we selected several Hsr-omega regions: the conserved 5’ region (Hsr5) found in all isoforms, the core repetitive region (RR) present in long isoforms, and three extended regions (RF1, RF2, and RF3) unique to the longest isoform, Hsr-omega-RF. We cloned and expressed these regions in flight muscles, then used immunofluorescence and in situ fluorescence hybridization to assess their co-localization with Bru. Additionally, we expressed Bru mutants to identify the protein regions required for Hsr-omega interaction. None of the truncated Hsr-omega constructs were able to interact with Bru to form B-body-like structures. However, the ability of Bru to bind Hsr-omega in B-bodies was mapped to its RNA-Recognition Motif 2 (RRM2). These findings suggest that protein recruitment into nuclear domains depends on RNA-binding specificity, while RNA\u27s ability to organize nuclear domains is length-dependent
Investigating the Impact of Sugar on Protein Stability by High Resolution Mass Spectrometry
No full textEnsuring that protein, antibody, peptide, and enzyme-based medications maintain their native structure during long-term storage is critical to the safety and efficiency of pharmaceuticals. A critical aspect of preservation for protein-based therapeutics is excipients, such as sugars, that can maintain their stability. Sucrose is a widely used stabilizer in the pharmaceutical industry. Various research suggests that sucrose can preserve the conformational stability of proteins during storage conditions. We hypothesize that although sucrose preserves the protein structure, we suspect that sucrose can strongly bind with the protein through glycosylation which may alter the function of the protein. Glycosylation disrupts the native structure of the protein, promotes aggregation, blocks active sites, reduces function, and leads to degradation, all of which decrease the protein’s shelf life. To test this hypothesis, we have employed high resolution mass spectrometry investigations monitoring the change of protein native conformation and glycosylation. Various concentrations of sucrose were prepared, incubated with a model protein Lysozyme, and analysed using mass spectrometry. All mass spectra were obtained using an Orbitrap Exploris 240. In this experiment, the control sample was Lysozyme in water. At lower concentration of 0.1mM sucrose, Lysozyme showed the binding of one sucrose molecule. At an increased concentration of 0.5mM sucrose, seven sucrose molecules are adducted with Lysozyme, while incubation with 1mM sucrose exhibited the formation of nine adducts. Furthermore, Lysozyme incubated with 3 mM sucrose displayed fifteen sucrose adduct formation with Lysozyme. It is evident that despite widespread usage as stabilizing agent of protein-based therapeutics, sucrose is not an effective stabilizing agent because it promotes unintentional glycosylation, which may alter the protein function and stability. In future, tandem mass spectrometry experiments will be performed to locate the glycosylation site of sucrose
Metal distribution in sands of Lake Allatoona
No full textUsing an XRF instrument, metal content of sand from various locations around Lake Allatoona was determined. Multiple samples were obtained from the same location with varying results. Many locations were analyzed. There was no one location where a point source of contamination was apparent. The results will show the ppm of lead, chromium, mercury that was prevalent in the samples, along with some other metals. Comparison to acceptable concentration levels of the metals will be discussed
Benford’s Phenomenon to Scientific Explanation in Kennesaw, GA
No full textThis research investigates the application of Benford’s Law in analysing the first-digit probabilities of real-world functions, focusing on economic data. Six datasets were collected, each containing an index and actual data values, ensuring statistical fairness and a sample size exceeding 100 for validity. Using Excel, the data was structured and transformed to simplify calculations, enabling graphical representations for analysis.
The study found that Benford’s Law, which was originally developed for datasets following exponential rules, was not observed in the analysed functions due to the specific nature of the data. While the law can be applied to other algebraic functions, each type of function provides a unique formula for first-digit probabilities. The selected datasets did not align with the expected probability distribution, partly due to inconsistencies such as abrupt jumps instead of a continuous sequence. The graphical visualization of data played a crucial role in identifying these deviations.
These findings emphasize that while Benford’s Law is a powerful tool for detecting irregularities in naturally occurring datasets, its applicability depends on the dataset\u27s underlying structure. The study highlights that the law\u27s effectiveness is not universal and varies with different algebraic functions. Understanding when Benford’s Law does not apply is equally valuable, as it prevents misinterpretation in fraud detection and data validation. This study reinforces the importance of careful dataset selection in statistical analysis and data science applications
Stories about Reading in Schools and What they can tell Educators about Teaching Reading
No full textThere is no doubt that reading is a fundamental skill in each person\u27s life. Teachers play a pivotal role in the development of reading skills and relationships with reading. This project seeks to explore pre-service teachers’ relationships with reading throughout their lives, with hopes of initiating the conversations that will improve their reading lives, and in turn that of the children they teach. This research investigates the reading lives and experiences of pre-service teachers and how they affect their plans for approaching teaching reading in their future classrooms. To gather information, subjects will be interviewed with questions focusing on personal experiences and feelings. To organize data, the “Portraiture” methodology created by Sarah Lawrence Lightfoot (1997) will be used. This methodology focuses on each subject as a whole person, capturing their individuality as he or she answers interview questions. The results of this research will showcase the complex relationship between pre-service teachers\u27 reading lives and experiences and their plans for teaching reading
Creating a Digital Twin of the Human Heart Using Machine Learning
Full text linkPersonalized simulations of the left atrium enable professionals to create patient-specific models that replicate the electrophysiological and biomechanical behavior of the atrium. Patient-specific CT/MRI data is segmented to create finite element analysis (FEA) meshes, which enable precise modeling. However, manual segmentation is time-consuming and prone to errors. Additionally, existing neural network-based tools fail to capture detailed anatomical features limiting their use for FEA simulations and personalized treatment planning. This research aims to develop an automated neural network-based tool to accurately segment the left atrium and create a ready-to-use FEA mesh for left atrium simulations, or a digital twin of the human heart. To address these challenges, the focus was on the intricate anatomy of the left atrium, including the appendage and trabeculations, to improve both segmentation accuracy and simulation quality. To achieve this, a neural network will be trained using 120 3D CT/MRI images from public datasets and 100 CT images from Emory University collaborators. The images are then manually segmented to render a replica of the left atrium through the SimVascular workflow. The segmentation is then smoothed using the software tool Meshmixer to create high-quality training datasets. Data augmentation will enhance the dataset. A HeartDeformNet-derived neural network will be implemented for segmentation, using 150 images for training, 20 for validation, and 50 for testing. The predicted meshes will be compared with manually segmented ones using the DICE score and other quality metrics. Based on the quality metrics, necessary adjustments will be made to the results. The predicted FEA mesh will be integrated with a 0D lumped-parameter model to simulate full-cycle left atrium function based on patient-specific inputs such as ECG signals and pressure measurements. This tool is expected to reduce segmentation time and errors while improving simulation accuracy, contributing to better pre-surgical planning and personalized cardiovascular treatments
Quantum Machine Learning in Science and Cybersecurity
No full textQuantum machine learning has the potential to revolutionize cybersecurity by enabling more precise threat detection across massive datasets, and to explore this potential, our team—composed of Cliff Russell, Hayden Agnew, and Josiah Sado—aims to determine whether a quantum-enhanced model can more effectively detect malicious activities within IBM’s Nutch logs compared to conventional approaches. By focusing on suspicious patterns in both raw and processed logs, we plan to train a quantum-based machine learning system on a carefully filtered dataset and measure its detection accuracy, speed, and scalability against established benchmarks. Preliminary results suggest that quantum methodologies may reduce false positives and uncover hidden anomalies more efficiently, thereby bridging the gap between emerging quantum science and real-world cybersecurity. Ultimately, these findings could lead to more robust and proactive threat detection strategies in both scientific and commercial domains
Trilingual Families’ Literacy Practices with Implications for Classroom Teachers: Through the Experience of Manuscript Writing as Student Researchers
No full textImmigrant families extreme lack of support on all fronts, especially when it comes to trilingual/multilingual literacy practices at schools. Due to lack of resources, it is challenging for families and teachers alike to aid in the students’ literacy development in all languages involved. We need to learn more about effective familial literacy practices that can be conducted by any public-school teacher, and replicable by parents at home. The purpose of this presentation is to explore the process of creating a manuscript and being published through the lens of undergraduate and graduate researchers.
In this presentation, we discuss the process of writing a manuscript aimed to help public-school teachers provide as much literary support to multilingual students in the classroom. Typically, in American schools, English is the only language of interest when it comes to literacy. The goal of this study is to create a greater understanding of various languages among public-school teachers and the best way to practice literacy of heritage languages at school in addition to literacy at home. Additionally, we look into ways that teachers can communicate to the parents of the multilingual students the several literacy practices that are the most efficient in the classroom that can also be managed at home. The expected result of this manuscript is for teachers to adopt literacy practices in the classroom and become knowledgeable in multilingual literacy they can apply to their practice.
This presentation will benefit students looking to publish their research by explaining the process of publishing, from the perspective of an undergraduate researcher. The study itself will benefit teachers looking to make their classroom more inclusive and become a pillar for immigrant families to rely on for language and literacy development in heritage languages in public schools
Evaluating Transferability of Adversarial Attacks Between Models of Different Architectures
No full textThis study investigates the transferability of adversarial attacks across neural network architectures, focusing on a comparative analysis between the Momentum Iterative Fast Gradient Sign Method (MI-FGSM) and the Fast Gradient Sign Method (FGSM). Recent scholarly research has underscored the vulnerability of neural networks to adversarial perturbations; however, the impact of model architectural differences on the transferability of such attacks remains insufficiently explored. This study aims to make a unique contribution by systematically comparing the inter-model attack success rates of MI-FGSM and FGSM, while also extending the analysis to additional architectures such as MobileNet and AlexNet. The research methodology involves generating adversarial examples on a ResNet50 model trained on the MNIST dataset using both MIFGSM and FGSM. These adversarial examples are then transferred to VGG19, MobileNet, and AlexNet to evaluate the effectiveness of each attack method, measured by attack success rate and computational overhead. By applying both MI-FGSM and FGSM under similar conditions, we aim to reveal how momentum-based iterative methods compare with FGSM in terms of transferring attack across neural network architectures. These findings will be discussed in the context of current challenges in neural network robustness and the development resilient machine learning systems