Computer Sciences Commons^™

Unpaired Virtual Histological Staining Of Tissue From Autofluorescence Using Regularized Cycle-Consistent Adversarial Networks, Zhesi Wen

Theses and Dissertations

We present a regularized CycleGAN with a Dense Residual U-Net to virtually stain autofluorescence images of tissue into H&E-like images. Our method outperforms standard architectures, reduces artifacts, and achieves superior FID scores, enabling efficient, label-free, and accurate digital pathology for unpaired datasets using multi-channel fluorescence inputs.

Multiscale Integration Of Receptor-Ligand Dynamics Into Discrete And Continuous Tumor Growth Models With Application To Tyrosine Kinase Inhibitor Treatment, Romasa Qasim

Open Access Theses & Dissertations

The epidermal growth factor (EGF) receptor cascade plays a crucial role in the survival and proliferation of tumor cells. Tyrosine kinase inhibitors (TKIs) are a class of drugs that inhibit epidermal growth factor receptors (EGFRs), thereby preventing the downstream signal transduction. Despite their importance, models that link spatial receptor dynamics to tumor growth remain scarce. Further, TKIs act through selective mechanisms, inhibiting active, inactive, or all receptor states, which poses a challenge to traditional modeling approaches.

We propose to numerically study two mathematical models incorporating receptor-dynamics into cancer models to describe the impact of EGFR overexpression and TKIs. The first …

Simulating Chill: Exploring The Cognitive And Therapeutic Potential Of Cold Vr Environments, Jessica Turner, Piper Hutson, James Hutson

Faculty Scholarship

This study investigates the cognitive and therapeutic potential of immersive virtual reality (VR) environments designed to simulate cold conditions. Through the engagement of participants through multisensory stimuli—including vivid visual representations of the Athabasca Glacier, auditory effects of icy winds, and corresponding haptic feedback—the research evaluates neurological and physiological responses associated with attention, emotional regulation, and stress modulation. Participants experienced virtual scenarios featuring icy winds and snow, activating specific neurological pathways involving the occipital lobe, primary visual cortex, superior colliculus, and insula, thus reinforcing sensory integration. Through predictive coding, the anterior insula and hypothalamus were engaged, prompting thermoregulatory simulations and subconscious …

Ai Model For Predicting Asthma Prognosis In Children, Elham Sagheb, Chung-Il Wi, Katherine S King, Bhavani Singh Agnikula Kshatriya, Euijung Ryu, Hongfang Liu, Miguel A Park, Hee Yun Seol, Shauna M Overgaard, Deepak K Sharma, Young J Juhn, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.

OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.

METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 …

Network-Based Attacks In Cloud Computing In 2020-2024, Yaswanth Sai Manikanta Anguluri

Electronic Theses, Projects, and Dissertations

As the use of cloud technologies has increased in the past five years, the number of network attacks is also increasing. During 2020 to 2024, there are lot of changes in cloud technologies which led to various network attacks in the cloud computing environments from 2020 to 2024. This study investigates the evolution of network-based attacks in cloud environments from 2020 to 2024. Data was collected from Kaggle website to analyze the trends of the evolution of network-based attacks. The research questions are: (Q1) How do the trends change in network-based attack from 2020 to 2024 and why? (Q2) Which …

A System And Method For Measuring Spatially Varying Surface Appearances With A Study Of Feathers, Jessica Baron-Lis

All Dissertations

Real-world materials, particularly biological structures such as feathers exhibit complex appearances that vary spatially across their surfaces. The field of computer graphics provides a means of understanding such surfaces through material modeling which uses both analytical models and data acquired from light-surface interactions. There are many efforts within the past decade in measuring materials for graphics, but common limitations in these works include not accounting for spatially varying properties and reliance on neural networks and synthetic datasets.

Feathers from modern birds present diverse appearances due to how light interacts with their unique hierarchical microstructures. Variations in those structures lead to …

Oculomics: Current Concepts And Evidence, Zhuoting Zhu, Yueye Wang, Ziyi Qi, Wenyi Hu, Xiayin Zhang, Siegfried K Wagner, Yujie Wang, An Ran Ran, Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Alex Suh, Yih Chung Tham, Carol Y Cheung, Xiaohong Yang, Honghua Yu, Zongyuan Ge, Wei Wang, Bin Sheng, Yun Liu, Andrew G Lee, Alastair K Denniston, Peter Van Wijngaarden, Pearse A Keane, Ching-Yu Cheng, Mingguang He, Tien Yin Wong

Faculty, Staff and Student Publications

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

2025 Acssc Program, Acssc Planning Committee

Annual Celebration for Student Scholarship and Creativity

No abstract provided.

Volume 16, Maggie Duncan, Madeline Little, Alicia Hoffman, Megan Livesay, Gabrielle Quaresma, Serenity Allen, Laina Pfountz, Ainslie Allred, Sabrina Robles, Nicholas J. Duellman, Trinity L. Deguzman, Melissa H. Savage, Margaret Dudley, Jocelyn Escobar, Olivia Hildreth, Olivia Hopkins, Benjamin Gettier, Lee Kassay, Jade Riddle, Ashley Seiders

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Artist’s Statement Maggie Duncan

On Mentoring Dr. Lee Millar Bidwell

The Hujum Campaign in Uzbekistan and its Consequences by Madeline Little

Wet Cupping Compared to Dry Needling for Treatment of Patients with Low Back Pain: A Critically Appraised Topic by Alicia Hoffman and Megan Livesay

Optimization of eDNA Air Sampling Via 3D Printed Fan by Gabrielle Quaresma

Beyond the Classroom: A Qualitative Study of Teacher Attrition and Retention by Serenity Allen and Laina Pfountz

The Treatment of Subacromial Impingement Syndrome with Platelet-Rich plasma Injections Verses …

Ensemble Learning With Explainable Ai For Improved Heart Disease Prediction Based On Multiple Datasets, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao

Faculty, Staff and Student Publications

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting …

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

Highly Regenerable Magnetic Sulfonated Chitosan Crosslinked With Glutaraldehyde Composite Beads (Iron Sand/Naso3-Chi-G) For Aqueous Mercury Removal, Fathurrahmi Fathurrahmi, Rahmi Rahmi, Lelifajri Lelifajri, Anggun Sixthia Wulan Ayu, Muhammad Iqhrammullah

Karbala International Journal of Modern Science

We developed a novel adsorbent from sulfonated, glutaraldehyde-crosslinked chitosan embedded with magnetic iron sand. The adsorbent was synthesized through a two-step process: (1) sulfonation with N(SO₃Na)₃ to introduce sulfonate groups, and (2) crosslinking with glutaraldehyde to enhance structural stability. The optimal formulation, containing 43.5% iron sand and crosslinked with 0.17 M glutaraldehyde, exhibited the highest Hg²⁺ adsorption capacity (30.74 mg/g) at pH 3. The adsorbents were characterized using Scanning Electron Microscopy (SEM), Fourier transform infra-red spectroscopic (FT-IR), and X-ray diffraction (XRD) techniques. Adsorption equilibrium was achieved within 60 minutes, and isotherm modeling showed that the process followed the Freundlich model …

Enhancing State-Of-The-Art Motor Imagery Classification With Reinforcement Learning, Anton Shepelev

USF Tampa Graduate Theses and Dissertations

One of the key obstacles to the rapid adoption of non-invasive Brain-Computer Interfaces (BCIs) for Motor Imagery (MI) is the low signal-to-noise ratio, and the substantial data requirements which can be mentally taxing for users. EEGNet, a compact Convolutional Neural Network (CNN), has long been considered the state-of-the-art (SOTA) for MI classification, demonstrating strong performance even with limited data. However, recent studies advocate for integrating Deep Reinforcement Learning (RL) to further enhance classification accuracy by dynamically optimizing feature extraction and decision-making processes. Despite this potential, practical implementations remain scarce due to challenges in stabilizing RL training and adapting it to …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

Raising Awareness About Hydrographic Careers Through Sea-Going Opportunities, Juliet Kinney, Rochelle Wigley, Sara Cardigos, Fahima Bellabad, Larissa Marques Freguette

Center for Coastal and Ocean Mapping

There is a worldwide shortage of hydrographic personnel (van Wegen, 2021, Hydro International 2008).  Calls for action to address this issue include the IHO’s Hydrography at Sea opportunities, while intiatiative such as Seabed 2030 are bringing broader attention to the field and helping to catalyze new discussions and partnerships in hydrography (IHO, 2024).  The global sea floor mapping community needs to develop a broader workforce pipeline.  We would like to highlight the importance of providing time at sea and leadership opportunities in developing a robust workforce.  We start with an overview of a selection of current exchange and training programs …

Energy-Aware Clustering Using Intelligent Scheme For Heterogeneous Wireless Sensor Networks, Enaam A. Al-Hussain, Ghaida A. Al-Suhail

Karbala International Journal of Modern Science

Heterogeneous Wireless Sensor Networks (WSNs) involve nodes with varying capabilities, such as different energy levels, sensing ranges, and computational abilities, which enable them to execute different tasks professionally. Clustering techniques play a crucial role in improving energy efficiency and reliability in WSNs. The evolution of cluster based WSNs from homogeneous into heterogeneous techniques allowed the deployment of smart devices capable of performing complex operations in in diverse environments. However, the heterogeneity of nodes necessitates more sophisticated and adaptive algorithms to fully exploit these capabilities. This paper proposes a new protocol, referred to as IT2F-HLEACH, which integrates Interval Type-2 Fuzzy Logic …

Comprehensive Review On The Application Of Bio-Immunoinformatics In The Development Of Highly Ef-Fective New Candidate Vaccines Against Tuberculosis, Ahyar Ahmad, Andriansjah Rukmana, Miski A. Khairinisa, Dian A. E. Pitaloka, Rosana Agus, Rusdina B. Ladju, Tarwadi Ahmad, Astutiati Nurhasanah, Carina C. D. Joe, Muhammad N. Massi, Harningsih Karim, Irda Handayani, Siti Roszilawati Binti Ramli

Karbala International Journal of Modern Science

Tuberculosis (TB) remains a significant public health challenge worldwide. Currently, Bacillus Calmette-Guerin (BCG) is the only vaccine available for TB prophylaxis. However, the efficacy of the BCG vaccine against adult pulmonary TB is considered inconsistent. This condition encourages researchers to look for more effective options, such as subunit vaccines. This condition requires the development of a more effective subunit vaccine to protect active TB in productive and adult ages. There is an urgent need for more effective vaccines, as the Bacillus Calmette-Guérin (BCG) vaccine currently available has inconsistent efficacy and is only partially effective in adults. Bio-immunoinformatics, an interdisciplinary field …

Leveraging Large Language Models For Knowledge-Free Weak Supervision In Clinical Natural Language Processing, Enshuo Hsu, Kirk Roberts

Faculty, Staff and Student Publications

The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partial solutions to this issue, particularly using large language models (LLMs), but their performance still trails traditional supervised methods with moderate amounts of gold-standard data. In particular, inferencing with LLMs is computationally heavy. We propose an approach leveraging fine-tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a prompt-based approach, the LLM is used to generate weakly-labeled …

Designing Accessible Ui/Ux For Epileptic Patients: A Scalable Solution For Music Therapy Delivery, Amethyst G.H. Mckenzie

Computer Science Senior Theses

How can we design an accessible, scalable UI/UX system tailored to the cognitive, visual, and motor impairments of epileptic patients, that ensures safe and effective interactions with music therapy applications? This research explores the intersection of accessibility, user-centred design, and digital health, using an iterative design process to develop and refine the SONATA app—a clinically deployable music therapy platform.

Through two prototype iterations, usability testing, and quantitative event logging, this study compares the effectiveness of structured versus flexible navigation in improving user experience. Key findings reveal that structured navigation reduces unintended detours, while progressive disclosure techniques enhance instructional clarity. Additionally, …

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

Evaluating The New Nd: Yag Laser Method In Phytosynthesizing Silver Nanoparticles And Assessing Their Medical Applications., Arshad Mahdi Hamad, Qanat Mahmood Atiya

Karbala International Journal of Modern Science

Silver nanoparticles (AgNPs) were synthesized via an innovative green synthesis method using amygdalin (Am) as a reducing agent and the Nd: YAG laser as a catalyst. We studied the properties of the nanoparticles using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), ultraviolet-visible spectroscopy (UV), and Fourier transform infrared spectroscopy (FTIR) techniques. All the results of the examination demonstrate excellent structural and optical properties. In addition, the molecular docking of the complex composed of amygdalin and AgNPs was tested on three proteins concerned with the virulence of Pseudomonas aeruginosa and …

Characterization Of 1,8-Cineole (Eucalyptol) From Myrtle And Its Potential Antibacterial And Antioxidant Activities*, Humera Khan

Karbala International Journal of Modern Science

1,8-Cineole is a naturally occurring chemical molecule predominantly found in fragrant plants, particularly Myrtle. Its aroma is distinctive and has been the subject of numerous investigations due to its various biological actions. This study examines the characterization of 1,8-Cineole derived from Myrtle and investigates its antibacterial and antioxidant properties. This study seeks to compare 1,8-Cineole with antibiotics like Amoxicillin and Tetracycline, and moreover, to investigate its antioxidant capabilities against diverse bacterial strains (both Gram-positive and Gram-negative). 1,8-Cineole exhibits the most effective antibacterial properties, demonstrating an inhibition zone of 12.00 mm against Staphylococcus aureus and 10.00 mm against Pseudomonas aeruginosa. …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

A Statistical Framework For Multi-Trait Rare Variant Analysis In Large-Scale Whole-Genome Sequencing Studies, Xihao Li, Han Chen, Margaret Sunitha Selvaraj, Eric Van Buren, Hufeng Zhou, Yuxuan Wang, Ryan Sun, Zachary R Mccaw, Zhi Yu, Min-Zhi Jiang, Daniel Dicorpo, Sheila M Gaynor, Rounak Dey, Donna K Arnett, Emelia J Benjamin, Joshua C Bis, John Blangero, Eric Boerwinkle, Donald W Bowden, Jennifer A Brody, Brian E Cade, April P Carson, Jenna C Carlson, Nathalie Chami, Yii-Der Ida Chen, Joanne E Curran, Paul S De Vries, Myriam Fornage, Nora Franceschini, Barry I Freedman, Charles Gu, Nancy L Heard-Costa, Jiang He, Lifang Hou, Yi-Jen Hung, Marguerite R Irvin, Robert C Kaplan, Sharon L R Kardia, Tanika N Kelly, Iain Konigsberg, Charles Kooperberg, Brian G Kral, Changwei Li, Yun Li, Honghuang Lin, Ching-Ti Liu, Ruth J F Loos, Michael C Mahaney, Lisa W Martin, Rasika A Mathias, Braxton D Mitchell, May E Montasser, Alanna C Morrison, Take Naseri, Kari E North, Nicholette D Palmer, Patricia A Peyser, Bruce M Psaty, Susan Redline, Alexander P Reiner, Stephen S Rich, Colleen M Sitlani, Jennifer A Smith, Kent D Taylor, Hemant K Tiwari, Ramachandran S Vasan, Satupa'itea Viali, Zhe Wang, Jennifer Wessel, Lisa R Yanek, Bing Yu, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Josée Dupuis, James B Meigs, Paul L Auer, Laura M Raffield, Alisa K Manning, Kenneth M Rice, Jerome I Rotter, Gina M Peloso, Pradeep Natarajan, Zilin Li, Zhonghua Liu, Xihong Lin

Faculty, Staff and Student Publications

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple …

An Effective Secure Multi-Objective Task Scheduling Algorithm In Multi-Cloud Environment, V K S K Sai Vadapalli, Ramesh Babu Gurujukota, Phaneendra Varma Chintalapati, Satyanarayana Murty, G. Sai Chaitanya Kumar, Satish Kumar Kode

Karbala International Journal of Modern Science

In cloud environments, task scheduling is essential for improving performance. Nevertheless, the existence of several heterogeneous clouds makes scheduling extremely difficult, requiring increasingly advanced algorithms to manage these environments' diversity and dynamic nature. To solve this, numerous authors have created a variety of task schedulers utilizing heuristic and metaheuristic techniques. Nevertheless, it remains dynamic and challenging because task scheduling is an NP-hard issue. Furthermore, in many complicated situations, it is still problematic to guarantee security throughout the task’s execution. Therefore, this paper introduces a multi-objective security-aware task scheduler using the Crayfish Mud Ring Optimization Algorithm for a multi-cloud environment. This …

Computational Investigation Of The Unveils Nsd2 Inhibition Potential Of Berberis Vulgaris, Sambucus Nigra, And Morus Alba Through Virtual Screening, Molecular Docking, Md Simulation, And Dft Analyses, Supriyo Saha, Vanshita Gupta, Ahad Hossain, Prinsa Prinsa, Jannatul Ferdous, Kiran Bharat Lokhande, Vikash Jakhmola, Sarkar M. A. Kawsar

Karbala International Journal of Modern Science

Nuclear receptor binding set domain protein 2 (NSD2) plays a key role in chromatin regulation and is associated with different cancers and other developmental problems. Berries are rich in major secondary metabolites with anticancer properties. Here, we virtually screened 145 berry phytochemicals as putative NSD2 inhibitors via structure-based virtual screening, molecular docking, MD simulations, and DFT and ADMET analyses. Among them, α-carotene had the maximum docking score of -9.9 kcal/mol, followed by sulfuretin and β-amyrin. MD simulation analysis revealed that the dynamic behavior of the ligand‒NSD2 complexes was within the limit, indicating no significant changes in the structural integrity of …

Evaluation Of Green Tea Yoghurt Enriched With Lacticaseibacillus Paracasei E1 Microcapsules On Macrophage M1 Profile In High Fat-Fructose Diet Mice, Esha Ardiansyah, Nur Alfi Maghfirotus Sa’Adah, Rahmi Izati, Belinda Nabiila Al Faizah, Dawama Nur Fadlilah, Septhyanti Aprilia Kavitarna, Mochammad Fitri Atho’Illah, Siti Nur Arifah, Yoga Dwi Jatmiko, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Obesity is caused by an energy imbalance that increases chronic low-grade inflammation, including macrophage cell infiltration. Adipose tissue macrophages are polarized into pro-inflammatory macrophage type 1 (M1), secrete large amounts of pro-inflammatory cytokines, and activate transcription factors. Yoghurt with probiotics is popular at all ages for its health benefits and must be protected by microencapsulation. Green tea (Camellia sinensis L.) fortification provides yoghurt nutrients while improving its functional qualities and bioactivity. This study aimed to evaluate the effect of microencapsulation of Lacticaseibacillus paracasei E1 in green tea yoghurt (GTY) on the profile of M1 macrophages in mice fed a …

Development And Characterization Of Sodium Alginate-Based Active Edible Films Functionalized With Olive Mill Wastewater Extract, Nassima Hadri, Mohamed Didi Ould El-Hadj, Zineb Mahcene, Fatih Bozkurt, Rusen Metin Yildirim, Youcef Rahmani, Aicha Tedjani, Muhammet Arici

Karbala International Journal of Modern Science

Phenolic compounds from olive mill wastewater (PCO) of Algerian origin were used to produce sodium alginate-based active films using the casting method. The effects of adding various concentrations of PCO (0%, 0.1%, and 0.2% w/v) were evaluated regarding the molecular, morphological, thermal, physicochemical, optical, barrier, biodegradability, antimicrobial, and antioxidant properties of the alginate films. The FTIR and SEM results elucidated the development of a coherent cross-linked structure attributed to hydrogen bonding interactions between PCO and alginate chains. Consequently, the films exhibited enhanced crystallinity and thermal stability, as revealed by DSC analysis. Moreover, PCO addition positively influenced several film properties, including …

Optimizing Electrode Configurations For Eeg Mild Cognitive Impairment Detection, Yi Jiang, Xin Zhang, Zhiwei Guo, Xiaobo Zhou, Jiayuan He, Ning Jiang

Faculty, Staff and Student Publications

The Optimal electrode configuration of Electroencephalograms (EEG) systems for mild cognitive impairment (MCI) detection and monitoring in non-clinical settings, i.e. number of electrodes and the positions of the electrodes, remains to be explored. In the current study, we explored the optimization of electrode configuration for MCI detection. We used a 32-channel EEG device to record the data of 21 MCI patients and 20 cognitively normal elderly (NC) undergoing working memory (WM) tasks. Based on the differential value (MCI group vs. NC group) from the Power Spectral Density (PSD) value of each electrode in θ and α frequency band during WM …