Digital Commons Network^™

New Insights On Hybridization In Potamogeton Floridanus (The Florida Pondweed), Kaitlyn R. Sampson

Graduate Theses and Dissertations (2019 - present)

Freshwater ecosystems are some of the most important and highest threatened habitats in the world, and aquatic plants play an important, but often-overlooked, role in maintaining them. Potamogeton is a diverse and ecologically important aquatic plant genus well known for taxonomic difficulty and rampant hybridization. lbis study aimed to 1) test the hypothesis that Potamogeton jloridanus (Florida pondweed) is a hybrid between P. oakesianus and P. pulcher, and 2) to investigate correlations in ecological conditions for the focal species. This study revealed the discovery of a new population of P. jloridanus in Big Coldwater Creek in Santa Rosa Co., FL, …

Final 2024 Residential Metals Abatement Program (Rmap) Mandan Park Soil Remedial Action Work Plan (Rawp), Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Study Of Electron- And Photon-Induced Dissociative Electron Attachment To Molecules And Within Anion-Molecule Clusters, Mahmudul Hasan

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Dissociative electron attachment (DEA) is a resonant process in which a molecule captures a low-energy electron, forming a transient negative ion (TNI). Subsequently, this unstable TNI fragments into a stable anion and one or more neutral fragments. DEA is crucial in various phenomena, ranging from atmospheric and radiation chemistry to processes occurring in plasmas, and is particularly significant in the context of radiation-induced damage to biological molecules. This study uses different experimental methods to better understand the fragmentation in molecular anions forming through dissociative photoexcitation and electron attachment. We have developed an experimental apparatus for dissociative photoexcitation studies. This apparatus …

Application Of Event Cameras And Neuromorphic Computing To Vslam: A Survey, Sangay Tenzin, Alexander Rassau, Douglas Chai

Research outputs 2022 to 2026

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, …

Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting …

Testing Multiple Environmental Dna Substrates For Detection Of The Cryptic And Critically Endangered Burrowing Freshwater Crayfish Engaewa Pseudoreducta, Kathryn L. Dawkins, Paul Nevill, Brian Chambers, Shane Herbert, Quinton F. Burnham

Research outputs 2022 to 2026

Effective conservation of endangered species depends on knowledge of their distributions, but species detection can often be challenging. An example of this is provided by the Critically Endangered Margaret River burrowing crayfish (Engaewa pseudoreducta), which is highly cryptic. Due to the burrowing habit of this crayfish, detection of this species currently requires a great deal of effort, the results are often non-conclusive, and, as it involves manual excavation of their burrows, the habitat of this and other species is destroyed in the detection process. In response to these challenges, this study developed and optimized a species-specific probe-based qPCR assay targeting …

Strong Marine Heatwaves Trigger Flowering In Seagrass, Catalina A. García-Escudero, Victoria Litsi-Mizan, Pavlos T. Efthymiadis, Vasilis Gerakaris, Oscar Serrano, Eugenia T. Apostolaki

Research outputs 2022 to 2026

In recent decades, the global intensification of marine heatwaves has impacted several ecosystems and species, including the endemic Mediterranean seagrass Posidonia oceanica. However, the scarcity of research in Eastern Mediterranean meadows, where historical and present thermal conditions differ from those of the Western Mediterranean, hampers our ability to draw comprehensive conclusions regarding the species' response to elevated sea temperatures. Here, we studied flowering patterns of P. oceanica meadows (3–15 m depth) of the Greek seas and assessed their potential association with marine heatwaves, while also examining the effects on plant growth associated with the transition from vegetative to sexual reproduction. …

Fedstem-Adl: A Federated Spatial-Temporal Episodic Memory Model For Adl Prediction, Doudou Wu, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines …

Urban Soil Compaction Remediation By Shallow Tillage And Compost In Hydroseeded Lawn, James Jihoon Kang, Adam Flores, Engil Isadora Pujol Pereira, Jungseok Ho

School of Earth, Environmental, & Marine Sciences Faculty Publications

Construction activities often involve removal of topsoil and compaction of the exposed soil by heavy equipments. Such compacted soils with low organic matter can lead to low infiltration and poor vegetation establishment. The objective of this study was to investigate the efficacy of tillage (shallow till) and compost on soil physical and biological properties in a hydroseeded lawn as a post-construction best management practice for soil compaction remediation. The experimental site received a total of four land treatments in five replicated trials and it was hydroseeded with common Bermuda grass: 1) No Tillage + Compost (NT-C), 2) No Tillage + …

Streaminghub - A Realtime Biosignal Processing Framework For Lab Scale Experimentation, Yasith Jayawardana

Computer Science Theses & Dissertations

In human subjects research, biosignals such as eye movements, heart rate, and brain activity, are often collected and analyzed to find patterns with tangible real-world implications. Modern advancements in technology have sparked interest towards analyzing biosignals in realtime. When developing such algorithms, one may expect to find free, open-source tools that provide easy access to live, recorded, and simulated data streams. Yet, biosignal interfaces are often vendor-specific, making cross-vendor biosignal streaming non-trivial. Likewise, reading biosignal datasets is also non-trivial, as their content may be arranged quite differently.

To combat this divide, we provide the scientific community with a realtime biosignal …

Coherent Backscattering Under Conditions Of Electromagnetically Induced Transparency In Ultracold Rubidium, Joshua D. Carter

Physics Theses & Dissertations

This dissertation presents experimental results of coherent backscattering of light in an ultracold ensemble of rubidium atoms confined in a magneto optical trap under conditions of electromagnetically induced transparency (EIT) in a cascade-type system. Electromagnetically induced transparency was investigated experimentally in both a counterpropagating and orthogonal laser geometry and compared to theory. The experimental results were largely in good agreement with theory. Coherent backscattering was then measured with and without an EIT control field present to investigate the modification, if any, that EIT has on the enhancement of the coherent backscattering cone. The results indicated that the electromagnetically induced transparency …

Hydrogen Sulfide As A Strong Ligand Affecting Trace Metal Cycling In The Pacific And Southern Oceans, Nicole R. Buckley

OES Theses and Dissertations

Historically, hydrogen sulfide was considered unimportant in the open ocean since it has primarily been associated with anoxic environments. Yet, in the late 1980s it was suggested that this sulfur gas is also being produced in the oxic, surface ocean through the hydrolysis of carbonyl sulfide (OCS). Since then, several studies have demonstrated that hydrogen sulfide is indeed found in oxic seawater at pico- to nanomolar concentrations due not only to carbonyl sulfide hydrolysis, but also assimilatory sulfate reduction by phytoplankton. Hydrogen sulfide’s importance in seawater is largely due to its high metal-ligand stability constants allowing it to affect the …

A Microservices Approach To Electroencephalography Research In The Public Cloud, Bathsheba Farrow

Computer Science Theses & Dissertations

The rich information content of the brain is embedded in electroencephalography (EEG) data that instantaneously measures its electrophysiological activity. Expansion of EEG data usage for health risk predictions and other applications requires reliable and consistent methods for extracting features from raw signals. However, the intricate nature of EEG signal data analysis is further complicated by the magnitude of variability in current research practices, including data preprocessing strategies. The heavy reliance on antiquated, stove-piped applications and pipelines also highlights a need for improved solutions that enable efficient, distributed preprocessing of large EEG data collections.

To address these challenges, we propose a …

Use Of Cobalt(Iii) And Copper(Ii) Complexes With Thiosemicarbazone Ligands And Berberine Chloride As Anti-Cancer Agents Against Triple Negative Breast Cancer, Duaa Rashad Alajroush

Chemistry & Biochemistry Theses & Dissertations

It is a worrying fact that chemotherapeutic options for treating triple negative breast cancer are limited in nature, including the use of inorganic pharmaceuticals such as cisplatin and its analogues. It has been reported that cisplatin has undesirable side effects when used as a chemotherapeutic drug. As such, there is a need to use non-platinum-containing complexes as chemotherapeutic agents. Based on that issue, it has been reported by our research group and others that cobalt- and copper-containing complexes have shown great potential in anti-cancer properties when compared to cisplatin. In this research project, the cytotoxic effects of the following complexes: …

Study Of Atypical Alarmone Synthesis In Clostridioides Difficile, Declan Nathaniel Butler

Chemistry & Biochemistry Theses & Dissertations

Clostridioides difficile is a Gram-positive anaerobic bacterium that causes infections in humans that costs healthcare systems billions per year. C. difficile infection has high rates of recurrence due to multiple antibiotic resistance. When bacteria are in stressful environments, they produce hyperphosphorylated guanosine ribonucleotide signaling molecules called alarmones. The accumulation of alarmones activates the stringent response (SR), in which bacterial cells induce transcription of stress survival genes to delay growth and replication. The C. difficile SR is regulated by enzymatic activity of a bifunctional synthetase/hydrolase, RelA/SpoT homolog (RSH), and a monofunctional small alarmone synthetase (RelQ). Additionally, the SR is potentially regulated …

The Ecological Applications Of Δ17O: Validating A Novel Technique For Assessing Animal Metabolism And Water Intake, Zachary Taylor Steele

Biological Sciences Theses & Dissertations

Animals obtain water via different processes such as drinking, consuming food, and through metabolic processes like at the end of the electron transport chain when atmospheric oxygen is combined with hydrogen to form ‘cytochrome oxidase water’. Assessing animal water flux and metabolism has mainly been conducted via stable isotope analysis (SIA) of biological samples using hydrogen (²H) and oxygen isotopes (¹⁶O, ¹⁷O, and ¹⁸O; Δ¹⁷O), typically using enriched isotope tracers. However, natural abundance isotope modeling offers an approach for conducting SIA of biological samples that differs because it relies on the unique, …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

Exploring The Effects Of An Alfalfa Leaf-Derived Adsorbent On Microbial Community, Ileal Morphology, Barrier Function, And Immunity In Turkey Poults During Chronic Aflatoxin B1 Exposure, Maria De Jesus Nava-Ramirez, Jing Liu, Juan Omar Hernandez-Ramirez, Xochitl Hernandez-Velasco, Juan D. Latorre, Alma Vazquez-Duran, Guolong Zhang, Roberto Señas-Cuesta, Sergio Gomez-Rosales, Andressa Stein, Billy M. Hargis, Guillermo Tellez-Isaias, Abraham Mendez-Albores, Jesus A. Maguey-Gonzalez

Poultry Science Faculty Publications and Presentations

This article follows-up on our recently published work, which evaluated the impact of the addition of an alfalfa leaf-derived adsorbent in the aflatoxin B₁ (AFB₁)-contaminated diet in regard to the production parameters, blood cell count, serum biochemistry, liver enzymes, and liver histology of turkey poults. This paper presents complementary results on microbial community, ileal morphology, barrier function, and immunity. For this purpose, 350 1-day-old female turkey poults were randomly distributed into five groups: (1) Control, AFB₁-free diet; (2) AF, AFB₁-contaminated diet at 250 ng/g; (3) alfalfa, AFB₁-free diet + 0.5% ( …

T. Boudieri Extract Potentiates The Effects Of Capecitabine Treatment In Human Colon Cancer Cells, Katia Sawaya, Mahmoud Khalil, Ghada Khawaja

BAU Journal - Science and Technology

Resistance to chemotherapy remains a major challenge for colorectal cancer patients worldwide, hence the persistent need to uncover alternative treatments as well as new adjunct therapies in the fight against cancer. Natural extracts constitute an excellent source of bioactive substances that have a promising potential in that regard, with minimal negative side effects. We have previously demonstrated that both the ethanolic as well as the water extracts of Terfezia boudieri, a black desert truffle abundant in and part of the cuisine of the Mediterranean region, have significant antiproliferative effects against colon cancer cells. Our aim in this study was …

Problem-Based Learning Model Assisted With A Humanistic-Merdeka Belajar Approach Improved Biology Students’ Learning Ownership, Dita Tri Mulyani, Dwi Listyorini

Jurnal Pendidikan Sains

Fostering ownership over learning constitutes a pivotal aspect for students to cultivate a comprehensive understanding, employ cognitive strategies effectively, hone essential skills, and refine learning methodologies. Preliminary studies have underscored a prevalent deficiency in the learning ownership among biology students, categorizing it as notably low. The purpose of this study is to ascertain the effect of the Project Based Learning (PBL) model assisted with a Humanistic-Merdeka Belajar (HMB) approach, in nurturing students’ ownership of their learning endeavors. Employing a quasi-experimental design, the research adopts a pre-test post-test non-equivalent control group configuration, administered both prior to and following the intervention within …

Asistensi Mengajar (Am) Program In Merdeka Belajar Kampus Merdeka (Mbkm) As A Pedagogical Mastery Accelerator For Vocational High School Teacher Candidates, Muhammad Idris Effendi, Yoto Yoto, Widiyanti Widiyanti

Jurnal Pendidikan Sains

This research delves into the issue of inadequate pedagogical proficiency among vocational high school (SMK) teacher candidates, primarily stemming from the constrained timeframe of educational internships, typically spanning a mere two months. As an option, this study suggests the implementation of an Asistensi Mengajar (AM) initiative within the framework of the Merdeka Belajar Kampus Merdeka Program (MBKM), extending over a semester’s duration. Employing a literature review methodology, the research highlights that the brevity of conventional two-month internships poses a significant impediment to the development of pedagogical acumen among SMK teacher candidates. The proposed option entails an AM program under MBKM, …

Analysis Of Genetic Engineering Material Misconceptions In Biology Textbooks For Ninth-Grade In Yogyakarta, Silfi Pratiwi, Purwanti Pratiwi Purbosari, Oktira Roka Aji, Rinaldi Rizal Putra

Jurnal Pendidikan Sains

Textbooks play a pivotal role in the pedagogical landscape, serving as indispensable aids for learning, founts of knowledge, and platforms for practical application. This study endeavors to scrutinize the content of textbooks, specifically focusing on ninth-grade biology textbooks within the educational milieu of Yogyakarta City, and to juxtapose these contents with existing literature to discern potential misconceptions. Employing a descriptive qualitative approach, the investigation centers on printed biology textbooks utilized in ninth-grade curricula across junior high schools in Yogyakarta. The selection of textbooks for analysis was based on their prevalence as instructional materials, as determined through survey data. Genetic engineering …

Students’ Science Literacy On Discussion-Based Contextual Approaches Of Human Reproductive System Learning, Yeni Mulyaningsih, Chaerul Rochman, Adam Malik, Tri Wahyu Agustina, Yulia Sukmawardani, Nur Hamidah, Nurul Annisa

Jurnal Pendidikan Sains

The aim of this study was to enhance students’ science literacy through a discussion-based contextual approach in the learning of the human reproductive system. This quantitative research employed an experimental method with a quasi-experimental posttest-only design. The study involved 60 students, with 30 students each in class XI MIPA 4 serving as the experimental group and class XI MIPA 5 serving as the control group. The control group received instruction through a conceptual approach-based lecture method, whereas the experimental group engaged in learning through a contextual approach-based discussion method. The research instrument comprised 10 open-ended science literacy questions pertaining to …

Identify Resources Activated By Students When Solving Straight Motion Kinematics Problems With Different Representation, Nur Utami Amaliah, Sutopo Sutopo, Eny Latifah, Hutkemri Zulnaidi

Jurnal Pendidikan Sains

This research aims to explore kinematic resources in linear kinematics by utilizing different forms of problem representation. The study was conducted by administering 15 reasoned multiple-choice questions to 146 high school students from three districts in Central Sulawesi, Indonesia. The research instrument covers topics in linear kinematics, including distance, displacement, speed, and velocity, presented through diagrammatic representations, graphs, and mathematical equations. The students’ written answers were analyzed both quantitatively and qualitatively. Quantitative analysis was conducted to determine descriptive statistics from the data, while qualitative analysis identified the resources students activated when solving these questions. The findings reveal that the various …

Metabolomics Analysis Reveals Metabolite Diversity Of The Rare Cliff Plant Oresitrophe Rupifraga Unge, Hao Wang, Jinjun Cao, Sheng Chang, Caifeng Yan, Guangming Zhang

Faculty, Staff and Student Publications

Oresitrophe is monotypic, with the only species, Oresitrophe rupifraga Bunge, which is exclusive to China, having special growth and developmental traits due to its habitat. Furthermore, it has bright flowers and medicinal benefits. This study investigated the metabolites present in various tissues of Oresitrophe rupifraga Bunge. Using a widely targeted metabolomics approach, 1965 different metabolites were identified in Oresitrophe rupifraga Bunge. Based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), the aboveground and underground metabolites of Oresitrophe rupifraga differed significantly. The comparison between bulblets and leaves revealed the differential expression of 461 metabolites, whereas the …

Effects Of The Biochar Of Senna Tora On The Soil Fertility Of Misau Farmlands, Bauchi State, Northern Nigeria, Ozoilo Onyeka Calistus, Umar Faruk Hassan, Auwal Adamu Mahmoud, Haruna Baba, Hamza Badamasi, Hannatu Akanang

Journal of Bioresource Management

The use of biochar as an amendment to the soil is attracting the scientific community's attention because of its cost-effectiveness, eco-friendliness, and its promising success in increasing the fertility of the soil and crop yield. In the present study, the effects of biochar from Senna tora applications on soil characteristics and the availability of micronutrients in Misau, Bauchi State, Nigeria, were explored. A completely randomized approach was adopted and duplicated three times, with reference soil (no biochar), 30-day, and 60-day biochar treatments. The biochar was removed at the end of the treatment, and soil samples from the various treatments were …

Containerization On A Self-Supervised Active Foveated Approach To Computer Vision, Dario Dematties, Silvio Rizzi, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global …

Enhancing Tumor Classification Through Machine Learning Algorithms For Breast Cancer Diagnosis, Lawrence Agbota, Edmund F. Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei- Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, Augustina Adjei

School of Mathematical & Statistical Sciences Faculty Publications

In cancer diagnosis, machine learning helps improve cancer detection by providing doctors with a second perspective and allowing for faster and more accurate determination and decisions. Numerous studies have used both classic machine learning approaches and deep learning to address cancer classification. In this study, we examine the efficacy of five commonly used machine learning algorithms; both traditional and deep learning models namely, Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), Decision Tree and Deep Neural Networks (DNN). We analyze their ability to properly classify tumors as Benign or Malignant using the Wisconsin breast cancer dataset (WBCD). Random Forest …

Hierarchical Quantized Autoencoders: Using Hierarchical Models For Data Compression Across Multiple Domains, Armani Lorenzo Rodriguez

Theses and Dissertations

In the era of vast data processing and transmission, sending data over a channel for downstream operations is a very common occurrence. The bandwidth of this data channel acts as a limiting factor in this operation, capping the amount of data that can be sent over a time period. Therefore, in addition to pursuing advancements in networking technology, there exists a need for more efficient means of data compression. Learned compression is the application of machine learning models to the data compression problem, and in this study, we leverage the ability of neural networks to learn the underlying structure of …