Digital Commons Network^™

Secure Federated Learning Via Neural Cryptography With Homomorphic Operations, Espen Sele, Ferhat Ozgur Catak, Jungwon Seo, Murat Kuzlu

Engineering Technology Faculty Publications

This study examines neural cryptography with homomorphic operations as an alternative secure aggregation method for federated learning (FL). It proposes a novel neural cryptographic system supporting homomorphic addition on fixed-point encrypted data, and consisting of three networks, namely (1) an encryption network (Alice), (2) a homomorphic network (HO), and (3) a decryption network (Bob), along with an adversarial Eve network. Using the MNIST dataset, the proposed Neural Homomorphic Operation System (NHOS) is evaluated against a plaintext baseline and the CKKS scheme, a widely used public-key homomorphic encryption method. The results show that the proposed NHOS approach offers a satisfying performance, …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

T3-Ciders: Train-The-Trainer And Community Building To Increase Cyberinfrastructure Adoption In Cybersecurity Research And Education, Wirawan Purwanto, Mohan Yang, Peng Jiang, Shanan Chappell Moots, Masha Sosonkina, Hongyi Wu

University Administration Publications

T³-CIDERS is a train-the-trainer program to increase the adoption of advanced cyberinfrastructure (CI) and data skills into the fabric of research and education in cybersecurity and cyber-related disciplines. T³-CIDERS trains faculty, researchers, and students as “future trainers” (FTs) with hands-on technical and instructional skills to enable more people to effectively leverage CI in cybersecurity. The program includes a series of technical pre-training modules, a weeklong summer institute, ongoing learning engagements conducted over an academic year; it culminates with the FTs conducting locally tailored CI-infused training events at their respective home institutions. Ultimately, T³-CIDERS aims to build a “CI+cybersecurity” community of …

Interaction Of Cardiac Leiomodin With The Native Cardiac Thin Filament, Madison Little, Cristina M. Risi, Tania M. Larrinaga, Mason D. Summers, Tyler Nguyen, Garry E. Smith Jr., Jennifer Atherton, Carol C. Gregorio, Alla S. Kostyukova, Vitold E. Galkin

Department of Biomedical and Translational Sciences Faculty Publications

Every heartbeat depends on cyclical contraction-relaxation produced by the interactions between myosin-containing thick and actin-based thin filaments (TFs) arranged into a crystalline-like lattice in the cardiac sarcomere. Therefore, the maintenance of thin filament length is crucial for myocardium function. The thin filament is comprised of an actin backbone, the regulatory troponin complex and tropomyosin that controls interactions between thick and thin filaments. Thin filament length is controlled by the tropomodulin family of proteins; tropomodulin caps pointed ends of thin filaments, and leiomodin (Lmod) promotes elongation of thin filaments by a “leaky-cap” mechanism. The broader distribution of Lmod on the thin …

Revealing Rainfall Partitioning Characteristics Of Shrubs In Semi-Arid Sandy Land: Based On In-Situ Measurement Data, Hu Liu, Limin Duan, Yongzhi Bao, Xin Tong, Huimin Lei, Zhiming Han, Wenrui Zhang, Xixi Wang, V. P. Singh, Tingxi Liu

Civil & Environmental Engineering Faculty Publications

Rainfall is partitioned by the vegetation canopy into three components: throughfall, stemflow, and canopy interception, which profoundly influence key hydrological processes, including vegetation growth, groundwater recharge, water cycling, and regional water balance. However, research comparing the applicability of throughfall measurement methods in semi-arid shrub communities remains scarce, and quantitative analyses of rainfall partitioning processes based on in-situ observations are still inadequate. This study focuses on the sand-fixing pioneer vegetation of semi-arid sandy land, Caragana microphylla, and conducts simultaneous throughfall measurements using the rain gauge method and the trough method to compare the differences between the two approaches. Based on …

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Implementing A Chatbot To Promote Hereditary Breast & Ovarian Cancer Genetic Screening In Women's Health: Identifying Barriers And Facilitators To Screening Adoption, Easton N. Wollney, Shireen Madani Sims, Luisel J. Ricks-Santi, Elizabeth Eddy, Daniel Wiesman, Carla L. Fisher

Department of Biomedical and Translational Sciences Faculty Publications

Background

To promote genetic screening among women at risk for hereditary breast and ovarian cancer (HBOC), the American College of Obstetricians and Gynecologists recommends that risk assessment be integrated into practice. Chatbots like the Genetic Information Assistant (Gia®) are increasingly implemented to expand access to hereditary genetic screening. Factors that impact chatbot implementation for HBOC risk screening and women's uptake are not fully realized. To refine implementation strategies prior to full scale implementation, we sought to identify women's perceived facilitators/barriers to adopting Gia screening in a rural population within a large healthcare system in the southern United States.

Methods

We …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

An Overview Of The Special Issue, Rachel Gibson, Trent Buskirk

Data Science Faculty Publications

[Introduction] Across the quantitative social sciences, researchers increasingly face significant challenges and opportunities prompted by the arrival of new sources of very rich, highly granular, and often unstructured digital data. While traditional methods such as surveys and content analysis tools remain indispensable for measuring individual attitudes, behaviors, demographic characteristics, and media messaging online, they often struggle to capture the complex multimodal information streams and metadata generated by social media platforms, mobile devices, sensors, and tracking applications. Collecting and analyzing these diverse new forms of content, dynamic moment-to-moment behaviors, and naturally occurring interactions has become a pressing and exciting research task-one …

A New Deepfake Detection Method With No-Reference Image Quality Assessment To Resist Image Degradation, Jiajun Jiang, Wen-Chao Yang, Chung-Hao Chen, Timothy Young

Electrical & Computer Engineering Faculty Publications

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

The Pre-Polarization And Concentration Of Cells Near Micro-Electrodes Using Ac Electric Fields Enhances The Electrical Cell Lysis In A Sessile Drop, Kishor Kaphle, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Cell lysis is the starting step of many biomedical assays. Electric field-based cell lysis is widely used in many applications, including point-of-care (POC) applications, because it provides an easy one-step solution. Many electric field-based lysis methods utilize micro-electrodes to apply short electric pulses across cells. Unfortunately, these cell lysis devices produce relatively low cell lysis efficiency as electric fields do not reach a significant portion of cells in the sample. Additionally, the utility of syringe pumps for flow cells in and out of the microfluidics channel causes cell loss and low throughput cell lysis. To address these critical issues, we …

Key Brain Region Identification In Obesity Prediction With Structural Mri And Probabilistic Uncertainty Aware Model, Walia Farzana, Megan A. Witherow, Ahmed Temtam, Liangsuo Ma, Melanie Bean, F. Gerry Moeller, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Monitoring Plastic Pollution Using Bioindicators: A Global Review And Recommendations For Marine Environments, Matthew S. Savoca, Neil Angelo Abreo, Andres H. Arias, Laura Baes, Matteo Baini, Elisa Bergami, Susanne Brander, C. Anela Choy, Illaria Corsi, Bavo De Witte, Camila Domit, Sarah Dudas, Emily M. Duncan, Claudia E. Fernández, Maria Cristina Fossi, Ostin Garcés-Ordóñez, Brendan J. Godley, Daniel Gonzalez-Parades, Victoria González Carman, Bonnie M. Hamilton, Britta Denise Hardesty, Sang Hee Hong, Shirel Kahane-Rapport, Lauren M. Kashiwabara, Mariana Baptista Lacerda, Guillermo Luna-Jorquera, Clara Manno, Sarah E. Nelms, Cristina Panti, Diego J. Pérez-Venegas, Christopher K. Pham, Jennifer F. Provencher, Sara Purca, Harunur Rashid, Yasmina Rodríguez, Conrad Sparks, Chengjun Sun, Martin Thiel, Catherine Tsangaris, Robson G. Santos

Biological Sciences Faculty Publications

Monitoring the movement of plastic into marine food webs is central to understanding and mitigating the plastic pollution crisis. Bioindicators have been a component of the environmental monitoring toolkit for decades, but how, where, and which bioindicators are used in long-term monitoring programs has not yet been assessed. Moreover, these programs have yet to be synthesized and evaluated globally. Doing so is imperative if we are to learn from these pioneering programs and expand on their efforts. We reviewed global monitoring programs using bioindicators that focus on plastic pollution and found 11 worldwide that met our definition of long-term monitoring. …

Heterotrophy Of Particulate Organic Matter Subsidies Contributes To Divergent Bleaching Responses In Tropical Sclaractinian Corals, Connor R. Love, Kelly E. Speare, Michael D. Fox, Veronica Z. Radice, Kelton W. Mcmahon, Gretchen E. Hofmann,, David L. Valentine, Marie E. Strader

Biological Sciences Faculty Publications

Heterotrophic feeding on plankton and particulate organic matter (POM) by tropical Scleractinian corals is known to aid in the resistance and recovery from thermally induced bleaching. However, the relative importance of heterotrophy in promoting bleaching resistance and recovery is likely to vary based on ecological context and the severity of heat stress. In 2019, the Pacific Island of Mo'orea experienced mass coral mortality during a widespread marine heatwave. Many Acropora hyacinthus colonies on the shallow reef slope (5 m) were resistant to bleaching, while colonies on the deeper fore reef (14 m) often bleached and subsequently recovered, despite similar thermal …

Analyzing Microbial Communities In Porites Astreoides, Sanaa Caldwell, Katie Parker, Daniel Barshis

Biological Sciences Faculty Publications

Coral reefs are vital marine ecosystems that support many different species and ecosystem services through protecting coastlines from storms and erosion, providing raw materials for medication, and facilitating diversity amongst marine species. Anthropogenic climate change is causing ocean temperatures to exceed the range of coral thermal tolerance resulting in more coral bleaching events globally. However, some corals have the capacity to withstand high temperatures through different thermal tolerance strategies. A balanced microbial community plays a critical role in coral health and recent studies have indicated that a balanced microbiome is associated with temperature-stress tolerant corals. Yet, there is a severe …

A Blueprint To Greener Shorelines: Advancing The Effectiveness, Sustainability, And Widespread Adoption Of Coastal Nature-Based Solutions Through Transdisciplinary Research, Taylor M. Sloey, Sierra Hildebrandt, Rebecca L. Morris, Matthew V. Bilskie, Aaron Bland, David Bushek, Gabriella Dipetto, Daniel Elefant, Vincent Encomio, Ramin Familkhalili, Christine Hladik, Danielle Kreeger, Avery B. Paxton, Cindy M. Palinkas, Latina Steele, Andrew Scheld, Daisuke Taira, Jason D. Toft, Armando J. Ubeda, Christine Whitcraft, Donna Marie Bilkovic

Biological Sciences Faculty Publications

Coastal nature-based solutions (NbS) have emerged as powerful tools to enhance sustainable development and ecological restoration goals. As a rapidly growing field spanning across social, political, ecological, economic, and engineering disciplines, it is critical that researchers working in coastal NbS regularly attempt to identify emerging focal areas for scientific inquiry. Following the 27th Biennial meeting of the Coastal and Estuarine Research Federation, we provide a transdisciplinary perspective (including biologists, engineers, oceanographers, geoscientists, economists, and facilitators of workforce training programs) of pertinent research questions that, if answered, will advance the effectiveness, sustainability, and widespread adoption of coastal NbS. These suggestions for …

Thermal Tolerance Limits Of Mesophotic Corals In American Samoa, Daniel Barshis, Veronica Z. Radice, Katherine E. Parker, Nicholas Evensen

Biological Sciences Faculty Publications

We analyzed the thermal tolerances of multiple coral species from an upper mesophotic (38–40m) and a shallow reef (5–10m) with our portable Coral Bleaching Automated Stress System (CBASS). Depth generalist Montipora grisea (deep and shallow) and depth specialists Pachyseris speciosa and Leptoseris sp. (deep only) were assayed from Leone, located on the western coast of Tutuila in American Samoa. Fragments from 10 genotypes of each species were exposed to a 17-hour thermal stress assay reaching four maximum temperatures ranging from 30–38°C under in situ light conditions (150 μmol photons m⁻² s⁻¹ under a Lee Filters “deep …

High Temporal Resolution Gene Expression Analysis Of Acute Heat Stress Response In Acropora Cervicornis And Porites Astreoides, Katherine Parker, Nicolas Evensen, Iliana Baums, Daniel Barshis

Biological Sciences Faculty Publications

Widespread coral bleaching and mortality events are becoming increasingly frequent due to rising ocean temperatures, though certain coral species and populations have displayed higher tolerance to elevated emperatures than others. As such, it is of high research priority to identify thermally tolerant coral populations for research and conservation and assess the biological mechanisms allowing these populations to survive exposure to higher temperatures. This research utilized the Coral Bleaching Automated Stress System (CBASS), a portable experimental aquaria system with precise temperature manipulation, to assess the thermal thresholds and heat stress response of two coral species from Summerland Key, FL, USA. Seven …

A Multispecies, Multisite Assessment Of Coral Thermal Tolerances In American Samoa, Using Acute Heat Stress Assays, Ponchanok Weeriyanun, Nicolas R. Evensen, Katherine E. Parker, Veronica Z. Radice, Daniel J. Barshis

Biological Sciences Faculty Publications

Reef-building corals are being impacted by increasing seawater temperatures, with marine heatwaves leading to mass bleaching events and extensive mortality. However, corals can display variation in thermal tolerance, both within and among species. In this study, we sampled four coral species (Acropora hyacinthus, Montipora grisea, Pocillopora verrucosa, and Porites lobata) from two sites on Tutuila, American Samoa, with contrasting environmental regimes but similar Maximum Monthly Means (MMM). Coral fragments were subjected to short-term acute heat stress experiments using the Coral Bleaching Automated Stress System (CBASS). PAM Fluorometry was used to measure the symbiont’s effective quantum yield (Fv/Fm) following heat stress …

Efficacy Of Stress Acclimation Techniques In Coastal Wetland Plants (Spartina Alterniflora And S. Patens): Implications For Restoration And Nursery Production, Taylor M. Sloey, Kylie D. Gieser, Kaitlyn Pounders, Sofía E. Báez, Jennifer L. Whytlaw

Biological Sciences Faculty Publications

The implementation of coastal nature-based features, created wetlands, and wetland restoration has increased globally over recent years. Many of these projects have been successful in meeting their goals, however, when projects fail, it is often attributed to mortality of transplanted vegetation. Transplant mortality may be due to inappropriate timing, poor site selection, or abiotic stressors associated with transplant shock. Science-backed guidelines have been well established in many coastal regions regarding site specifications and species selection, but nursery practices for reducing transplant shock remain inconsistent. In this study, we tested a combination of four salinity acclimation treatments and two inundation acclimation …

The Pluralistic Natural Capital Values Of A Tropical City, Adrienne Grêt-Regamey, Justine Saunders, Peter Edwards, Daniel Richards, Jahson I. Alemu, Natasha Bhatia, Roman Carrasco, Zuzana Drillet, Tze Kwan Fung, Yan Feng Leon Gaw, Wanggi Jaung, Andrea Law, Rachel Ai Ting Leong, Aikeen Youu Ming Lim, Mahyar Masoudi, Yudhishthra Nathan, Rachel Rui Ying Oh, Wen Ting Ooi, Fairul Edros Ahmad Shaikh Shaikh, Xiao Ping Song, Claudia L.Y. Tan, Puay Yok Tan, Sergio Wicki, Lynn-Wei Wong, Yanyun Yan, Erik Yando, Alex Thiam Koon Yee, Jingyuan Zhang, Daniel A. Friess

Biological Sciences Faculty Publications

Nature in cities is essential for human well-being. Quantifying and valuing the goods and services provided by nature to city dwellers is missing in tropical contexts. Yet, as cities worldwide face similar challenges, understanding the services provided by tropical urban ecosystems becomes imperative for effective management. Here, we present the first Natural Capital Assessment of a tropical city, unveiling three critical insights. Firstly, we demonstrate the vital reliance of a developed tropical city on nature, particularly for climate change mitigation through regulating services. Secondly, we identify intact natural areas as Singapore’s most valuable assets, stressing the significance of the quality …

Low Salinities Impair Heat Tolerance And Alter Reproductive Traits With No Implications For Survival In A Marine Annelid From Coastal Habitats, Gabrielle Newton, Gloria Massamba-N'Siala

Biological Sciences Faculty Publications

Climate change is expected to intensify salinity and temperature fluctuations in estuarine and coastal habitats. However, the impact of salinity variation on ectotherms’ thermal tolerance remains underexplored, limiting our understanding of the interplay between these stressors. This study investigated salinity-dependent changes in life history traits (survival, growth rate, fecundity, and egg size) over a 33-day exposure to a salinity gradient (12, 17, 22, 27, 32, 35, 40, 45, 50) in the marine annelid Ophryotrocha labronica. Individuals that survived the salinity treatments were assessed for their upper and lower thermal tolerance limits. Female individuals, the target of the study, exhibited …

Future-Oriented Coastal Protection: The Utility Of Living Shorelines Under Changing Climatic Conditions, Gabriella R. Dipetto, Donna Marie Bilkovic, Taylor M. Sloey, Erik S. Yando, Erik L. Walters

Biological Sciences Faculty Publications

An accumulation of novel nature-based shoreline management techniques have developed over the past two decades out of the continued need to protect waterfront land combined with an awareness of the relationship between healthy coastal ecosystems and human societies. Shoreline protection strategies such as living shorelines incorporate these nature-based approaches to simultaneously enhance ecosystem functioning while providing shoreline stabilization benefits. The continued role of these approaches into the future, however, will be challenged by a rapidly changing coastal environment. While living shorelines have received global attention and integration into climate policy and governance frameworks, the ability of shoreline systems to withstand …

Summer Temperature Maxima Already Challenge Thermal Capacities Of Coral In The Southern Persian/Arabian Gulf, Rachel Alderdice, Gabriela Perna, Hannah Manns, Anna Fiesinger, Luigi Colin, Kathryn Stankiewicz, Kelly Gomez-Campo, Samantha Goyen, Line Bayse, Nitin Baliga, Jake Valenzuela, Iliana B. Baums, Daniel J. Barshis, John A. Burt, Christian R. Voolstra

Biological Sciences Faculty Publications

The future of coral reefs remains uncertain as intensifying ocean warming continues to drive coral decline and demise worldwide. Corals occurring in extreme environments such as the Persian/Arabian Gulf (PAG), the world’s warmest sea, represent a promising resource to study the genetic architecture of thermal tolerance. However, their thermal capacities remain unresolved. We applied short-term heat stress assays using the Coral Bleaching Automated Stress System (CBASS) to determine signatures of thermal tolerance from three common species (Porites harrisoni, Platygyra daedalea, and Cyphastrea microphthalma) across three sites (two in the extreme southern PAG and a more benign …

Carbon Dynamics Across The Mangrove-Tidal Flat Ecotone In A Tropical Seascape, E. S. Yando, I. J.B. Alemu, L. Kiah Eng, T. M. Sloey, M. Van Breugel, Bhatia N. Friess

Biological Sciences Faculty Publications

Mangroves and other blue carbon ecosystems are important for climate through the storage and sequestration of carbon. Most research estimates carbon dynamics by subsampling central portions of the ecosystem and are then upscales using habitat maps to patch, landscape, or regional levels. However, heterogeneity is rarely accounted for - particularly at edges. Further, adjacent tidal flat ecosystems have only recently been considered for carbon storage, but may be undervalued if areas where they transition with vegetated systems are not accounted for. To address the lack of information at coastal ecotones, we examined both soil carbon storage and carbon dioxide flux …