Digital Commons Network^™

Use Of Thyroid Imaging Reporting And Data Systems (Tirads) Ultrasound In Differential Diagnosis Of Thyroid Nodules, Aya Mohamed Hamed Abdelaal, Ghada M Ahmed Gaballah, Donia Mohamed Sobh

Mansoura Medical Journal

Background: Thyroid nodules refer to masses of abnormally proliferating cells with abnormal tissue stiffness in thyroid. This study aimed to assess diagnostic accuracy of Thyroid Imaging Reporting and Data System in differentiating benign from malignant thyroid nodules.

Methods: This cross-sectional study included forty adult patients with sixty-two thyroid nodules. A 7.5-10 MHz linear array transducer was used to perform imaging protocol. Three-dimensional measurements were done on nodules. All thyroid nodules were  examined using doppler ultrasound. All thyroid nodules were described in terms of composition, echogenicity, margins, shape and echogenic foci.  According to ACR TI-RADS,  the points were given …

Preventing Coccus Longulus Infestation, Nicholas Wellls

Honors College Theses

In the Armstrong greenhouse, the Coccus longulus scale insect is a prevalent problem on a specific species of Begonia known as Begonia ‘Richmondensis’. Inside the greenhouse there are several types of begonias with very similar characteristics to the Richmondensis begonia. However, none of these other begonias are infested by C. longulus, despite similar leaf thickness and environmental conditions such as humidity and light. This project examined whether differences in the chemical composition of the Begonia leaves can explain this pattern. Results were not statistically significant, but they showed that the chemicals had a noticeable impact on the scale, and …

Validity Assessment Of Resting Heart Rate Variability From The Garmin Health Snapshot, Kayla M. Porter, Andrew Flatt

Honors College Theses

Purpose: To assess the agreement between the Garmin Forerunner 265 (a commercially available sports watch) and a single-channel electrocardiographic (ECG) chest strap for determining resting heart rate variability (HRV). Secondary aims were to assess the impact of skin tone and body position on measurement accuracy.

Methods: Young adults (n = 30, 57% women) aged 18–39 years without known cardiovascular conditions and without tattooing or scarring on the dorsal left wrist were recruited. HRV was recorded simultaneously using ECG and the Forerunner 265’s optical sensor during Garmin’s 2-minute “Health Snapshot.” Measurements were obtained in three standardized positions: supine, seated, and standing. …

Conceptualizing A Model For Transforming Academic Search With Retrieval-Augmented Generation, Nikesh Narayanan

All Works

This study conceptualizes the transformative potential of RetrievalAugmented Generation (RAG) in academic research, addressing limitations of traditional search methods reliant on keyword matching, Boolean logic, and metadata analysis. RAG combines precise data retrieval with the generative capabilities of advanced AI models, synthesizing dispersed information into coherent outputs. By bridging retrieval accuracy and contextual generation, RAG offers a framework for providing researchers with comprehensive, nuanced, and up-todate insights tailored to their queries. This article explores the integration of tools like ChatGPT and LangChain to outline a RAGbased system, illustrating its potential to enhance academic discovery by harmonizing natural language processing, semantic …

Optimized Hybrid Beamforming For Ris-Assisted Multi-User Mimo In 6g Mmwave Networks: A Low-Complexity Approach To Spectral Efficiency And Interference Mitigation, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Thippa Reddy Gadekallu, Hana Mujlid, Hashim Elshafie, Rashid A. Saeed

All Works

The joint optimization of hybrid beamforming and reconfigurable intelligent surface (RIS) phase shifts in multi-user millimeter-wave (mmWave) MIMO systems is a challenging problem, mainly due to high computational complexity and the lack of adaptive interference management. Existing approaches typically rely on fixed Zero-Forcing (ZF) or Maximum-Ratio Transmission (MRT) designs or require iterative optimization with high overhead, limiting their practical use in dense 6G environments. To overcome these challenges, this research proposes a RIS-Aided Adaptive Zero-Forcing and Maximum-Ratio Transmission Hybrid Precoding (RA-ZMHP) framework for 6G mmWave multi-user MIMO systems. The main novelty of the method lies in an adaptive ZF–MRT mixing …

Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens

All Works

Objective: To develop an AI framework that combines genetic, phenotypic, and lifestyle data for profiling skin-health patterns and generating hypothesis-supporting summaries for potential decision support. Methods and procedures: A dataset of 5,254 individuals integrates six genes (FLG, AQP3, MMP-1, MMP-3, SOD2, GPX), six phenotype severities, and 20+ lifestyle factors. Mutation burden and interactions are tested by ANOVA. K-modes clustering identifies four interpretable dermatological profiles within the cohort and is embedded in leakage-free nested cross-validation (train-only selection; test labels from training centroids). Subtypes are predicted from genetics plus lifestyle using an XGBoost (XGB) classifier; explainability uses gain, permutation importance, and SHAP …

A Novel Bernstein Operational Matrix Approach For Tempered Fractional Differential Equations: Convergence And Stability Analysis, Jalal Al Hallak, Mohammed Alshbool, Ishak Hashim, Eddie Shahril Ismail, Shaher Momani

All Works

Tempered fractional differential equations (TFDEs) incorporate exponential decay into fractional operators to account for truncated memory and semi-long-range dependence in a variety of applications, including anomalous diffusion, viscoelasticity, transport phenomena, geophysical processes, and financial dynamics. In this work, a tempered fractional Bernstein method (TFBM) was proposed for the numerical solution of TFDEs involving Caputo-type derivatives. The proposed formulation combined a Bernstein polynomial approximation with an analytic representation of the Caputo–tempered fractional derivative through operational matrices. On this basis, two collocation-based variants were developed, namely, a Chebyshev-type method (TFBM-C) and a Legendre-type method (TFBM-L). For the linear setting, a convergence analysis …

Empirically Evaluating The Accessibility Of A Pon-Enable Feature Diagrams Notation By The Red-Green Colorblind Community, Mohamed El-Attar, Sarah Kohail, Rima Grati

All Works

In 2016, an enhanced version of a feature diagram notation developed using the Physics of Notations (PoN) framework was introduced. Empirical evidence demonstrated that this revised notation was more cognitively effective than the original. However, the new notation relies on color, specifically red, which poses accessibility challenges for individuals with red–green color vision deficiency, as they cannot perceive the notation as originally intended. Consequently, the cognitive effectiveness of a red–green–deficient (RGD) version of the new notation relative to the original notation remained unknown. Although the PoN framework specifies several principles that may be satisfied with or without the use of …

Mapping Multiclass-Targeted Hate Speech In Online Discourse: An Open Dataset, Sanaa Kaddoura, Sumaia Al-Kohlani

All Works

Online social networks have become central spaces for public discourse, where hostile and discriminatory language toward social groups can cause psychological and social consequences for marginalized communities. Although multiple public hate speech datasets are available, many rely on binary categorization practices that obscure linguistic, cultural, and contextual variation across targeted groups. As a result, minority and less visible forms of hate speech remain insufficiently documented and analyzed. This discussion paper examines methodological limitations in existing hate speech annotation schemes and presents a re-annotation framework applied to the HatEval2019 dataset. The proposed framework introduces target-specific multiclass labels that distinguish subcategories of …

The Occurence Of Expansive Clay Soils In Illinois And Their Effects On Shallow Lightly Loaded Foundations, Greg James Gollaher

Masters Theses

"The damage caused to shallow foundations and slabs on grade by the shrink-swell activity of clay soils is well documented across the United States. There are local and national resources available that delineate areas that are problematic and areas that are not. These resources paint Illinois as an undemanding foundation environment. However, our experience differs from this. Our experience tells us that damage related to the shrink-swell activity of clay soils is widespread, and commonly severe causing not only cosmetic damage to structures and hard improvements but also affecting the serviceability and longevity of structures. My goal is to accurately …

Exploring The Potential Of Renewable Energy For Sustainable Mobility: A Simulation- Based Study Of Hydrogen Vehicle Penetration In Oman’S Road Network, Siham Farrag, Tarek R. Sheltami, Fatma Outay, Ansar Ul Haque Yasar

All Works

Hydrogen fuel is gaining attention as a promising zero-emission energy source, aligning with global sustainability goals and supporting the transition to zero carbon emissions. This study examines the potential of using hydrogen as an alternative fuel for sustainable mobility in Muscat, Oman. We developed an integrated modelling framework that combines microscopic traffic simulation, energy demand modeling, refueling infrastructure station’ estimation, and well-to-wheel (WTW) emissions evaluation. A microscopic simulation software (SUMO) was applied to evaluate the penetration rate of hydrogen-powered vehicles (0%, 20%, 40%, 60%) with different hydrogen production pathways. Results indicate that with a 60% penetration of green hydrogen, total …

Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid

All Works

Balancing energy efficiency with stringent timing guarantees in real-time mixed-criticality systems (MCS) is a key challenge, especially in multicore architectures. This paper introduces a novel energy-aware scheduling framework that integrates dynamic voltage and frequency scaling (DVFS) with a Decreasing-Criticality-Decreasing-Utilization (DCDU) allocation approach. The optimal operating frequencies are obtained at each criticality level; high-criticality tasks are assigned to cores at full operating frequency to maintain timing guarantees, while low-criticality tasks are allocated using worst-case execution times scaled to their optimal frequency. A fixed-priority response-time analysis is used for schedulability in low mode, high mode, and during mode changes. The extensive simulations …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin

All Works

Low-altitude remote sensing networks are increasingly important for applications, such as environmental monitoring, disaster response, infrastructure inspection, and real-time sensing services. However, when many sensing nodes share limited spectrum resources, severe cochannel interference can degrade communication reliability and delay sensing-data delivery. This challenge becomes more critical in edge-enabled deployments, where control decisions must be made under strict latency, memory, and computational constraints. To address this issue, this article proposes a large language model (LLM)-enhanced edge-aware lightweight reconfigurable intelligent surface (RIS)-assisted dynamic channel allocation (EL-RIS-DCA) framework for interference mitigation in dense low-altitude remote sensing networks. The novelty of the proposed framework …

Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed

All Works

Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification …

Rumooz Aljareemah: An Intelligent Search System For Uae Criminal Law With Case Correlation Framework For Forensic Investigators, Rahaf Alnuaimi, Maryam Almarzooqi

All Works

Digital forensic investigations in the UAE encounter dual challenges: effectively correlating data across cases and complying with local legal frameworks. Conventional methods create information silos that hide connections between instances and increase the probability of procedural errors. This paper presents RUMOOZ ALJAREEMAH, a prototype platform for case correlation designed for cybersecurity experts and forensic investigators in the UAE. The approach integrates a correlation engine with a UAE-specific legal compliance framework, using authentication protocols, bilingual assistance, and text-based search algorithms. Our theoretical framework suggests enhancements in investigative efficiency, including reduced case resolution durations, improved identification of cross-case relationships, and a decrease …

Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan

All Works

The increasing digitization of urban infrastructure has introduced advanced efficiency and connectivity in smart cities while exposing them to sophisticated cybersecurity threats. This study explores how Quantum Storage Mechanisms (QSM) can be integrated with digital forensic readiness systems to enhance smart city security and incident response. Through a simulated environment, the research evaluates the effectiveness of QSM against three critical cyberattack scenarios: Distributed Denial of Service (DDoS), sensor spoofing, and supply chain firmware attacks. The findings reveal that QSM-enabled systems outperform traditional cybersecurity tools by ensuring tamper-proof evidence collection, real-time threat detection, and secure long-term data retention. The study also …

Stem Identities Of Iowa State University's Biological And Biomanufacturing Systems Technology Students, Levi Nettleton

Dissertations and Theses @ UNI

STEM identity plays a crucial role in students' pursuit and persistence into the STEM field. Iowa State University's Biological and Biomanufacturing Systems Technology (BBST) undergraduate program, developed in alignment with the goals of Chemurgy 2.0, prepares students for careers in the growing STEM workforce. The purpose of this study is to examine how participation in the Biological and Biomanufacturing Systems Technology program influences undergraduate students' STEM identity and how students within the program perceive themselves within the scope of STEM. Specifically, the study investigates students' perceptions of their STEM identity across the four components of STEM identity: recognition, performance and …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Changepoint Analyses Confirms Global Tropical Cyclone Frequency Decline, Michael Wehner, Thomas Fisher, Norou Diawara, Robert Lund

Mathematics & Statistics Faculty Publications

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

A Composite Narxnn Approach To Photovoltaic Power Forecasting With Integrated Weather Inputs And Uncertainty Quantification, Denisse Urenda Castañeda, Sharmin Abdullah, Jackson Morgan, Honglun Xu, Michael Pokojovy, Tzu-Liang Tseng

Mathematics & Statistics Faculty Publications

Solar photovoltaics (PV) are a major source of sustainable energy. Yet, their power output is highly sensitive to environmental variability, particularly solar irradiance, cloud cover, wind, and temperature. Accurate forecasting of PV power is essential for efficient grid integration and energy planning, especially in applications requiring reliable longer-term forecasting rather than one-step-ahead predictions. This study presents a PV power forecasting approach using Nonlinear Autoregressive models with Exogenous Inputs (NARX), integrating large-scale numerical weather historical data as exogenous variables. Although NARX models effectively capture temporal dependencies, they can become overly dependent on historical power values, reducing responsiveness to real-time weather changes. …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Thermomechanical Modeling Of Transpiration Surfaces For Hypersonic Propulsion, Spencer D. Smith

Graduate Studies Theses and Dissertations 2026

Hypersonic flight vehicles are a promising form of high speed passenger transport and efficient space access architectures. However, they are fundamentally a thermally limited technology. High heat fluxes exceeding 10 MW/m2 and gas temperatures between 3000-4000K can be found both within the engine, and on the outer surface. Current conventional engineering materials are non-viable at these conditions. In order to make these materials viable, active cooling strategies could be utilized. One particularly promising type is transpiration cooling, where a coolant is diffused through a porous surface. Although researched heavily in the 1960s, adoption is still minimal. Part of the reason …

Associations Of Blood Pressure Level And Variability With Cortical Thickness: A Cross-Sectional Analysis From The Maracaibo Aging Study, Romeo De Leon, Shana Garza, Silvia Mejia-Arango, Kristina Vatcheva, Sokratis Charisis, Claudia L. Satizabal, Luis J. Mena, Joseph H. Lee, Joseph D. Terwilliger, Eron Manusov

School of Medicine Publications

Background: Although high blood pressure (BP) level and variability are associated with Alzheimer's Disease (AD), their relationship with cortical thickness in brain regions that are associated with AD is unclear. Furthermore, the role of 24-hour BP has not been examined. We investigated the associations of office and ambulatory BP measures with cortical thickness in brain regions implicated in AD.

Methods: We performed a cross sectional analysis of 304 participants without dementia from a population-based study with office and 24-hour BP and magnetic resonance imaging (MRI) data. We considered cortical thickness values derived from 10 regions throughout the frontal, parietal, and …

Reduced Threat-Related Neural Efficiency: A Possible Biomarker For Pediatric Anxiety Disorders, Julia O. Linke, Reut Naim, Simona P. Haller, Parmis Khosravi, Beck Scheinberg, Meghan E. Byrne, Anita Harrewijn, Ellen Leibenluft, Melissa A. Brotman, Anderson M. Winkler, Daniel Samuel Pine

School of Medicine Publications

Objective: Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of affected youths. To support the search for improved, mechanistically grounded interventions, this study evaluated neural efficiency, defined as similarity in functional connectivity between a threat task and rest, as a potential biomarker. The study evaluated neural efficiency in relation to anxiety diagnosis and treatment response.

Methods: The authors compared 103 youths with an anxiety disorder diagnosis (mean age, 12.5 years [SD=2.91], 62% female) to 103 youths with no psychiatric diagnosis (mean age, 13.4 years …

Lorawan-Based Asset Tracking System Development And Performance Analysis Within Stem-Pbl Framework: A Mixed-Method Study, Yousef Alfarraj, Ghada Abdelhady, Abdelrahman Ahmed, Mahmoud Elaasar, Jair J. Aguilar

Teaching and Learning Faculty Publications

This study examines the integration of project-based learning (PBL) within the integrative STEM framework to advance practical problem-solving skills and investigate students' perceptions of the STEM-PBL approach through developing an IoT-enabled asset tracking system using Long-Range Wide Area Network (LoRaWAN) technology to tackle real-world logistical challenges. Adopting a mixed-methods approach, the study combines an experimental approach utilizing quantitative system performance analysis with qualitative insights gathered through participant interviews with ten graduate students. Results reveal that the LoRaWAN system demonstrated high reliability with only 0.437% downtime, validating the technical feasibility of the educational project. Furthermore, participants perceived the STEM-PBL approach positively, …

Unraveling Posttranslational Modification Complexity: Advances In Quantitative Histone Proteoform Mass Spectrometry, Karl F Poncha, Alyssa T Paparella, Nicolas L Young

Faculty, Staff and Students Publications

Histone proteins and their posttranslational modifications are central to chromatin structure and function. These modifications often occur in combinations, generating a diverse array of histone proteoforms that contribute to the dynamic regulation of chromatin architecture. Advancements in mass spectrometry-based proteomics, particularly top-down and middle-down approaches, have significantly enhanced our ability to characterize these proteoforms and elucidate PTM crosstalk. This review provides an analysis of the epigenetic machinery involved in the addition, recognition, and removal of histone PTMs, emphasizing the complexity introduced by histone variants and combinatorial PTM patterns. We examine the challenges and limitations of traditional antibody-based methods for PTM …