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Fences On The Epistemological Prairie: A Settler Colonial Approach To “Religion And Science”, Lisa L. Stenmark Jan 2025

Fences On The Epistemological Prairie: A Settler Colonial Approach To “Religion And Science”, Lisa L. Stenmark

Faculty Research, Scholarly, and Creative Activity

Building on the idea of religion and science as conceptual maps of intellectual territory, I use a settler colonial analysis as a framework for thinking about decolonizing religion and science in a way that moves away from abstraction and towards action; addressing not just the ideas, but the tools of control—the fences—that impose ideas on the territory itself. Comparing the Wyoming prairie with the epistemological prairie, I describe the maps, fences and other tools and technologies of settler colonialism used to appropriate Indigenous Land and knowledge, eventually turning it into private property. It is in this last step—the creation of …

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Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen Jan 2025

Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen

Faculty Research, Scholarly, and Creative Activity

Transdisciplinary research and research teams are becoming increasingly valued in academic spaces. The potential of transdisciplinary research is that the diversity of thought and experience can create robust research processes from project inception, methodological protocol, data collection, data analysis and reporting output. Yet, transdisciplinary research teams can also bring about complications pertaining to conflicting epistemological perspectives, areas of expertise, and objectives for applied impact. In noting the benefits and downsides of transdisciplinary research, this article aims to detail how a transdisciplinary research team navigated a qualitative and participatory longitudinal research project. Drawing from a larger community-based participatory research project, the …

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“I Bet Being Trans In The Netherlands Is Like Living In The Future”: Could The Netherlands Serve As An Imperfect “Gold Standard” Comparator For Transgender Youth Research In The United States?, Anna I.R. Van Der Miesen, Sharanya Rao, Abigail L. Fischbach, Alex C. Bakker, Marjolein Van Den Brink, Renata A. Sanders, Caitlin Ryan, Stephen M. Rosenthal, Joep Roeffen, Asaf Orr Esq, Heather Newby, Kathie Moehlig, Richard Mocarski, Kim Horsnell, Thijs De Greeff, Annelou L.C. De Vries, Aisa Burgwal, Owen Wilson, Annika Kooke Jan 2025

“I Bet Being Trans In The Netherlands Is Like Living In The Future”: Could The Netherlands Serve As An Imperfect “Gold Standard” Comparator For Transgender Youth Research In The United States?, Anna I.R. Van Der Miesen, Sharanya Rao, Abigail L. Fischbach, Alex C. Bakker, Marjolein Van Den Brink, Renata A. Sanders, Caitlin Ryan, Stephen M. Rosenthal, Joep Roeffen, Asaf Orr Esq, Heather Newby, Kathie Moehlig, Richard Mocarski, Kim Horsnell, Thijs De Greeff, Annelou L.C. De Vries, Aisa Burgwal, Owen Wilson, Annika Kooke

Faculty Research, Scholarly, and Creative Activity

Background: In available comparative studies, Dutch transgender youth have consistently shown better psychosocial well-being compared to those in other countries. Therefore, the Netherlands (N.L.) could be considered a gold-standard—though imperfect—comparator for transgender youth research. The United States (U.S.) is heterogeneous in its policies and practices impacting transgender youth, and policies are in flux. This study examines similarities and differences between U.S. and Dutch policies, practices, and contexts relevant to the well-being of transgender adolescents. Methods: A three-stage Delphi procedure was conducted with expert panels in the N.L. and U.S. Results: This process identified candidate mechanisms driving differences in outcomes for …

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Study Finds Pelvic Floor Dysfunction In Women With Multiple Sclerosis Is An Unmet Need Requiring Nursing Action, Nicole Zhang Jan 2025

Study Finds Pelvic Floor Dysfunction In Women With Multiple Sclerosis Is An Unmet Need Requiring Nursing Action, Nicole Zhang

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

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Resilience Practices In Health Science And Medical Libraries During The Early Stages Of The Covid-19 Pandemic, Justin F. Gaynor Jan 2025

Resilience Practices In Health Science And Medical Libraries During The Early Stages Of The Covid-19 Pandemic, Justin F. Gaynor

Faculty Research, Scholarly, and Creative Activity

This paper uses the concept of resilience engineering as an organizing principle to discuss best practices that evolved within health science/medical libraries in the United States during COVID-19 crisis, focusing on the period March–August 2020. Protection of library staff, assistance to medical staff, reducing the circulation of misinformation and public health consumerism all required substantial changes to standard processes. These process changes had to arise in the context of both physical isolation and information overload. Some practices became widespread due to their utility, and these are the focus of this report.

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Exploration Of The Effects Of Tai Chi Practice On Lower Limb Corticomuscular Coherence During Balance-Demanding Virtual Reality Conditions In Older Adults, Yang Hu, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez Jan 2025

Exploration Of The Effects Of Tai Chi Practice On Lower Limb Corticomuscular Coherence During Balance-Demanding Virtual Reality Conditions In Older Adults, Yang Hu, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez

Faculty Research, Scholarly, and Creative Activity

Tai Chi practice has been widely adopted to improve balance and prevent falls in older adults. However, the neural mechanisms underlying the benefits of Tai Chi are difficult to evaluate during traditional balance assessments. The goal of this study was to evaluate the effects of Tai Chi and healthy aging on corticomuscular coherence (CMC) while standing in virtual balance-demanding environments. We recorded neural, muscular, and behavioral data in an immersive virtual reality environment while implementing sensory and mechanical perturbations to introduce high postural threats. Through the acquisition of electroencephalography and electromyography signals, we examined β and γ CMC changes in …

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Rift - Reddit Information Falsity Tagger, Parth Joshi Jan 2025

Rift - Reddit Information Falsity Tagger, Parth Joshi

Master's Projects

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity …

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Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari Jan 2025

Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari

Master's Projects

The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …

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Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse Jan 2025

Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse

Master's Projects

When using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the …

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Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku Jan 2025

Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku

Master's Projects

Coral reefs can be primarily found in tropical and sub-tropical regions of our oceans, providing a thriving habitat for millions of species. Marine sponges, which can be frequently found in coral reefs, play a critical role that contributes to the maintenance of these ecosystems, including the recycling of nutrients through water filtration. However, rising ocean temperatures and acidification due to climate change have resulted in the bleaching and death of coral reefs worldwide. In order to preserve these reefs and the sponges that depend on them, scientists have been performing studies on their biodiversity. This includes collecting numerous images of …

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Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo Jan 2025

Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo

Master's Projects

Missense mutations can impact protein function and structure, yet their effects on protein function are difficult to predict. In this study, I compared two deep learning models, ESM1v and ESM1b, by evaluating their mutation predictions against experimental structural stability data. ESM1v showed a stronger correlation with experimental structural stability scores compared to ESM1b. A sigmoid curve was fitted to explore this relationship further. Over 100,000 mutations were identified where experimental stability differed significantly from model predictions. Many mutations that remained structurally stable experimentally but were predicted as harmful by the ESM models were frequently found at known functional sites. Structural …

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Ribomoe: An Application Of Mixture Of Experts On Artificial Riboswitch Classification, Hainian Audrey Long Jan 2025

Ribomoe: An Application Of Mixture Of Experts On Artificial Riboswitch Classification, Hainian Audrey Long

Master's Projects

Urban water sewage is a potential health concern due to its possibility to spread contagious RNA viruses such as Coxsackievirus B3. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. To address this, this study proposes a novel viral detection method using synthetic riboswitches that bind to the target virus and trigger a reporter gene, amplifying the detection signals. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features. This model used …

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Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula Jan 2025

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

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Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel Jan 2025

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

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Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu Jan 2025

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

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Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy Jan 2025

Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy

Master's Projects

The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …

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Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan Jan 2025

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

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Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula Jan 2025

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

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Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar Jan 2025

Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar

Master's Projects

In this thesis, we present a comparative analysis of the use of transformerbased and Large Language Model (LLM) models for State of Health (SoH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries. With electric cars and renewable energy systems based on batteries at the forefront, the need to predict degradation accurately in order to enhance the performance and reduce maintenance costs has become imperative. Most traditional prediction methods lag the complex and non-linear characteristics of degradation in batteries, and hence the usage of sophisticated methods becomes a necessity. The research employs the CALCE dataset, which includes long-horizon cycling data …

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Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre Jan 2025

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …

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Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula Jan 2025

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

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Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran Jan 2025

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

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Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings, Tianxiang Chen Jan 2025

Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings, Tianxiang Chen

Master's Projects

Recommender systems surround us. They shape what we watch, how we buy, and even what our future might look like next. The Amazon Review Dataset and Movielens, those two datasets will help this project explore how to improve recommender systems through the user’s preferences. Two methods were combined: sequence-based models and graph-based models. Sequence models, such as LSTMs and Transformers, look at the order of user actions to find patterns by their sequence. On the other hand, Graphbased models focus on relationships between users, items, and their attributes. Textual embeddings added depth and context. Both methods offer something special, according …

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Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana Jan 2025

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

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Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder Jan 2025

Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder

Master's Projects

Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …

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Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana Jan 2025

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …

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Maple: Malware Analysis Through Projection Of Low-Dimensional Embeddings, Quang Duy Tran Jan 2025

Maple: Malware Analysis Through Projection Of Low-Dimensional Embeddings, Quang Duy Tran

Master's Projects

Machine learning has become a popular and powerful tool for malware analysis and detection. With the rise in popularity of natural language processing (NLP) techniques, researchers can now extract contextual embeddings from malware opcode sequences, enabling the capability to analyze hidden malware patterns and advanced code obfuscation strategies. However, unlike malware binaries, which can be directly visualized as images, these embeddings exist in high-dimensional spaces, making it difficult to observe their global patterns or spatial structures. In this paper, we propose a framework for visualizing malware embeddings in lower-dimensional space using various dimensionality reduction techniques. Our approach converts malware binaries …

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Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui Jan 2025

Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui

Master's Projects

This work explores securing and optimization of Transformer-based time series forecasting models. We employ several quantization techniques, including quantization-aware training (QAT), and tested the robustness of quantized models by adversarially attacking them. The preliminary results of this work, in our controlled setup, indicate that quantized models outperform the full precision model in terms of robustness against adversarial attacks. They achieved this robustness while showing a very minimal decrease in their forecasting performance.

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Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti Jan 2025

Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti

Master's Projects

In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) …

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Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan Jan 2025

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

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