Data Science Commons^™

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Resource Optimization For Air Mobility Under Emergency Situations, Yongxin (Jack) Liu

Math Department Colloquium Series

This project aims to improve air traffic management in emergencies. We first developed a GRU neural network to forecast weather-related airport capacity constraints using historical data, underscoring the value of real-time data analysis. We then optimized emergency evacuation air travel using Particle Swarm Optimization, demonstrating the ability to quickly aggregate evacuation flight resources cost-effectively. Finally, we provided a hybrid model combining a genetic algorithm with a neural network for evacuation planning, we show that neural network can be integrated accelerate genetic algorithms for efficient and performance assured system optimization.

Assessing Gait Metrics For Early Parkinson's Disease Prediction: A Preliminary Analysis Of Underfit Models, Daniel Salinas, Gerardo Medellin, Katherine Bolado, Tomas Gomez, Kelsey Potter-Baker, Nawaz Khan Abdul Hack, Ramu Vadukapuram

Research Symposium

Background: Parkinson's Disease (PD) is characterized by both motor and non-motor symptoms, and its diagnosis primarily relies on clinical presentation. There is a growing need for diagnostic tools to identify the early signs of PD, particularly the initial motor impairments often manifested as gait abnormalities. Here we seek to present preliminary findings to address this need. Our study focuses on using Machine Learning techniques (ML) to predict the PD clinical stage most efficiently and accurately. Specifically, we have sought to evaluate how spatiotemporal characteristics and other locomotor performance variables obtained on a walkway system can be utilized to identify the …

Deep Learning Model For Personalized Prediction Of Positive Mrsa Culture Using Time-Series Electronic Health Records, Masayuki Nigo, Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie, Degui Zhi

Faculty, Staff and Student Publications

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC

On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo

Theses and Dissertations

The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …

A Staged Framework For Llm-Powered Information Extraction In Government Contracts, Jung H. Yae

Theses and Dissertations

The manual extraction of meaningful insights and conversion of content into structured forms to enhance document processing require substantial resources and are susceptible to errors. Despite numerous applications of various Natural Language Processing (NLP) models to streamline the manual process, challenges persist due to domain-specific data constraints and the deficiency of annotated data. This study attempts to address these challenges by leveraging a Large Language Model (LLM) to analyze government contracts. Through rigorous evaluation, we demonstrate the LLM’s effectiveness in information extraction and mitigating hallucinations, achieving a 87.86% accuracy in metadata extraction.

Spec: A Soft Prompt-Based Calibration On Performance Variability Of Large Language Model In Clinical Notes Summarization, Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also …

Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering

Theses and Dissertations

Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …

Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O

Theses and Dissertations

This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …

Multiple Control Of Azoquinoline Based Molecular Photoswitches, Youming Lv, Hebo Ye, Lei You

Faculty, Staff and Student Publications

Multi-addressable molecular switches with high sophistication are creating intensive interest, but are challenging to control. Herein, we incorporated ring-chain dynamic covalent sites into azoquinoline scaffolds for the construction of multi-responsive and multi-state switching systems. The manipulation of ring-chain equilibrium by acid/base and dynamic covalent reactions with primary/secondary amines allowed the regulation of

Evaluating Sojump.Com As A Tool For Online Behavioral Research In China, Alessandro Del Ponte, Lianjun Li, Lina Ang, Noah Lim, Wei Jie Seow

Political Science Faculty Articles and Research

SoJump.com (wjx.cn; in short: SoJump) is a survey company that allows researchers to build and deploy inexpensive online surveys in China. Here we evaluate SoJump’s data quality and similarity to the national benchmark. In the first study, we compare SoJump’s performance in China to MTurk’s performance against national benchmarks in the United States and India. In the second study, we compare three Chinese platforms in two-wave panel studies. We conducted the panels on SoJump, Credamo (SoJump’s major competitor), and Cint (national benchmark). We included attention and comprehension checks, economic games, cognitive tasks, and a framing experiment. We find that SoJump’s …

Anomaly Detection On Small Wind Turbine Blades Using Deep Learning Algorithms, Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz, Todd K. Moon, Mohammad A. S. Masoum

Electrical and Computer Engineering Faculty Publications

Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and …

Hillside Agricultural Machinery And Agricultural Intelligence Driven By New Technologies, Hong Qiao, Yanfeng Lyu, Enhao Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

Hilly and mountainous areas play a crucial role in China’s agricultural production. However, the low level of comprehensive mechanization for crop cultivation, planting, and harvesting in these regions severely hampers the modernization of agriculture. The complex terrain and diverse cropping patterns in hilly areas necessitate the development of specialized agricultural machinery and robots tailored to these unique landscapes. To advance this process, the Chinese government has introduced a series of policies in recent years to support the research and development of agricultural machinery for hilly regions, providing strong backing for the advancement of agricultural technology in these areas. Against this …

The Pathogenicity Of Vancomycin-Resistant Enterococcus Faecalis To Colon Cancer Cells, Li Zhang, Mingxia Deng, Jing Liu, Jiajie Zhang, Fangyu Wang, Wei Yu

Faculty, Staff and Student Publications

BACKGROUND: The aim of this study was to investigate the pathogenicity of vancomycin-resistant Enterococcus faecalis (VREs) to human colon cells in vitro.

METHODS: Three E. faecalis isolates (2 VREs and E. faecalis ATCC 29212) were cocultured with NCM460, HT-29 and HCT116 cells. Changes in cell morphology and bacterial adhesion were assessed at different time points. Interleukin-8 (IL-8) and vascular endothelial growth factor A (VEGFA) expression were measured via RT-qPCR and enzyme-linked immunosorbent assay (ELISA), respectively. Cell migration and human umbilical vein endothelial cells (HUVECs) tube formation assays were used for angiogenesis studies. The activity of PI3K/AKT/mTOR signaling pathway was measured …

The Fuxi Farm: Practice Exploration And Reflection On Integrated Innovation Of Smart Agriculture Technology, Yucheng Zhang, Xiaobo Zhang, Shuqin Gao, Congcong Zheng, Jingyao Zhang, Ya Wen, Lujun Li, Zhuo Wang, Tie Li, Honglong Zhao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Smart agriculture plays a pivotal role in enhancing food security by optimizing resource use and improving crop yields through the application of AI technologies. The establishment of smart farms is critical to rapidly integrating AI into agricultural production, offering platforms for both the deployment and testing of AI solutions and equipment. This paper examines the development of smart farms, with a focus on practices from developed countries, and assesses the current status of smart farming in China. We then focus on exploring the content, pathways, and practices for constructing a smart agricultural production system in China through the establishment of …

Accelerate Innovation Of Forage Intelligent Breeding Technology: Reflection And Suggestions, Haichun Jing, Weijuan Hu, Jingbo Jin, Jingyu Zhang, Yao Zhou, Yue Gong, Gang Yao, Lei Wang, Kang Chong

Bulletin of Chinese Academy of Sciences (Chinese Version)

The world is facing unprecedented changes today, with continuous population growth intensifying the pressure on food demand. The shift towards diversified dietary structures has increased the demand for feed grains, while climate change further threatens China’s food security by affecting agricultural productivity and carbon sequestration economy. At present, China has entered a new era of food supply and feed. As the food supply for animals, forage is the core component of feed, and its industrial development has profound strategic significance for ensuring national food security. This study analyzes the current situation of China’s forage seed industry, summarizes the cutting-edge achievements …

Rapid Soil Detection Technology Aids In Assessing Soil Nutrients In China, Wei Wu, Xiaoyong Liao, Xiaopeng Li, Yuntao Wu, Huixian Lu, Yucheng Zhang, Jiabao Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Soil quality is the core issue for ensuring China’s food security, and soil health is the top priority of soil quality. Comprehensive, rapid and accurate acquisition of soil background data is the prerequisite for achieving homogeneous soil management and formulating balanced fertilization strategies, which will play a key role in improving non-point source pollution caused by unreasonable fertilization and achieving balanced grain yield increase. This study reviews rapid soil testing methods both domestically and internationally. Based on previous research, it proposes a technical approach to rapidly detect soil nutrient concentrations by calculating the gamma quanta released during the decay of …

Artificial Intelligence Innovation For Smart Plant Factory To Diversify Its Big Food Production Functions, Huaqin Gong, Haichun Jing, Xin Tan, Xianhui Wang, Yucheng Zhang, Rongcheng Lin, Mingyu Yang, Shuang Lin, Hualing Xie, Yanping Yang, Shitang Ye, Peng Li, Tingyun Kuang

Bulletin of Chinese Academy of Sciences (Chinese Version)

China’s food security is facing more and more severe and complex challenges, such as tight balance between food demand and supply, the upgrading of consumption structure, and resource constraints. The plant factory is an advanced scenario in agricultural production, a diversified food production system that gets rid of the constraints of “ask for food from the mother nature” and respond to disasters and wars, and is oriented to space. Nevertheless, the high cost greatly limits the application and development of plant factories. The rapid development of artificial intelligence will bring new opportunities for the innovative development and expansion of plant …

Data And Intelligent Driven Space Science Experimental Research: New Exploration Under Ai4s Paradigm, Shengyang Li, Kang Liu, Yunfei Liu, Chufan Lai

Bulletin of Chinese Academy of Sciences (Chinese Version)

As artificial intelligence (AI) technology continues to advance, it is revolutionizing various scientific fields, giving rise to a new research paradigm known as AI for Science (AI4S). This study highlights the unique multidisciplinary advantages of AI in space science experiments conducted under microgravity conditions. It provides a comprehensive analysis of AI-driven approaches to multimodal space science experiment data pattern mining, domain knowledge extraction, interdisciplinary knowledge integration, and cognitive intelligence. The study reveals AI’s substantial potential to enhance intelligent scientific research, cognition, and discovery within the realm of space science experiments. The findings suggest that data-driven space science research, as a …

Mortality Outcomes In A Large Population With And Without Covert Cerebrovascular Disease, Úna Clancy, Eric J Puttock, Wansu Chen, William Whiteley, Ellen M Vickery, Lester Y Leung, Patrick H Luetmer, David F Kallmes, Sunyang Fu, Chengyi Zheng, Hongfang Liu, David M Kent

Faculty, Staff and Student Publications

Covert cerebrovascular disease (CCD) is frequently reported on neuroimaging and associates with increased dementia and stroke risk. We aimed to determine how incidentally-discovered CCD during clinical neuroimaging in a large population associates with mortality. We screened CT and MRI reports of adults aged ≥50 in the Kaiser Permanente Southern California health system who underwent neuroimaging for a non-stroke clinical indication from 2009-2019. Natural language processing identified incidental covert brain infarcts (CBI) and/or white matter hyperintensities (WMH), grading WMH as mild/moderate/severe. Models adjusted for age, sex, ethnicity, multimorbidity, vascular risks, depression, exercise, and imaging modality. Of n=241,028, the mean age was …