Digital Commons Network^™

Capn2 Correlates With Insulin Resistance States In Pcos As Evidenced By Multi-Dataset Analysis, Xi Luo, Yunhua Dong, Haishan Zheng, Xiaoting Zhou, Lujuan Rong, Xiaoping Liu, Yun Bai, Yunxiu Li, Ze Wu

Journal Articles

OBJECTIVE: IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies.

METHODS: We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR …

Dew: A Wavelet Approach Of Rare Sound Event Detection., Sania Gul, Muhammad Salman Khan, Ata Ur-Rehman

Journal Articles

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Phylogenetic Inference From Single-Cell Rna-Seq Data, Xuan Liu, Jason I Griffiths, Isaac Bishara, Jiayi Liu, Andrea H Bild, Jeffrey T Chang

Journal Articles

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then …

Artificial Intelligence Cad Tools In Trauma Imaging: A Scoping Review From The American Society Of Emergency Radiology (Aser) Ai/Ml Expert Panel., David Dreizin, Pedro V Staziaki, Garvit D Khatri, Nicholas M Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary S Delproposto, Maximiliano Klug, J Stephen Spann, Nathan Sarkar, Yunting Fu

Journal Articles

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS: A total of 4052 records were screened, and 233 full-text …

Simple Combination Of Multiple Somatic Variant Callers To Increase Accuracy., Alexander J Trevarton, Jeffrey T Chang, W Fraser Symmans

Journal Articles

Publications comparing variant caller algorithms present discordant results with contradictory rankings. Caller performances are inconsistent and wide ranging, and dependent upon input data, application, parameter settings, and evaluation metric. With no single variant caller emerging as a superior standard, combinations or ensembles of variant callers have appeared in the literature. In this study, a whole genome somatic reference standard was used to derive principles to guide strategies for combining variant calls. Then, manually annotated variants called from the whole exome sequencing of a tumor were used to corroborate these general principles. Finally, we examined the ability of these principles to …

Confidence-Based Laboratory Test Reduction Recommendation Algorithm, Tongtong Huang, Linda T Li, Elmer V Bernstam, Xiaoqian Jiang

Journal Articles

BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

METHODS: We collected internal patient data from a teaching hospital in Houston and external patient data from the MIMIC III database. The study used a conservative definition of unnecessary laboratory tests, which was defined as stable (i.e., stability) and below the lower normal bound (i.e., normality). Considering that machine learning models may yield less reliable results when trained on noisy inputs containing low-quality information, we estimated prediction confidence to assess the …

Confidence-Based Laboratory Test Reduction Recommendation Algorithm, Tongtong Huang, Linda T Li, Elmer V Bernstam, Xiaoqian Jiang

Journal Articles

BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

METHODS: We collected internal patient data from a teaching hospital in Houston and external patient data from the MIMIC III database. The study used a conservative definition of unnecessary laboratory tests, which was defined as stable (i.e., stability) and below the lower normal bound (i.e., normality). Considering that machine learning models may yield less reliable results when trained on noisy inputs containing low-quality information, we estimated prediction confidence to assess the …

Research On Cloud Manufacturing Service Recommendation Based On Graph Neural Network, Minghui Li, Xiaoqiu Shi, Yuqiang Shi, Yong Cai, Xuewen Dong

Journal Articles

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural …

Henry Gas Solubility Optimization Double Machine Learning Classifier For Neurosurgical Patients, Diana T Mosa, Amena Mahmoud, John Zaki, Shaymaa E Sorour, Shaker El-Sappagh, Tamer Abuhmed

Journal Articles

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients' data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients' outcome status into four states. Those are mortality, morbidity, …

Computer-Vision-Based Vibration Tracking Using A Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method, Guang-Yu Nie, Saran Srikanth Bodda, Harleen Kaur Sandhu, Kevin Han, Abhinav Gupta

Journal Articles

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains …

A Novel Qkd Approach To Enhance Iiot Privacy And Computational Knacks, Kranthi Kumar Singamaneni, Gaurav Dhiman, Sapna Juneja, Ghulam Muhammad, Salman A Alqahtani, John Zaki

Journal Articles

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data …

Artificial Intelligence In Cardiovascular Medicine: Historical Overview, Current Status, And Future Directions, Zvonimir Krajcer

The Texas Heart Institute Journal

Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of our daily lives, and cardiovascular medicine is no exception. Here, we provide physicians with an overview of the past, present, and future of artificial intelligence applications in cardiovascular medicine. We describe essential and powerful examples of machine-learning applications in industry and elsewhere. Finally, we discuss the latest technologic advances, as well as the benefits and limitations of artificial intelligence and machine learning in cardiovascular medicine.

A Novel Imputation Approach For Sharing Protected Public Health Data, Elizabeth A Erdman, Leonard D Young, Dana L Bernson, Cici Bauer, Kenneth Chui, Thomas J Stopka

Journal Articles

No abstract provided.

Variant-Specific Inflation Factors For Assessing Population Stratification At The Phenotypic Variance Level, Tamar Sofer, Xiuwen Zheng, Cecelia A Laurie, Stephanie M Gogarten, Jennifer A Brody, Matthew P Conomos, Joshua C Bis, Timothy A Thornton, Adam Szpiro, Jeffrey R O'Connell, Ethan M Lange, Yan Gao, L Adrienne Cupples, Bruce M Psaty, Kenneth M Rice

Journal Articles

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from …

Highly Variable Recessive Lthal Or Nearly Lethal Mutation Rates During Germ-Line Development Of Male Drosophila Melanogaster, Jian-Jun Gao, Xue-Rong Pan, Jing Hu, Li Ma, Jian-Min Wu, Ye-Lin Shao, Sara A Barton, Ronny C Woodruff, Ya-Ping Zhang, Yun-Xin Fu

Journal Articles

Each cell of higher organism adults is derived from a fertilized egg through a series of divisions, during which mutations can occur. Both the rate and timing of mutations can have profound impacts on both the individual and the population, because mutations that occur at early cell divisions will affect more tissues and are more likely to be transferred to the next generation. Using large-scale multigeneration screening experiments for recessive lethal or nearly lethal mutations of Drosophila melanogaster and recently developed statistical analysis, we show for male D. melanogaster that (i) mutation rates (for recessive lethal or nearly lethal) are …

Mechanical Behavior Of Fully Expanded Commercially Available Endovascular Coronary Stents, Josip Tambaca, Suncica Canic, Mate Kosor, R David Fish, David Paniagua

The Texas Heart Institute Journal

The mechanical behavior of endovascular coronary stents influences their therapeutic efficacy. Through computational studies, researchers can analyze device performance and improve designs. We developed a 1-dimensional finite element method, net-based algorithm and used it to analyze the effects of radial loading and bending in commercially available stents. Our computational study included designs modeled on the Express, Cypher, Xience, and Palmaz stents.

We found that stents that did not fully expand were less rigid than the fully expanded stents and, therefore, exhibited larger displacement. Stents with an open-cell design, such as Express-like or Xience-like stents, had a higher bending flexibility. Stents …

Cellular Dynamic Simulator: An Event Driven Molecular Simulation Environment For Cellular Physiology, Michael J Byrne, M Neal Waxham, Yoshihisa Kubota

Journal Articles

In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into …

Perfusion In Rat Brain At 7 T With Arterial Spin Labeling Using Fair-Truefisp And Quipss, Emilio Esparza-Coss, Jarek Wosik, Ponnada A Narayana

Journal Articles

Measurement of perfusion in longitudinal studies allows for the assessment of tissue integrity and the detection of subtle pathologies. In this work, the feasibility of measuring brain perfusion in rats with high spatial resolution using arterial spin labeling is reported. A flow-sensitive alternating recovery sequence, coupled with a balanced gradient fast imaging with steady-state precession readout section was used to minimize ghosting and geometric distortions, while achieving high signal-to-noise ratio. The quantitative imaging of perfusion using a single subtraction method was implemented to address the effects of variable transit delays between the labeling of spins and their arrival at the …

Extended Kalman Filter For Estimation Of Parameters In Nonlinear State-Space Models Of Biochemical Networks, Xiaodian Sun, Li Jin, Momiao Xiong

Journal Articles

It is system dynamics that determines the function of cells, tissues and organisms. to develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of …

Generalized Fuzzy Clustering For Segmentation Of Multi-Spectral Magnetic Resonance Images, Renjie He, Sushmita Datta, Balasrinivasa Rao Sajja, Ponnada A Narayana

Journal Articles

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, …

Differential Dynamic Properties Of Scleroderma Fibroblasts In Response To Perturbation Of Environmental Stimuli, Momiao Xiong, Frank C. Arnett, Xinjian Guo, Hao Xiong, Xiaodong Zhou

Journal Articles

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by …

Searching For The Majority: Algorithms Of Voluntary Control., Jin Fan, Kevin G. Guise, Xun Liu, Hongbin Wang

Journal Articles

Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows) as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5) and content (ratio of left and right pointing arrows within a set) of the inputs to test competing hypotheses …

Differential Dynamic Properties Of Scleroderma Fibroblasts In Response To Perturbation Of Environmental Stimuli, Momiao Xiong, Frank C Arnett, Xinjian Guo, Hao Xiong, Xiaodong Zhou

Journal Articles

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by …

A Day In The Life Of Pubmed: Analysis Of A Typical Day's Query Log., Jorge R Herskovic, Len Y Tanaka, William Hersh, Elmer V Bernstam

Journal Articles

OBJECTIVE: To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines. DESIGN: We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day. MEASUREMENTS: We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies. RESULTS: The size of …

Bifurcation And Singularity Analysis Of A Molecular Network For The Induction Of Long-Term Memory, Hao Song, Paul Smolen, Evyatar Av-Ron, Douglas A. Baxter, John H H. Byrne

Journal Articles

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying …

Using Citation Data To Improve Retrieval From Medline., Elmer V Bernstam, Jorge R Herskovic, Yindalon Aphinyanaphongs, Constantin F Aliferis, Madurai G Sriram, William R Hersh

Journal Articles

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. …

Using Incomplete Citation Data For Medline Results Ranking., Jorge R Herskovic, Elmer V Bernstam

Journal Articles

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts …

A Reduced Model Clarifies The Role Of Feedback Loops And Time Delays In The Drosophila Circadian Oscillator, Paul Smolen, Douglas A. Baxter, John H. Byrne

Journal Articles

Although several detailed models of molecular processes essential for circadian oscillations have been developed, their complexity makes intuitive understanding of the oscillation mechanism difficult. The goal of the present study was to reduce a previously developed, detailed model to a minimal representation of the transcriptional regulation essential for circadian rhythmicity in Drosophila. The reduced model contains only two differential equations, each with time delays. A negative feedback loop is included, in which PER protein represses per transcription by binding the dCLOCK transcription factor. A positive feedback loop is also included, in which dCLOCK indirectly enhances its own formation. The model …