Life Sciences Commons^™

Bone Quality And Fractures In Women With Osteoporosis Treated With Bisphosphonates For 1 To 14 Years, Hartmut H. Malluche, Jin Chen, Florence Lima, Lucas J. Liu, Marie-Claude Monier-Faugere, David A. Pienkowski

Internal Medicine Faculty Publications

Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone-quality changes or low-energy fractures. This cross-sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone-quality parameters, and low-energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone-quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone-quality changes and to low-energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 …

Telomere Roles In Fungal Genome Evolution And Adaptation, Mostafa Rahnama, Baohua Wang, Jane Dostart, Olga Novikova, Daniel Yackzan, Andrew T. Yackzan, Haley Bruss, Maray Baker, Haven Jacob, Xiaofei Zhang, April Lamb, Alex Stewart, Melanie Heist, Joey Hoover, Patrick Calie, Li Chen, Jinze Liu, Mark L. Farman

Plant Pathology Faculty Publications

Telomeres form the ends of linear chromosomes and usually comprise protein complexes that bind to simple repeated sequence motifs that are added to the 3′ ends of DNA by the telomerase reverse transcriptase (TERT). One of the primary functions attributed to telomeres is to solve the “end-replication problem” which, if left unaddressed, would cause gradual, inexorable attrition of sequences from the chromosome ends and, eventually, loss of viability. Telomere-binding proteins also protect the chromosome from 5′ to 3′ exonuclease action, and disguise the chromosome ends from the double-strand break repair machinery whose illegitimate action potentially generates catastrophic chromosome aberrations. Telomeres …

Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei

Mathematics Faculty Publications

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex …

Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan

Mathematics Faculty Publications

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as …

Non-Transgenic Crispr-Mediated Knockout Of Entire Ergot Alkaloid Gene Clusters In Slow-Growing Asexual Polyploid Fungi, Simona Florea, Jolanta Jaromczyk, Christopher L. Schardl

Computer Science Faculty Publications

The Epichloë species of fungi include seed-borne symbionts (endophytes) of cool-season grasses that enhance plant fitness, although some also produce alkaloids that are toxic to livestock. Selected or mutated toxin-free endophytes can be introduced into forage cultivars for improved livestock performance. Long-read genome sequencing revealed clusters of ergot alkaloid biosynthesis (EAS) genes in Epichloë coenophiala strain e19 from tall fescue (Lolium arundinaceum) and Epichloë hybrida Lp1 from perennial ryegrass (Lolium perenne). The two homeologous clusters in E. coenophiala—a triploid hybrid species—were 196 kb (EAS1) and 75 kb (EAS2), and …

Literature Retrieval For Precision Medicine With Neural Matching And Faceted Summarization, Jiho Noh, Ramakanth Kavuluru

Institute for Biomedical Informatics Faculty Publications

Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Timing Of Maximal Weight Reduction Following Bariatric Surgery: A Study In Chinese Patients, Ting Xu, Chen Wang, Hongwei Zhang, Xiaodong Han, Weijie Liu, Junfeng Han, Haoyong Yu, Jin Chen, Pin Zhang, Jianzhong Di

Computer Science Faculty Publications

Introduction: Bariatric surgery is a well-received treatment for obesity with maximal weight loss at 12–36 months postoperatively. We investigated the effect of early bariatric surgery on weight reduction of Chinese patients in accordance with their preoperation characteristics.

Materials and Methods: Altogether, 409 patients with obesity from a prospective cohort in a single bariatric center were enrolled retrospectively and evaluated for up to 4 years. Measurements obtained included surgery type, duration of diabetic condition, besides the usual body mass index data tuple. Weight reduction was expressed as percent total weight loss (%TWL) and percent excess weight loss (%EWL).

Results: RYGB or …

Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin

Sanders-Brown Center on Aging Faculty Publications

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ …

Recent Shrinkage And Fragmentation Of Bluegrass Landscape In Kentucky, Bo Tao, Yanjun Yang, Jia Yang, S. Ray Smith, James F. Fox, Alex C. Ruane, Jinze Liu, Wei Ren

Plant and Soil Sciences Faculty Publications

The Bluegrass Region is an area in north-central Kentucky with unique natural and cultural significance, which possesses some of the most fertile soils in the world. Over recent decades, land use and land cover changes have threatened the protection of the unique natural, scenic, and historic resources in this region. In this study, we applied a fragmentation model and a set of landscape metrics together with the satellite-derived USDA Cropland Data Layer to examine the shrinkage and fragmentation of grassland in the Bluegrass Region, Kentucky during 2008–2018. Our results showed that recent land use change across the Bluegrass Region is …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …

Walking With A Robotic Exoskeleton Does Not Mimic Natural Gait: A Within-Subjects Study, Chad Swank, Sharon Wang-Price, Fan Gao, Sattam Almutairi

Kinesiology and Health Promotion Faculty Publications

Background: Robotic exoskeleton devices enable individuals with lower extremity weakness to stand up and walk over ground with full weight-bearing and reciprocal gait. Limited information is available on how a robotic exoskeleton affects gait characteristics.

Objective: The purpose of this study was to examine whether wearing a robotic exoskeleton affects temporospatial parameters, kinematics, and muscle activity during gait.

Methods: The study was completed by 15 healthy adults (mean age 26.2 [SD 8.3] years; 6 males, 9 females). Each participant performed walking under 2 conditions: with and without wearing a robotic exoskeleton (EKSO). A 10-camera motion analysis system synchronized with 6 …

Discerning Novel Splice Junctions Derived From Rna-Seq Alignment: A Deep Learning Approach, Yi Zhang, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Background: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results: …

Seqothello: Querying Rna-Seq Experiments At Scale, Ye Yu, Jinpeng Liu, Xinan Liu, Yi Zhang, Eamonn Magner, Erik Lehnert, Chen Qian, Jinze Liu

Computer Science Faculty Publications

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Imapsplice: Alleviating Reference Bias Through Personalized Rna-Seq Alignment, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The …

Seed Dormancy-Life Form Profile For 358 Species From The Xishuangbanna Seasonal Tropical Rainforest, Yunnan Province, China Compared To World Database, Qinying Lan, Shouhua Yin, Huiyin He, Yunhong Tan, Qiang Liu, Yongmei Xia, Bin Wen, Carol C. Baskin, Jerry M. Baskin

Biology Faculty Publications

Seed dormancy profiles are available for the major vegetation regions/types on earth. These were constructed using a composite of data from locations within each region. Furthermore, the proportion of species with nondormant (ND) seeds and the five classes of dormancy is available for each life form in each region. Using these data, we asked: will the results be the same if many species from a specific area as opposed to data compiled from many locations are considered? Germination was tested for fresh seeds of 358 species in 95 families from the Xishuangbanna seasonal tropical rainforest (XSTRF): 177 trees, 66 shrubs, …

Auditing Snomed Ct Hierarchical Relations Based On Lexical Features Of Concepts In Non-Lattice Subgraphs, Licong Cui, Olivier Bodenreider, Jay Shi, Guo-Qiang Zhang

Computer Science Faculty Publications

Objective—We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations.

Methods—Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT’s IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor …

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

Remote Sensing Of Forests Using Discrete Return Airborne Lidar, Hamid Hamraz, Marco A. Contreras

Forestry and Natural Resources Faculty Publications

Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests, however, remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. …

Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …

Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.

Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …

Detecting And Accounting For Multiple Sources Of Positional Variance In Peak List Registration Analysis And Spin System Grouping, Andrey Smelter, Eric C. Rouchka, Hunter N. B. Moseley

Molecular and Cellular Biochemistry Faculty Publications

Peak lists derived from nuclear magnetic resonance (NMR) spectra are commonly used as input data for a variety of computer assisted and automated analyses. These include automated protein resonance assignment and protein structure calculation software tools. Prior to these analyses, peak lists must be aligned to each other and sets of related peaks must be grouped based on common chemical shift dimensions. Even when programs can perform peak grouping, they require the user to provide uniform match tolerances or use default values. However, peak grouping is further complicated by multiple sources of variance in peak position limiting the effectiveness of …

Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds, Hamid Hamraz, Marco A. Contreras, Jun Zhang

Computer Science Faculty Publications

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and …

Using The Vehicle Routing Problem To Reduce Field Completion Times With Multiple Machines, Hasan Seyyedhasani, Joseph S. Dvorak

Biosystems and Agricultural Engineering Faculty Publications

The Vehicle Routing Problem (VRP) is a powerful tool used to express many logistics problems, yet unlike other vehicle routing challenges, agricultural field work consists of machine paths that completely cover a field. In this work, the allocation and ordering of field paths among a number of available machines has been transformed into a VRP that enables optimization of completion time for the entire field. A basic heuristic algorithm (a modified form of the common Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed for optimization. Both techniques were evaluated through computer simulations in two fields: a hypothetical basic …

Organelle_Pba, A Pipeline For Assembling Chloroplast And Mitochondrial Genomes From Pacbio Dna Sequencing Data, Aboozar Soorni, David Haak, David Zaitlin, Aureliano Bombarely

Kentucky Tobacco Research and Development Center Faculty Publications

Background: The development of long-read sequencing technologies, such as single-molecule real-time (SMRT) sequencing by PacBio, has produced a revolution in the sequencing of small genomes. Sequencing organelle genomes using PacBio long-read data is a cost effective, straightforward approach. Nevertheless, the availability of simple-to-use software to perform the assembly from raw reads is limited at present.

Results: We present Organelle-PBA, a Perl program designed specifically for the assembly of chloroplast and mitochondrial genomes. For chloroplast genomes, the program selects the chloroplast reads from a whole genome sequencing pool, maps the reads to a reference sequence from a closely related species, and …

Fourth-Generation Fan Assessment Numeration System (Fans) Design And Performance Specifications, Michael P. Sama, George B. Day, Laura M. Pepple, Richard S. Gates

Biosystems and Agricultural Engineering Faculty Publications

The Fan Assessment Numeration System (FANS) is a measurement device for generating ventilation fan performance curves. Three different-sized FANS currently exist for assessing ventilation fans commonly used in poultry and livestock housing systems. All FANS consist of an array of anemometers inside an aluminum shroud that traverse the inlet or outlet of a ventilation fan. The FANS design has been updated several times since its inception and is currently in its fourth-generation (G4). The current design iteration (FANS-G4) is reported in this article with an emphasis on the hardware and software control, data acquisition systems, and operational reliability. Six FANS-G4 …

Fedrr: Fast, Exhaustive Detection Of Redundant Hierarchical Relations For Quality Improvement Of Large Biomedical Ontologies, Guangming Xing, Guo-Qiang Zhang, Licong Cui

Institute for Biomedical Informatics Faculty Publications

Background: Redundant hierarchical relations refer to such patterns as two paths from one concept to another, one with length one (direct) and the other with length greater than one (indirect). Each redundant relation represents a possibly unintended defect that needs to be corrected in the ontology quality assurance process. Detecting and eliminating redundant relations would help improve the results of all methods relying on the relevant ontological systems as knowledge source, such as the computation of semantic distance between concepts and for ontology matching and alignment.

Results: This paper introduces a novel and scalable approach, called FEDRR – Fast, Exhaustive …

A Computational Tool For Estimating Off-Target Application Areas In Agricultural Fields, Rodrigo S. Zandonadi, Joe D. Luck, Timothy S. Stombaugh, Michael P. Sama, Scott A. Shearer

Biosystems and Agricultural Engineering Faculty Publications

A computational method for estimating off-target application areas based on the machine-controlled section width and the field shape was developed and implemented in software with a graphical user interface written in the MatLab environment. The program, which is called the Field Coverage Analysis Tool (FieldCAT), includes three modules: data import, data preparation, and coverage analysis. Nine field boundaries were evaluated to test the software using controlled section widths from 0.5 to 27 m and various swath orientations. The estimated off-target application area from the widest section width varied from 9% to 24% depending on the shape and size of the …

Pc–Based Data Acquisition For A Solid Substrate Cultivation Deep Bed Reactor, Mari S. Chinn, Sue E. Nokes, Richard S. Gates

Biosystems and Agricultural Engineering Faculty Publications

This work describes an instrumentation and data acquisition system designed for a deep bed reactor used to cultivate Trichoderma longibrachiatum on wheat bran. The system allowed on–line measurements of substrate temperature, oxygen concentration within the reactor headspace, relative humidity and temperature of the inlet air, and inlet airflow rates while maintaining aseptic conditions and without disturbing the cultivation process. An error analysis for the instrumentation and data acquisition equipment was completed and provided insight into the reliability of the sensor readings. The collected data provided quantitative information about the reactor system dynamics which can be used to evaluate and apply …

An Aeration Duct Design Model For Flat Grain Storage, Thomas C. Bridges, Douglas G. Overhults, Samuel G. Mcneill, G. M. White

Biosystems and Agricultural Engineering Faculty Publications

Traditionally most grain is stored in circular type bins which provide a convenient means for handling and management. With the excess grain production and government loan programs of the past few years, some producers have used rectangular structures and covered piles to complement their round storages. The recommended management practices used with round bins are still required in those alternative storages and may be more critical in obtaining a quality end product.

One such recommended practice used with round bins is aeration. This practice is used to maintain a uniform temperature in the grain mass, preventing condensation and "hot" spots …