Digital Commons Network^™

Low Doses Of Bisphenol S Affect Post-Translational Modifications Of Sperm Proteins In Male Mice, Hedvika Řimnáčová, Miriam Štiavnická, Jiří Moravec, Marouane Chemek, Yaroslav Kolinko, Olga García-Álvarez, Peter R. Mouton, Azalia Mariel Carranza Trejo, Tereza Fenclová, Nikola Eretová, Petr Hošek, Pavel Klein, Milena Králíčková, Jaroslav Petr, Jan Nevoral

Computer Science and Engineering Faculty Publications

Background: Bisphenol S (BPS) is increasingly used as a replacement for bisphenol A in the manufacture of products containing polycarbonates and epoxy resins. However, further studies of BPS exposure are needed for the assessment of health risks to humans. In this study we assessed the potential harmfulness of low-dose BPS on reproduction in male mice.

Methods: To simulate human exposure under experimental conditions, 8-week-old outbred ICR male mice received 8 weeks of drinking water containing a broad range of BPS doses [0.001, 1.0, or 100 µg/kg body weight (bw)/day, BPS1-3] or vehicle control. Mice were sacrificed and testicular tissue taken …

Delta Radiomic Features Improve Prediction For Lung Cancer Incidence: A Nested Case–Control Analysis Of The National Lung Screening Trial, Dmitry Cherezov, Samuel H. Hawkins, Dmitry B. Goldgof, Lawrence O. Hall, Ying Liu, Qian Li, Yoganand Balagurunathan, Robert J. Gillies, Matthew B. Schabath

Computer Science and Engineering Faculty Publications

Background: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6‐16 mm [intermediate], and ≥16 mm [large]).

Methods: We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow‐up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow‐up screen (T2) and for 307 nodule‐positive controls that had three consecutive positive screens not diagnosed as lung cancer. The …

Automatic Infants’ Pain Assessment By Dynamic Facial Representation: Effects Of Profile View, Gestational Age, Gender, And Race, Ruicong Zhi, Ghada Z. D. Zamzmi, Dmitry Goldgof, Terri Ashmeade, Yu Sun

Computer Science and Engineering Faculty Publications

Infants’ early exposure to painful procedures can have negative short and long-term effects on cognitive, neurological, and brain development. However, infants cannot express their subjective pain experience, as they do not communicate in any language. Facial expression is the most specific pain indicator, which has been effectively employed for automatic pain recognition. In this paper, dynamic pain facial expression representation and fusion scheme for automatic pain assessment in infants is proposed by combining temporal appearance facial features and temporal geometric facial features. We investigate the effects of various factors that influence pain reactivity in infants, such as individual variables of …

Energy-Efficient Multicast Transmission For Underlay Device-To-Device Communications: A Social-Aware Perspective, Fan Jiang, Yao Liu, Chenbi Li, Changyin Sun

Computer Science and Engineering Faculty Publications

In this paper, by utilizing the social relationships among mobile users, we present a framework of energy-efficient cluster formation and resource allocation for multicast D2D transmission. In particular, we first deal with D2D multicast cluster/group formation strategy from both physical distance and social trust level. Then we aim to maximize the overall energy-efficiency of D2D multicast groups through resource allocation and power control scheme, which considers the quality-of-service (QoS) requirements of both cellular user equipment and D2D groups. A heuristic algorithm is proposed to solve above energy-efficiency problem with less complexity. After that, considering the limited battery capacity of mobile …

Predicting Malignant Nodules From Screening Ct Scans, Samuel Hawkins, Hua Wang, Ying Liu, Alberto Garcia, Olya Stringfield, Henry Krewer, Qiang Li, Dmitry Cherezov, Matthew Schabath, Lawrence O. Hall, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Objectives

The aim of this study was to determine whether quantitative analyses (“radiomics”) of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.

Methods

Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.

Results

The best models used 23 stable features in a random forests classifier and could …

Deep Feature Transfer Learning In Combination With Traditional Features Predicts Survival Among Patients With Lung Adenocarcinoma, Rahul Paul, Samuel H. Hawkings, Matthew B. Schabath, Robert J. Gilies, Lawrence O. Hall, Dmitry Goldgof

Computer Science and Engineering Faculty Publications

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained …

Differences In Patient Outcomes Of Prevalence, Interval, And Screen-Detected Lung Cancers In The Ct Arm Of The National Lung Screening Trial, Matthew B. Schabath, Pierre P. Massion, Zachary J. Thompson, Steven A. Eschrich, Yoganand Balagurunathan, Dmitry Goldof, Denise R. Aberle, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Lung cancer screening identifies cancers with heterogeneous behaviors. Some lung cancers will be identified among patients who had prior negative CT screens and upon follow-up scans develop a de novo nodule that was determined to be cancerous. Other lung cancers will be identified among patients who had one or more prior stable positive scans that were not determined to be lung cancer (indeterminate pulmonary nodules), but in follow-up scans was diagnosed with an incidence lung cancer. Using data from the CT arm of the National Lung Screening Trial, this analysis investigated differences in patient characteristics and survival endpoints between prevalence-, …

Cognitive Networking For Next-G Wireless Communications, Qingqi Pei, Pin-Han Ho, Yao Liu, Qinghua Li, Lin Chen

Computer Science and Engineering Faculty Publications

No abstract provided.

Cognitive Networking For Next-G Wireless Communications, Qingqi Pei, Pin-Han Ho, Yao Liu, Qinghua Li, Lin Chen

Computer Science and Engineering Faculty Publications

No abstract provided.

Survey On Fall Detection And Fall Prevention Using Wearable And External Sensors, Yueng Santiago Delahoz, Miguel Angel Labrador

Computer Science and Engineering Faculty Publications

According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and …

Supervised Categorical Principal Component Analysis For Genome-Wide Association Analyses, Meng Lu, Hye-Seung Lee, David Hadley, Jianhua Z. Huang, Xiaoning Qiao

Computer Science and Engineering Faculty Publications

In order to have a better understanding of unexplained heritability for complex diseases in conventional Genome-Wide Association Studies (GWAS), aggregated association analyses based on predefined functional regions, such as genes and pathways, become popular recently as they enable evaluating joint effect of multiple Single-Nucleotide Polymorphisms (SNPs), which helps increase the detection power, especially when investigating genetic variants with weak individual effects. In this paper, we focus on aggregated analysis methods based on the idea of Principal Component Analysis (PCA). The past approaches using PCA mostly make some inherent genotype data and/or risk effect model assumptions, which may hinder the accurate …

Joint Clustering Of Protein Interaction Networks Through Markov Random Walk, Yijie Wang, Xiaoning Qian

Computer Science and Engineering Faculty Publications

Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization …

Architectural Power Estimation Based On Behavior Level Profiling, Srinivas Katkoori, Ranga Vemuri

Computer Science and Engineering Faculty Publications

High level synthesis is the process of generating register transfer (RT) level designs from behavioral specifications. High level synthesis systems have traditionally taken into account such constraints as area, clock period and throughput time. Many high level synthesis systems [1] permit generation of many alternative RT level designs meeting these constraints in a relatively short time. If it is possible to accurately estimate the power consumption of RT level designs, then a low power design from among these alternatives can be selected.In this paper, we present an accurate power estimation technique for register transfer level designs generated by high level …