Bioinformatics.

The primary purpose of this study was to investigate the effect of acute high-intensity interval exercise (HIIE) vs. continuous moderate-intensity exercise (CME) on serum CTRP9 and brachial FMD responses in obese and normal-weight subjects. Sixteen participants (9 obese and 7 normal-weight) completed HIIE and CME in a randomized fashion. Our results showed a significant time effect for CTRP9 immediately following acute HIIE and CME in both groups. Furthermore, both significant treatment by time and group by time interactions for FMD were observed following both exercise protocols, with greater CME-induced FMD response in obese subjects than normal-weight subjects. Additionally, a positive correlation in percent change (baseline to peak) between CTRP9 and FMD was observed following acute CME. These findings support acute CME for improvement of endothelial function in obesity. Furthermore, the novel results from this study provide a foundation for additional examination of the mechanisms of exercise-mediated CTRP9 on endothelial function.

A bio-inspired robotic underwater vessel was developed to test the effect of
fin morphology on the propulsive performance of caudal fin. The robotic vessel, called The
Bullet Fish, features a cylindrical body with a hemisphere at the forward section and a
conical body at the stern. The vessel uses an oscillating caudal fin for thrust generation.
The robotic vessel was tested in a recirculating flume for seven different caudal fins that
range different bio-inspired forms and aspect ratios. The experiments were performed at
four different flow velocities and two flapping frequencies: 0.5 and 1.0 Hz. We found that
for 1 Hz flapping frequency that in general as the aspect-ratio decreases both thrust
production tends and power decrease resulting in a better propulsive efficiency for aspect
ratios between 0.9 and 1.0. A less uniform trend was found for 0.5 Hz, where our data
suggest multiple efficiency peaks. Additional experiments on the robotic model could help
understand the propulsion aquatic locomotion and help the design of bio-inspired
underwater vehicles.

Modern cancerous tumor diagnostics is nearly impossible without invasive
methods, such as biopsy, that may require involved surgical procedures. In recent years
some work has been done to develop alternative non-invasive methods of medical
diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection,
has been analyzed using statistical models, including Rayleigh, Rice, Nakagami,
and K statistical distributions. The homodyned-K (H-K) distribution has been found to be
a good statistical tool to analyze the envelope and/or the intensity of backscattered signal
in ultrasound tissue characterization. However, its use has usually been limited due to the
fact that its probability density function (PDF) is not available in closed-form. In this work
we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution
that has a closed-form, for the ultrasound tissue characterization applications. More
specifically, we show that some tissue conditions that cause the backscattered signal to
have low effective density values, can be successfully modeled by the I-K PDF. We
introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional
tools for characterization of ultrasonic breast tissue images. The entropy may be used as a
goodness of fit measure that allows to select a better-fitting statistical model for a specific
data set. In addition, the values of the entropies as well as the values of the statistical
distribution parameters, allow for more accurate classification of tumors.

SRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and analyzed both the short term (12 hours) and long term (48 hours) effects of SRSF1 expression in a human cell line. Furthermore, we analyzed and compared the effect of the expression of a naturally occurring deletion mutant of SRSF1 (RRM12) to the full-¬‐length protein. Our analysis reveals that shortly after SRSF1
is over-¬‐expressed the transcription of several histone coding genes is down-¬‐regulated, allowing for a more relaxed chromatin state and efficient transcription by RNA Polymerase II. This effect is reversed at 48 hours. At the same time key genes for the immune pathways are activated, more notably Tumor Necrosis Factor-¬‐Alpha (TNF-¬‐α), suggesting a role for SRSF1 in T cell functions.

Developments in advanced technologies, such as DNA microarrays, have generated
tremendous amounts of data available to researchers in the field of bioinformatics.
These state-of-the-art technologies present not only unprecedented opportunities to
study biological phenomena of interest, but significant challenges in terms of processing
the data. Furthermore, these datasets inherently exhibit a number of challenging
characteristics, such as class imbalance, high dimensionality, small dataset size, noisy
data, and complexity of data in terms of hard to distinguish decision boundaries
between classes within the data.
In recognition of the aforementioned challenges, this dissertation utilizes a variety
of machine-learning and data-mining techniques, such as ensemble classification
algorithms in conjunction with data sampling and feature selection techniques to alleviate
these problems, while improving the classification results of models built on
these datasets. However, in building classification models researchers and practitioners
encounter the challenge that there is not a single classifier that performs relatively
well in all cases. Thus, numerous classification approaches, such as ensemble learning
methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple
classification models and then combines their decisions into a single final result.
Ensemble learning often performs better than single-base classifiers in performing
classification tasks.
This dissertation conducts thorough empirical research by implementing a series
of case studies to evaluate how ensemble learning techniques can be utilized to
enhance overall classification performance, as well as improve the generalization ability
of ensemble models. This dissertation investigates ensemble learning techniques
of the boosting, bagging, and random forest algorithms, and proposes a number of
modifications to the existing ensemble techniques in order to improve further the
classification results. This dissertation examines the effectiveness of ensemble learning
techniques on accounting for challenging characteristics of class imbalance and
difficult-to-learn class decision boundaries. Next, it looks into ensemble methods
that are relatively tolerant to class noise, and not only can account for the problem
of class noise, but improves classification performance. This dissertation also examines
the joint effects of data sampling along with ensemble techniques on whether
sampling techniques can further improve classification performance of built ensemble
models.