Abstract: Cortisol is essential to the regulation of the immune
system and pathological yawning is a symptom of multiple sclerosis
(MS). Electromyography activity (EMG) in the jaw muscles typically
rises when the muscles are moved – extended or flexed; and yawning
has been shown to be highly correlated with cortisol levels in healthy
people as shown in the Thompson Cortisol Hypothesis. It is likely
that these elevated cortisol levels are also seen in people with MS.
The possible link between EMG in the jaw muscles and rises in saliva
cortisol levels during yawning were investigated in a randomized
controlled trial of 60 volunteers aged 18-69 years who were exposed
to conditions that were designed to elicit the yawning response.
Saliva samples were collected at the start and after yawning, or at the
end of the presentation of yawning-provoking stimuli, in the absence
of a yawn, and EMG data was additionally collected during rest and
yawning phases. Hospital Anxiety and Depression Scale, Yawning
Susceptibility Scale, General Health Questionnaire, demographic,
and health details were collected and the following exclusion criteria
were adopted: chronic fatigue, diabetes, fibromyalgia, heart
condition, high blood pressure, hormone replacement therapy,
multiple sclerosis, and stroke. Significant differences were found
between the saliva cortisol samples for the yawners, t (23) = -4.263, p
= 0.000, as compared with the non-yawners between rest and poststimuli,
which was non-significant. There were also significant
differences between yawners and non-yawners for the EMG
potentials with the yawners having higher rest and post-yawning
potentials. Significant evidence was found to support the Thompson
Cortisol Hypothesis suggesting that rises in cortisol levels are
associated with the yawning response. Further research is underway
to explore the use of cortisol as a potential diagnostic tool as an assist
to the early diagnosis of symptoms related to neurological disorders.
Bournemouth University Research & Ethics approval granted:
JC28/1/13-KA6/9/13. Professional code of conduct, confidentiality,
and safety issues have been addressed and approved in the Ethics
submission. Trials identification number: ISRCTN61942768.
http://www.controlled-trials.com/isrctn/
Abstract: Long Distance Truck Drivers (LDTDs) have been
found to be a high risk group in the spread of HIV/AIDS globally;
perhaps, due to their high Sexual Risk Behaviours (SRBs).
Interventions for reducing SRBs in trucking population have not been
fully exploited. A quasi-experimental control group pretest-posttest
design was used to assess the efficacy of psycho-education and
behavioural skills training in reducing SRBs among LDTDs. Sixteen
drivers rivers were randomly assigned into either experimental or
control groups using balloting technique. Questionnaire was used as
an instrument for data collection. Repeated measures t-test and
independent t-test were used to test hypotheses. Intervention had
significant effect on the SRBs among LDTDs at post-test (t{7}=
6.01, p
Abstract: Adolescents with Autism Spectrum Disorders (ASD)
often experience social-communication difficulties that negatively
impact their social interactions with typical peers. However, unlike
other age and disability groups, there is little intervention research to
inform best practice for these students. One evidence-based strategy
for younger students with ASD is peer-mediated intervention (PMI).
PMI may be particularly promising for use with adolescents, as peers
are readily available and are natural experts for encouraging authentic
high school conversations. This paper provides a review of previous
research that evaluated the use of PMI to improve the socialcommunication
skills of students with ASD. Specific intervention
features associated with positive student outcomes are identified and
recommendations for future research are provided. Adolescents with
ASD are targeted due the critical importance of social conversation at
the high school level.
Abstract: Object-oriented modeling is spreading in current
simulation of physiological systems through the use of the individual
components of the model and its interconnections to define the
underlying dynamic equations. In this paper we describe the use of
both the SIMSCAPE and MODELICA simulation environments in
the object-oriented modeling of the closed loop cardiovascular
system. The performance of the controlled system was analyzed by
simulation in light of the existing hypothesis and validation tests
previously performed with physiological data. The described
approach represents a valuable tool in the teaching of physiology for
graduate medical students.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: Robotic surgery is used to enhance minimally invasive
surgical procedure. It provides greater degree of freedom for surgical
tools but lacks of haptic feedback system to provide sense of touch to
the surgeon. Surgical robots work on master-slave operation, where
user is a master and robotic arms are the slaves. Current, surgical
robots provide precise control of the surgical tools, but heavily rely
on visual feedback, which sometimes cause damage to the inner
organs. The goal of this research was to design and develop a realtime
Simulink based robotic system to study force feedback
mechanism during instrument-object interaction. Setup includes three
VelmexXSlide assembly (XYZ Stage) for three dimensional
movement, an end effector assembly for forceps, electronic circuit for
four strain gages, two Novint Falcon 3D gaming controllers,
microcontroller board with linear actuators, MATLAB and Simulink
toolboxes. Strain gages were calibrated using Imada Digital Force
Gauge device and tested with a hard-core wire to measure
instrument-object interaction in the range of 0-35N. Designed
Simulink model successfully acquires 3D coordinates from two
Novint Falcon controllers and transfer coordinates to the XYZ stage
and forceps. Simulink model also reads strain gages signal through
10-bit analog to digital converter resolution of a microcontroller
assembly in real time, converts voltage into force and feedback the
output signals to the Novint Falcon controller for force feedback
mechanism. Experimental setup allows user to change forward
kinematics algorithms to achieve the best-desired movement of the
XYZ stage and forceps. This project combines haptic technology
with surgical robot to provide sense of touch to the user controlling
forceps through machine-computer interface.
Abstract: Skin aging is a slow multifactorial process influenced
by both internal as well as external factors. Ultra-violet radiations
(UV), diet, smoking and personal habits are the most common
environmental factors that affect skin aging. Fat contents and fibrous
proteins as collagen and elastin are core internal structural
components. The direct influence of UV on elastin integrity and
health is central on aging of skin especially by time. The deposition
of abnormal elastic material is a major marker in a photo-aged skin.
Searching for compounds that may protect against cutaneous photodamage
is exceedingly valued. Retinoids and alpha hydroxy acids
have been endorsed by some researchers as possible candidates for
protecting and or repairing the effect of UV damaged skin. For
consolidating a better system of anti- and protective effects of such
anti-aging agents, we evaluated the combinatory effects of various
dosages of lactic acid and retinol on the dermal fibroblast’s elastin
levels exposed to UV. The UV exposed cells showed significant
reduction in the elastin levels. A combination of drugs with a higher
concentration of lactic acid (30 -35 mM) and a lower concentration of
retinol (10-15mg/mL) showed to work better in maintaining elastin
concentration in UV exposed cells. We assume this preservation
could be the result of increased tropo-elastin gene expression
stimulated by retinol whereas lactic acid probably repaired the UV
irradiated damage by enhancing the amount and integrity of the
elastin fibers.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.