Abstract: The beginning of 21st century has witnessed new
advancements in the design and use of new materials for biosensing
applications, from nano to macro, protein to tissue. Traditional
analytical methods lack a complete toolset to describe the
complexities introduced by living systems, pathological relations,
discrete hierarchical materials, cross-phase interactions, and
structure-property dependencies. Materiomics – via systematic
molecular dynamics (MD) simulation – can provide structureprocess-
property relations by using a materials science approach
linking mechanisms across scales and enables oriented biosensor
design. With this approach, DNA biosensors can be utilized to detect
disease biomarkers present in individuals’ breath such as acetone for
diabetes. Our wireless sensor array based on single-stranded DNA
(ssDNA)-decorated single-walled carbon nanotubes (SWNT) has
successfully detected trace amount of various chemicals in vapor
differentiated by pattern recognition. Here, we present how MD
simulation can revolutionize the way of design and screening of DNA
aptamers for targeting biomarkers related to oral diseases and oral
health monitoring. It demonstrates great potential to be utilized to
build a library of DNDA sequences for reliable detection of several
biomarkers of one specific disease, and as well provides a new
methodology of creating, designing, and applying of biosensors.
Abstract: Noninvasive diagnostics of diseases via breath
analysis has attracted considerable scientific and clinical interest for
many years and become more and more promising with the rapid
advancements in nanotechnology and biotechnology. The volatile
organic compounds (VOCs) in exhaled breath, which are mainly
blood borne, particularly provide highly valuable information about
individuals’ physiological and pathophysiological conditions.
Additionally, breath analysis is noninvasive, real-time, painless, and
agreeable to patients. We have developed a wireless sensor array
based on single-stranded DNA (ssDNA)-functionalized single-walled
carbon nanotubes (SWNT) for the detection of a number of
physiological indicators in breath. Seven DNA sequences were used
to functionalize SWNT sensors to detect trace amount of methanol,
benzene, dimethyl sulfide, hydrogen sulfide, acetone, and ethanol,
which are indicators of heavy smoking, excessive drinking, and
diseases such as lung cancer, breast cancer, and diabetes. Our test
results indicated that DNA functionalized SWNT sensors exhibit
great selectivity, sensitivity, and repeatability; and different
molecules can be distinguished through pattern recognition enabled
by this sensor array. Furthermore, the experimental sensing results
are consistent with the Molecular Dynamics simulated ssDNAmolecular
target interaction rankings. Thus, the DNA-SWNT sensor
array has great potential to be applied in chemical or biomolecular
detection for the noninvasive diagnostics of diseases and personal
health monitoring.
Abstract: Fabric textures are very common in our daily life.
However, the representation of fabric textures has never been explored
from neuroscience view. Theoretical studies suggest that primary
visual cortex (V1) uses a sparse code to efficiently represent natural
images. However, how the simple cells in V1 encode the artificial
textures is still a mystery. So, here we will take fabric texture as
stimulus to study the response of independent component analysis that
is established to model the receptive field of simple cells in V1. We
choose 140 types of fabrics to get the classical fabric textures as
materials. Experiment results indicate that the receptive fields of
simple cells have obvious selectivity in orientation, frequency and
phase when drifting gratings are used to determine their tuning
properties. Additionally, the distribution of optimal orientation and
frequency shows that the patch size selected from each original fabric
image has a significant effect on the frequency selectivity.
Abstract: A fault detection and identification (FDI) technique is
presented to create a fault tolerant control system (FTC). The fault
detection is achieved by monitoring the position of the light source
using an array of light sensors. When a decision is made about the
presence of a fault an identification process is initiated to locate the
faulty component and reconfigure the controller signals. The signals
provided by the sensors are predictable; therefore the existence of a
fault is easily identified. Identification of the faulty sensor is based on
the dynamics of the frame. The technique is not restricted to a
particular type of controllers and the results show consistency.