32 research outputs found
Distribution fit parameters for polystyrene bead data extracted from experimental data versus values provided by the manufacturer.
<p>All values are given in microns.</p
Experimental System and Calibration.
<p>(a) Schematic depiction of the experimental system. O and O' are object and image planes, respectively, while F and F' are the Fourier plane and its image, respectively. (b) Fourier image of a 200 lp/mm dual axis grating placed at O used to generate a pixel-to-angle calibration curve.</p
Scattering analysis of skim and whole fat milk.
<p>(a) and (b) One dimensional cut throughs of scattering data from skim and whole milk, respectively. Black curves are experimental data, and red curves are best fits to theory. (c) predicted particle size distributions as determined from scattering data for skim (solid line) and whole milk (dashed line).</p
Scattering analysis of sphered red blood cells.
<p>(a) Raw scattering data. (b) Portion of a 10× microscope image of the sphered RBCs. (c) One dimensional cut throughs of scattering data. Black curve is experimental data, and red curve is best fit to theory. (d) predicted particle size distributions as determined from scattering data (solid line) and image data (blue area).</p
Scattering analysis of polystyrene sphere suspensions.
<p>(a)–(c) Raw scattering data from 4, 6, and 8 micron particle suspensions, respectively. The green box in (a) shows the size and shape of the area within each image from which curves in (d)–(f) were calculated. (d)–(f) One dimensional cut throughs of scattering data from 4, 6, and 8 micron particle suspensions, respectively. Black curves are experimental data, and red curves are best fits to theory. (g) Expected (black) and predicted (red) particle size distributions (D in the text) as determined from scattering data.</p
Scattering analysis of a suspension of yeast cells.
<p>(a) Raw data. (b) One dimensional cut throughs of scattering data. Black curve is experimental data, and red curve is best fit to theory. (c) predicted particle size distribution as determined from scattering data.</p
Highly Sensitive, Portable Detection System for Multiplex Chemiluminescence Analysis
Chemiluminescence (CL) has emerged as a critical tool
for the sensing
and quantification of various bioanalytes in virtually all clinical
fields. However, the rapid nature of many CL reactions raises challenges
for typical low-cost optical sensors such as cameras to achieve accurate
and sensitive detection. Meanwhile, classic sensors such as photomultiplier
tubes are highly sensitive but lack spatial multiplexing capabilities
and are generally not suited for point-of-care applications outside
a standard laboratory setting. To address this issue, in this paper,
a miniaturized and versatile silicon-photomultiplier-based fiber-integrated
CL device (SFCD) was designed for sensitive multiplex CL detection.
The SFCD comprises a silicon photomultiplier array coupled to an array
of high numerical aperture plastic optical fibers to achieve 16-plex
detection. The optical fibers ensure efficient light collection while
allowing the fixed detector to be mated with diverse sample geometries
(e.g., circular or grid), simply by adjusting the fiber configuration.
In a head-to-head comparison with a lens-based camera system featuring
a cooled detector, the SFCD achieved a 14-fold improved limit of detection
in both direct and enzyme-mediated CL reactions. The SFCD also features
improved compactness and lower cost, as well as faster temporal resolution
compared with camera-based systems while preserving spatial multiplexing
and good environmental robustness. Thus, the SFCD has excellent potential
for point-of-care biosensing applications
Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes
Laser
tweezers Raman spectroscopy enables multiplexed, quantitative
chemical and morphological analysis of individual bionanoparticles
such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition
times per particle, leading to a lack of statistical power in typical
small-sized data sets. The long acquisition times present a bottleneck
not only in measurement time but also in the analytical throughput,
as particle concentration (and thus throughput) must be kept low enough
to avoid swarm measurement. The only effective way to improve this
situation is to reduce the exposure time, which comes at the expense
of increased noise. Here, we present a hybrid principal component
analysis (PCA) denoising method, where a small number (∼30
spectra) of high signal-to-noise ratio (SNR) training data construct
an effective principal component subspace into which low SNR test
data are projected. Simulations and experiments prove the method outperforms
traditional denoising methods such as the wavelet transform or traditional
PCA. On experimental liposome samples, denoising accelerated data
acquisition from 90 to 3 s, with an overall 4.5-fold improvement in
particle throughput. The denoised data retained the ability to accurately
determine complex morphochemical parameters such as lamellarity of
individual nanoliposomes, as confirmed by comparison with cryo-EM
imaging. We therefore show that hybrid PCA denoising is an efficient
and effective tool for denoising spectral data sets with limited chemical
variability and that the RR-NTA technique offers an ideal path for
studying the multidimensional heterogeneity of nanoliposomes and other
micro/nanoscale bioparticles
Media 3: Image reconstruction for structured-illumination microscopy with low signal level
Originally published in Optics Express on 07 April 2014 (oe-22-7-8687
Media 2: Image reconstruction for structured-illumination microscopy with low signal level
Originally published in Optics Express on 07 April 2014 (oe-22-7-8687