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