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

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 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

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

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

Oligomerization Alters Binding Affinity between Amyloid Beta and a Modulator of Peptide Aggregation

The soluble oligomeric form of the amyloid beta (Aβ) peptide is the major causative agent in the molecular pathogenesis of Alzheimer’s disease (AD). We have previously developed a pyrroline-nitroxyl fluorene compound (SLF) that blocks the toxicity of Aβ. Here we introduce the multiparametric surface plasmon resonance (MP-SPR) approach to quantify SLF binding and its effect on the self-association of the peptide via a label-free, real-time approach. Kinetic analysis of SLF binding to Aβ and measurements of layer thickness alterations inform on the mechanism underlying the ability of SLF to inhibit Aβ toxicity and its progression toward larger oligomeric assemblies. Depending on the oligomeric state of Aβ, distinct binding affinities for SLF are revealed. The Aβ monomer and dimer uniquely possess subnanomolar affinity for SLF via a nonspecific mode of binding. SLF binding is weaker in oligomeric Aβ, which displays an affinity for SLF on the order of 100 μM. To complement these experiments we carried out molecular docking and molecular dynamics simulations to explore how SLF interacts with the Aβ peptide. The MP-SPR results together with in silico modeling provide affinity data for the SLF-Aβ interaction and allow us to develop a new general method for examining protein aggregation

Smart and Fast Blood Counting of Trace Volumes of Body Fluids from Various Mammalian Species Using a Compact, Custom-Built Microscope Cytometer

We report an accurate method to count red blood cells, platelets, and white blood cells, as well as to determine hemoglobin in the blood of humans, horses, dogs, cats, and cows. Red and white blood cell counts can also be performed on human body fluids such as cerebrospinal fluid, synovial fluid, and peritoneal fluid. The approach consists of using a compact, custom-built microscope to record large field-of-view, bright-field, and fluorescence images of samples that are stained with a single dye and using automatic algorithms to count blood cells and detect hemoglobin. The total process takes about 15 min, including 5 min for sample preparation, and 10 min for data collection and analysis. The minimum volume of blood needed for the test is 0.5 μL, which allows for minimally invasive sample collection such as using a finger prick rather than a venous draw. Blood counts were compared to gold-standard automated clinical instruments, with excellent agreement between the two methods as determined by a Bland–Altman analysis. Accuracy of counts on body fluids was consistent with hand counting by a trained clinical lab scientist, where our instrument demonstrated an approximately 100-fold lower limit of detection compared to current automated methods. The combination of a compact, custom-built instrument, simple sample collection and preparation, and automated analysis demonstrates that this approach could benefit global health through use in low-resource settings where central hematology laboratories are not accessible