Excretory ducts of the left lacrimal system in pig.

<p>Conjunctival view at the level of the eye angle, left scale bar: 2 mm. EB: eyeball; LEA: left eye angle; 1–6: excretory ducts. Right bottom square: higher magnification of an excretory duct, scale bar: 200 µm.</p

3D reconstruction by digitized and computer based processed histological sections of the pig lacrimal gland.

<p>A) Visualization of seven excretory lacrimal ducts (different colors) within the paraffin embedded lacrimal gland (grey). B) Separate exposure of each lacrimal duct in different colors with surrounding gland tissue reconstruction. Only the yellow-marked duct system is displayed alone, without gland tissue reconstruction.</p

Enlargement of blue boxed region in Fig 2 showing a breast duct with some element of tangential sectioning.

<p>H&E histopathology (left). Virtual transillumination H&E image from MPM (right). Both modalities reproduce the duct structure as well as the surrounding collagen. Scale bar: 75 μm.</p

Reference <i>β</i> values expressed in the sRGB color space

<p>Reference <i>β</i> values expressed in the sRGB color space</p

Comparison between histopathology and virtual transillumination H&E image generated by MPM.

<p>a) H&E histopathology image with apocrine metaplasia (green box) and benign breast duct (blue box). b) Corresponding virtual transillumination H&E image. The higher axial resolution of the MPM image better resolves individual collagen fibers as compared to the H&E section, an effect that could be reduced by using a lower NA objective. Due to minor tilting of the histological cutting plane, the left side of the H&E image is slightly deeper than the MPM plane and therefore transects more of the duct on the bottom left. Scale bar: 500 μm.</p

Enlargement of green boxed region in Fig 5 showing a cluster of darker staining nuclei.

<p>a) Virtual transillumination Beer’s law method. b) Additive method with nonlinear transfer function. c) Additive method with linear transfer function. Neither additive method has sufficient dynamic range to render both the nuclei (red arrow) and surrounding collagen fiber (blue arrow) accurately because of the strong spectral overlap between eosin and hematoxylin. Scale bar: 50 um.</p

Schematic diagram of the OpenGL virtual transillumination rendering algorithm.

<p>a) Data flow in the OpenGL algorithm showing pixel data generated by the scan engine and A/D being processed by shaders. Fluorescence data is loaded into GPU RAM as a texture, processed by a shader running on hundreds or thousands of GPU cores and the final result is stored in the frame buffer for display. b) The relationship between vertex and pixel shaders. The vertex shader defines the quad’s position on screen and provides a mapping to the texture coordinates. Gray squares c₁ to c₄ show texture coordinate locations in GPU memory, while the associated vertices v₁ to v₄ are shown in blue. Blue dotted arrows show the association of the texture coordinates to the vertices by the vertex shader. The pixel shader performs the computations according to Beer’s law individually for each displayed pixel. The green grid indicates the pixel grid of the final image, while the green dotted arrows show the transform by a vertex shader which is run for each pixel.</p

Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease

<div><p>Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.</p> </div

Media 3: In vivo imaging of the rodent eye with swept source/Fourier domain OCT

Originally published in Biomedical Optics Express on 01 February 2013 (boe-4-2-351

Media 4: In vivo imaging of the rodent eye with swept source/Fourier domain OCT

Originally published in Biomedical Optics Express on 01 February 2013 (boe-4-2-351