AIFS—A Tool for Biomorphic Fractal Modeling

A considerable class of fractal sets can be represented by using the attractors of Iterated Function Systems (Barnsley, 1988), with affine contractive mappings of a metric space . The modeling capabilities of such systems are heavily limited however. For example, it is not easy to predict the location of the attractor nor its global shape. Then, Iterated Systems are not affinely invariant (affine mappings of the elements of the system do not result in affine image of its attractor). In this paper a new setting, the affine invariant Iterated Function System is described in such a way that it removes the mentioned shortcomings and can be used for shape-predictable modeling of fractal based forms. The stress is put on modeling of biological forms and their atributes such as: continuous deformation of the attractor in desired way (like in growing), branching (plants, vascular or alveolar network), gradual changing of fractal dimension from smooth to space-filling fractals. The last is useful for creating images of tissues in different stages of development, symmetry, gradual transformation from one to another form, etc. The fractal images obtained by AIFS are merely to gain resemblance to bio-forms.     

In situ spectral imaging of marker proteins in gastric cancer with near-infrared and visible quantum dots probes

This study presents the investigation of bioconjugating ability of near-infrared (NIR) CdSeTe/ZnS quantum dots (QDs) (710 nm) and visible CdSe QDs (595 nm) in immunofluorescent staining for cancer biomarkers in gastric cancer tissues probed with the homemade Hadamard transform (HT) spectral imaging microscope and a commercial multispectral imaging system. The results show that imunostaining ability of NIR QDs probes is stronger than that of visible QDs when the two kinds of QDs are simultaneously used to probe the cancer biomarkers such as cytokeratin 20 (CK20) and proliferating cell nuclear antigen (PCNA) in gastric cancer tissues. Moreover, when the two QDs probes are used for immunostaining successively for the same target molecules, staining order has great influences on the final results due to their different conjugating ability to the marker proteins. The results imply that NIR QDs hold more promise for real-time imaging of tumor tissues due to its higher sensitivity and contrast. In addition, the results also demonstrate the potential of Hadamard transform spectral imaging as a useful tool in biomedical analysis and quantitative evaluation for tumor tissues.     

Stereo‐Inversion in the (4R)‐γ‐Decanolactone Synthesis by Saccharomyces cerevisiae: (2E,4S)‐4‐Hydroxydec‐2‐enoic Acid Acts as a Key Intermediate

Methyl (2E,4R)‐4‐hydroxydec‐2‐enoate, methyl (2E,4S)‐4‐hydroxydec‐2‐enoate, and ethyl (±)‐(2E)‐4‐hydroxy[4‐²H]dec‐2‐enoate were chemically synthesized and incubated in the yeast Saccharomyces cerevisiae. Initial C‐chain elongation of these substrates to C₁₂ and, to a lesser extent, C₁₄ fatty acids was observed, followed by γ‐decanolactone formation. Metabolic conversion of methyl (2E,4R)‐4‐hydroxydec‐2‐enoate and methyl (2E,4S)‐4‐hydroxydec‐2‐enoate both led to (4R)‐γ‐decanolactone with >99% ee and 80% ee, respectively. Biotransformation of ethyl (±)‐(2E)‐4‐hydroxy(4‐²H)dec‐2‐enoate yielded (4R)‐γ‐[²H]decanolactone with 61% of the ²H label maintained and in 90% ee indicating a stereoinversion pathway. Electron‐impact mass spectrometry analysis (Fig. 4) of 4‐hydroxydecanoic acid indicated a partial C(4)→C(2) ²H shift. The formation of erythro‐3,4‐dihydroxydecanoic acid and erythro‐3‐hydroxy‐γ‐decanolactone from methyl (2E,4S)‐4‐hydroxydec‐2‐enoate supports a net inversion to (4R)‐γ‐decanolactone via 4‐oxodecanoic acid. As postulated in a previous work, (2E,4S)‐4‐hydroxydec‐2‐enoic acid was shown to be a key intermediate during (4R)‐γ‐decanolactone formation via degradation of (3S,4S)‐dihydroxy fatty acids and precursors by Saccharomyces cerevisiae.     

An exact, linear solution to the problem of imaging through turbulence

We show how, in principle, to solve the ‘blind deconvolution' problem. This is in the context of the problem of imaging through atmospheric turbulence. The approach is digital but not iterative, and requires as input data but two short-exposure intensity images, without the need for reference point sources. By taking the Fourier transform of each image and dividing, a set of linear equations is generated whose unknowns are sampled values of the two random point spread functions that degraded the images. An oversampling by 50% in Fourier space equalizes the number of unknowns and independent equations. With some prior knowledge of spread function support, and in the absence of added noise of image detection, the inverted equations give exact solutions. The two observed images are then inverse filtered to reconstruct the object.     

Stereoscopic video surveillance system for detection of target’s 3D location coordinates and moving trajectories

In this paper, a novel stereoscopic video surveillance system to track a target person and detect its three-dimensional (3D) location coordinates and moving trajectory is implemented by using the pan/tilt-controlled stereo camera system. Stereoscopic video images of a target person are captured with stereo camera system and the target face and its location in the left image plane can be detected by use of YCbCr color model and centroid method. Also the target location in the right image plane can be obtained through correlation between the left face image and the right image by using the binary phase extraction joint transform correlator (BPEJTC) algorithm. With these location data of the target face in the left and right image planes the pan/tilt systems can control the stereo camera to track a target person by positioning the target face at the center of the camera’s FOV and making the focusing points of the right and left camera coincided on the target face. And then, with the resultant pan/tilt angles and geometric parameters of the stereo camera system, the target’s real 3D location coordinates and moving trajectory in the world space can be obtained. From some experiments with 900 frames of stereoscopic video image pairs, it is analyzed that the target’s center location after being tracked is kept to be very low error ratio of 0.62%, 2.04% on the average in the x, y directions of the image plane, respectively. Also, the error ratio between the calculated and measured values of the 3D location coordinates of a target person is found to be very low value of 2.1%, 5.7% on the average in the X and Y directions of the real world space, respectively.