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The term ocular rigidity is widely used in clinical ophthalmology. Generally it is assumed as a resistance of the whole eyeball to mechanical deformation and relates to biomechanical properties of the eye and its tissues. Basic principles and formulas for clinical tonometry, tonography and pulsatile ocular blood flow measurements are based on the concept of ocular rigidity. There is evidence for altered ocular rigidity in aging, in several eye diseases and after eye surgery. Unfortunately, there is no consensual view on ocular rigidity: it used to make a quite different sense for different people but still the same name. Foremost there is no clear consent between biomechanical engineers and ophthalmologists on the concept. Moreover ocular rigidity is occasionally characterized using various parameters with their different physical dimensions. In contrast to engineering approach, clinical approach to ocular rigidity claims to characterize the total mechanical response of the eyeball to its deformation without any detailed considerations on eye morphology or material properties of its tissues. Further to the previous chapter this section aims to describe clinical approach to ocular rigidity from the perspective of an engineer in an attempt to straighten out this concept, to show its advantages, disadvantages and various applications.
Interior transmission eigenvalue problems for the Helmholtz equation play an important role in inverse wave scattering. Some distribution properties of those eigenvalues in the complex plane are reviewed. Further, a new scattering model for the interior transmission eigenvalue problem with mixed boundary conditions is described and an efficient algorithm for computing the interior transmission eigenvalues is proposed. Finally, extensive numerical results for a variety of two-dimensional scatterers are presented to show the validity of the proposed scheme.
We present new numerical results for shape optimization problems of interior Neumann eigenvalues. This field is not well understood from a theoretical standpoint. The existence of shape maximizers is not proven beyond the first two eigenvalues, so we study the problem numerically. We describe a method to compute the eigenvalues for a given shape that combines the boundary element method with an algorithm for nonlinear eigenvalues. As numerical optimization requires many such evaluations, we put a focus on the efficiency of the method and the implemented routine. The method is well suited for parallelization. Using the resulting fast routines and a specialized parametrization of the shapes, we found improved maxima for several eigenvalues.
An array of electrically isolated nanoplate field-effect silicon-on-insulator (SOI) capacitors as a new transducer structure for multiparameter (bio-)chemical sensing is presented. The proposed approach allows addressable biasing and electrical readout of multiple nanoplate field-effect capacitive (bio-)chemical sensors on the same SOI chip, as well as differential-mode measurements. The realized sensor chip has been applied for pH and penicillin concentration measurements, electrical monitoring of polyelectrolyte multilayer formation, and the label-free electrical detection of consecutive deoxyribonucleic acid (DNA) hybridization and denaturation events.
The coupling of charged molecules, nanoparticles, and more generally, inorganic/organic nanohybrids with semiconductor field-effect devices based on an electrolyte–insulator–semiconductor (EIS) system represents a very promising strategy for the active tuning of electrochemical properties of these devices and, thus, opening new opportunities for label-free biosensing by the intrinsic charge of molecules. The simplest field-effect sensor is a capacitive EIS sensor, which represents a (bio-)chemically sensitive capacitor. In this chapter, selected examples of recent developments in the field of label-free biosensing using nanomaterial-modified capacitive EIS sensors are summarized. In the first part, we present applications of EIS sensors modified with negatively charged gold nanoparticles for the label-free electrostatic detection of positively charged small proteins and macromolecules, for monitoring the layer-by-layer formation of oppositely charged polyelectrolyte (PE) multilayers as well as for the development of an enzyme-based biomolecular logic gate. In the second part, examples of a label-free detection by means of EIS sensors modified with a positively charged weak PE layer are demonstrated. These include electrical detection of on-chip and in-solution hybridized DNA (deoxyribonucleic acid) as well as an EIS sensor with pH-responsive weak PE/enzyme multilayers for enhanced field-effect biosensing.
This work describes a procedure to yield attenuation maps from MR images which are used for the absorption correction (AC) of brain PET data. Such an approach could be mandatory for future combined PET and MRI scanners, which probably do not include a transmission facility. T1-weighted MR images were segmented into brain tissue, bone, soft tissue, and sinus; attenuation coefficients corresponding to elemental composition and density as well as to 511 keV photon energy were respectively assigned. Attenuation maps containing up to four compartments were created and forward projected into sinograms with attenuation factors which then were used for AC during reconstruction of FDG-PET data. The commonly used AC based on a radioactive (68Ge) transmission scan served as reference. The reconstructed radioactivity values obtained with the MRI-based AC were about 20% lower than those obtained with PET-based AC if the skull was not taken into account. Considering the skull the difference was still about 10%. Our investigations demonstrate the feasibility of a MRI-based AC, but revealed also the necessity of a satisfying delineation of bone thickness which tends to be underestimated in our first approach of T1-weighted MR image segmentation.
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