In addition to monosodium urate, calcium pyrophosphate dihydrate, and apatite crystals, oxalate crystals are less often found in synovial fluids in association with acute or chronic arthritis. Oxalate crystal deposition disease is seen in patients with primary hyperoxaluria types 1 and 2 (PH1 and 2) and in patients with end-stage renal disease managed with long-term dialysis. Oxalate crystal deposits are found mainly in kidneys, bone, skin, and vessels, and less often inside the joints. Musculoskeletal and systemic manifestations of oxalate crystal deposition disease may be confused with those observed with the other most common types of crystal deposition diseases. Clinical and radiographic features include calcium oxalate osteopathy, acute and chronic arthropathy with chondrocalcinosis, synovial calcification, and miliary skin calcium oxalate deposits and vascular calcifications that affect mainly the hands and feet. Systemic life-threatening cardiovascular, neurologic, and hematologic manifestations are rare. Genomic DNA studies have identified those genetic defects of PH1 and PH2 that allow a precise early diagnosis. Kidney transplantation has poor outcome as a result of graft oxalosis. Combined liver and kidney transplantation is the treatment of choice in patients with PH1 and advanced renal failure. Pre-emptive isolated liver transplantation is the preferred treatment in patients who develop the disease during infancy with progressive manifestations of oxalosis. These novel findings in the understanding of the molecular and enzymatic aspects of primary hyperoxalurias have provided a more rational basis for the management and prevention of oxalate crystal deposition disease. This information may lead to a better understanding and effective management of other common calcium-containing crystal deposition diseases.

In this paper, a system identification method for continuous fractional-order Hammerstein models is proposed. A block structured nonlinear system constituting a static nonlinear block followed by a fractional-order linear dynamic system is considered. The fractional differential operator is represented through the generalized operational matrix of block pulse functions to reduce computational complexity. A special test signal is developed to isolate the identification of the nonlinear static function from that of the fractional-order linear dynamic system. The merit of the proposed technique is indicated by concurrent identification of the fractional order with linear system coefficients, algebraic representation of the immeasurable nonlinear static function output, and permitting use of non-iterative procedures for identification of the nonlinearity. The efficacy of the proposed method is exhibited through simulation at various signal-to-noise ratios.

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix (HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.

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