Protein release from water-swellable poly(d,l-lactide-PEG)-b-poly(ϵ-caprolactone) implants

In this study, water-swellable multiblock copolymers composed of semi-crystalline poly(ϵ-caprolactone) [PCL] blocks and amorphous blocks consisting of poly(d,l-lactide) (PDLLA) and poly(ethylene glycol) (PEG) [PDLLA-PEG] were synthesized. The block ratio of these [PDLLA-PEG]-b-[PCL] multiblock copolymers was varied and the degradation of implants prepared of these polymers by hot melt extrusion (HME) was compared with implants prepared of [PCL-PEG]-b-[PCL], a copolymer which has been described previously (Stanković et al., 2014). It was shown that the initial degradation rate of the [PDLLA-PEG]-b-[PCL] multiblock copolymers increased with increasing the content of amorphous [PDLLA-PEG] block and that the degradation rate of these multiblock copolymers was faster than that of the [PCL-PEG]-b-[PCL] multiblock copolymers due to rapid degradation of the [PDLLA-PEG] block. Furthermore, the release of the model proteins lysozyme and bovine serum albumin from polymer implants prepared by HME was studied. It was found that the protein release from [PDLLA-PEG]-b-[PCL] copolymers was incomplete, which is not acceptable for any application of these polymers. Besides, [PCL-PEG]-b-[PCL] copolymers showed slow and continuous release. We hypothesize that the incomplete release is explained by an irreversible interaction between the proteins and polymer degradation products or by entrapment of the protein in the hydrophobic and non-swellable polymer matrix that was left after degradation and loss of the hydrophilic [PDLLA-PEG] blocks from the degrading polymer

Instantaneous frequency estimation and localization

In many applications, a critical feature of a non-stationary signal is provided by its instantaneous frequency (IF), which accounts for the signal spectral variations as a function of time. This chapter presents methods and algorithms for the localization and estimation of the signal IF using time-frequency (t,f) based methods. The topic is covered in seven sections with appropriate internal cross-referencing to this and other chapters. In addition to filter banks and zero-crossings, one of the first conventional approaches for IF estimation used the spectrogram. To account for its major limitations related to accuracy, resolution, window dependence, and sensitivity, improvements were made by introducing iterative methodologies on the estimate provided by the first moment of the spectrogram (Section 10.1). Another approach uses an adaptive algorithm for IF estimation using the peak of suitable TFDs with adaptive window length (Section 10.2). This method was extended to the case of multicomponent signals using high-resolution TFDs such as the modified B-distribution (Section 10.3). When the signals considered have polynomial FM characteristics, both the peak of the polynomial WVD and higher-order ambiguity functions can be used as IF estimators (Section 10.4). In the special case when the signals are subject to random amplitude modulation (or multiplicative noise), IF estimation procedures are described using the peak of the WVD for linear FM signals, and the peak of the PWVD for nonlinear FM signals (Section 10.5). Then, a comparison of multicomponent IF estimation algorithms is provided (Section 10.6); and methods for IF and polynomial phase parameters estimation using linear (t,f) representations are presented (Section 10.7). Next, linear (t,f) methods are described for IF and polynomial phase parameter estimation. Finally, the concept of particle filtering is used for sequential Bayesian estimation of the IF (Section 10.8).Scopu

Characterization of cyclostationary signals and their generalizations

In this section, cyclostationary signals are characterized and their spectral analysis is provided. Links between the considered statistical functions and quadratic time-frequency representations are highlighted. Generalizations of the concept of cyclostationarity are addressed. The considered classes of signals are suitable models in several fields of application including communications, radar/sonar, telemetry, climatology, astronomy, acoustics, mechanics, biology, bioengineering, econometrics, and finance (see Refs. [58–61] and references therein)