Pollen-based microcapsules such as hollow sporopollenin exine capsules (SECs) have emerged as excellent drug delivery and microencapsulation vehicles. To date, SECs have been extracted primarily from a wide range of natural pollen species possessing largely spherical geometries and uniform surface features. Nonetheless, exploring pollen species with more diverse architectural features could lead to new application possibilities. One promising class of candidates is dandelion pollen grains, which possess architecturally intricate, cage-like microstructures composed of robust sporopollenin biopolymers. Here, we report the successful extraction and macromolecular loading of dandelion SECs. Preservation of SEC morphology and successful removal of proteinaceous materials was evaluated using scanning electron microscopy (SEM), matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry, elemental CHN analysis, dynamic image particle analysis (DIPA) and confocal laser scanning microscopy (CLSM). Among the tested processing schemes, acidolysis using 85% (v/v) phosphoric acid refluxed at 70 °C for 5 hours yielded an optimal balance of intact particle yield, protein removal, and preservation of cage-like microstructure. For proof-of-concept loading, bovine serum albumin (BSA) was encapsulated within the dandelion SECs with high efficiency (32.23 ± 0.33%). Overall, our findings highlight how hollow microcapsules with diverse architectural features can be readily prepared and utilized from plant-based materials.

This paper presents a multi lookup table (LUT) implementation scheme for the 3D distributed memory polynomial (3D-DMP) behavioral model used in Digital Predistortion (DPD) linearization for concurrent dual-band envelope tracking (ET) power amplifiers (PAs). The proposed 3D-Distributed Memory LUTs (3D-DML) architecture is suitable for efficient FPGA implementation. In order to optimize the linearization performance as well as to reduce the number of resources of the 3D-DML model, a new variant of the Orthogonal Matching Pursuit (OMP) algorithm is proposed to properly select the best LUTs. Experimental results show that the proposed strategy reduces the number of LUTs (i.e. the number of coefficients) while meeting the targeted linearity levels.

This paper presents an estimation/adaptation method based on the adaptive principal component analysis (APCA) technique to guarantee the identification of the minimum necessary parameters of a digital predistorter. The proposed estimation/adaptation technique is suitable for online field-programmable gate array or system on chip implementation. By exploiting the orthogonality of the resulting transformed matrix obtained with the APCA technique, it is possible to reduce the number of coefficients to be estimated which, at the same time, has a beneficial regularization effect by preventing illconditioning or overfitting problems. Therefore, this identification/adaptation method enhances the robustness of the parameter estimation and simplifies the adaptation by reducing the number of estimated coefficients. Due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results will show that it is possible to determine the minimum number of parameters to be estimated in order to meet the targeted linearity levels while ensuring a robust well-conditioned identification. Moreover, the results will show how thanks to the orthogonality property of the new basis functions, the coefficients of the digital predistorter can be estimated independently. This allows to tradeoff the digital predistorter adaptation time versus performance and hardware complexity.

This paper presents a technique to estimate the coefficients of a multi look-up table (LUT) digital predistortion (DPD) architecture based on the partial least squares (PLS) regression method. The proposed 3-D distributed memory LUTs (3D-DML) architecture is suitable for efficient FPGA implementation and compensates for the distortion arising in concurrent dual-band envelope tracking (ET) power amplifiers (PAs). On the one hand, a new variant of the Orthogonal Matching Pursuit (OMP) algorithm is proposed to properly select only the best LUTs of the DPD function in the forward path and thus reducing the number of required coefficients. On the other hand, the PLS regression method is proposed to address both the regularization problem of the coefficient estimation and, at the same time, reducing the number of coefficients to be estimated in the DPD feedback identification path. Moreover, by exploiting the orthogonality of the PLS transformed matrix, the computational complexity of the parameters' identification can be significantly simplified. Experimental results will prove how it is possible to reduce the DPD complexity (i.e. the number of coefficients) in both forward and feedback paths while meeting the targeted linearity levels.

Whereas distant migration was not encountered in the liver or lung, 2 transplanted rats developed abnormal foci of growth, that is, tumors, from the external anal sphincter-raising further safety questions. Additional evaluation of these foci seemed benign. Possible explanations include cell trapping, stem cell overgrowth, and/or paracrine factors. The lack of cell migration supports that future investigation of safety parameters could focus locally.

In this paper, a new method for dynamically estimating and updating the coefficients of a digital predistortion (DPD) linearizer is presented. By means of the partial least squares (PLS) algorithm, the basis matrix used in the DPD estimation/adaptation is dynamically updated at every iteration to minimize the linearization error. Moreover, only the minimum necessary DPD coefficients being required to meet a target estimation error are computed. The proposed estimation technique is compared with the standard least squares (LS) estimation solved by using QR decomposition. Experimental results show the similar linearization performance obtained with both estimation methods, but in the case of the dynamic PLS, using less coefficients at every iteration. Finally, the proposed algorithm allows a high level of parallelization, which makes it suitable for FPGA implementation.

This paper presents a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization. The proposed technique is dynamic in the sense of estimating, at every iteration of the coefficient's update, only the minimum necessary parameters according to a criterion based on the residual estimation error. At the first step, the original basis functions defining the DPD in the forward path are orthonormalized for DPD adaptation in the feedback path by means of a precalculated principal components analysis (PCA) transformation. The robustness and reliability of the precalculated PCA transformation (i.e., PCA transformation matrix obtained off-line and only once) is tested and verified. Then, at the second step, a properly modified partial least squares (PLS) method, named dynamic partial least squares (DPLS), is applied to obtain the minimum and most relevant transformed components required for updating the coefficients of the DPD linearizer. The combination of the PCA transformation with the DPLS extraction of components is equivalent to a canonical correlation analysis (CCA) updating solution, which is optimum in the sense of generating components with maximum correlation (instead of maximum covariance as in the case of the DPLS extraction alone). The proposed dynamic extraction technique is evaluated and compared in terms of computational cost and performance with the commonly used QR decomposition approach for solving the least squares (LS) problem. Experimental results show that the proposed method (i.e., combining PCA with DPLS) drastically reduces the amount of DPD coefficients to be estimated while maintaining the same linearization performance.

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