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1. Development and Characterization of PLGA‐Based Multistage Delivery System for Enhanced Payload Delivery to Targeted Vascular Endothelium

   https://doi.org/10.1002/mabi.202000377  

   Palma‐Chavez, Jorge A.; Fuentes, Kevin; Applegate, Brian E.; Jo, Javier A.; Charoenphol, Phapanin (January 2021, Macromolecular Bioscience)

   Abstract Vascular‐targeted drug delivery remains an attractive platform for therapeutic and diagnostic interventions in human diseases. This work focuses on the development of a poly‐lactic‐co‐glycolic‐acid (PLGA)‐based multistage delivery system (MDS). MDS consists of two stages: a micron‐sized PLGA outer shell and encapsulated drug‐loaded PLGA nanoparticles. Nanoparticles with average diameters of 76, 119, and 193 nm are successfully encapsulated into 3–6 µm MDS. Sustained in vitro release of nanoparticles from MDS is observed for up to 7 days. Both MDS and nanoparticles arebiocompatible with human endothelial cells. Sialyl‐Lewis‐A (sLe^A) is successfully immobilized on the MDS and nanoparticle surfaces to enable specific targeting of inflamed endothelium. Functionalized MDS demonstrates a 2.7‐fold improvement in endothelial binding compared to PLGA nanoparticles from human blood laminar flow. Overall, the presented results demonstrate successful development and characterization of MDS and suggest that MDS can serve as an effective drug carrier, which can enhance the margination of nanoparticles to the targeted vascular wall. 

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2. Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms

   Deng, Chengyuan; Gao, Jie; Lu, Kevin; Luo, Feng; Sun, Hongbin; Xin, Cheng (December 2024, The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS))

   We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utilize the negative eigenvalues of dissimilarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS’s ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods. 

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3. Fast Hausdorff Moment Transforms for Meta Distributions in Wireless Networks

   https://doi.org/10.1109/TWC.2023.3300841  

   Wang, Xinyun; Haenggi, Martin (April 2024, IEEE Transactions on Wireless Communications)

   In the analysis of wireless networks, the standard signal-to-interference (SIR) distribution does not capture the performance at the individual link level. The meta distribution (MD) of the SIR resolves this problem by separating different sources of randomness, such as fading and point process(es). While it allows for a much sharper performance characterization, it can in most cases only be calculated based on the moments of the underlying conditional distribution, i.e., by solving a Hausdorff moment problem. Several methods to reconstruct MDs from the moments have been proposed but a rigorous analysis, comparison of their performance, and practical implementations are missing. In addition, a standard is needed for a consistent and objective comparison. This paper addresses the above-mentioned important shortcomings, introduces a tweaking mapping for adjusting approximations, presents terminology to categorize the quality of approximations, proposes the use of the Fourier- Legendre method, which has not previously been applied to MDs, and provides the achievable lower and upper bounds on the MD given the first 𝑛 moments. Further, to facilitate the use of MDs, we give comprehensive guidance on the selection of the best method to determine MDs, and we offer ready-to-use implementations of the proposed algorithms. This study fills an important gap in the literature by rigorously analyzing the MDs, comparing the performance of different methods, and offering user-friendly implementations for recovering MDs from moments. 

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4. Statistical Significance of Clustering with Multidimensional Scaling

   https://doi.org/10.1080/10618600.2023.2219708  

   Shen, Hui; Bhamidi, Shankar; Liu, Yufeng (January 2024, Journal of Computational and Graphical Statistics)

   Clustering is a fundamental tool for exploratory data analysis. One central problem in clustering is deciding if the clusters discovered by clustering methods are reliable as opposed to being artifacts of natural sampling variation. Statistical significance of clustering (SigClust) is a recently developed cluster evaluation tool for high-dimension, low-sample size data. Despite its successful application to many scientific problems, there are cases where the original SigClust may not work well. Furthermore, for specific applications, researchers may not have access to the original data and only have the dissimilarity matrix. In this case, clustering is still a valuable exploratory tool, but the original SigClust is not applicable. To address these issues, we propose a new SigClust method using multidimensional scaling (MDS). The underlying idea behind MDS-based SigClust is that one can achieve low-dimensional representations of the original data via MDS using only the dissimilarity matrix and then apply SigClust on the low-dimensional MDS space. The proposed MDS-based SigClust can circumvent the challenge of parameter estimation of the original method in high-dimensional spaces while keeping the essential clustering structure in the MDS space. Both simulations and real data applications demonstrate that the proposed method works remarkably well for assessing the statistical significance of clustering. Supplementary materials for this article are available online. 

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5. Pseudomorphic growth of thick Al _0.6 Ga _0.4 N epilayers on AlN substrates

   https://doi.org/10.1063/5.0092937  

   Rathkanthiwar, Shashwat; Houston Dycus, J.; Mita, Seiji; Kirste, Ronny; Tweedie, James; Collazo, Ramon; Sitar, Zlatko (May 2022, Applied Physics Letters)

   We report on the absence of strain relaxation mechanism in Al 0.6 Ga 0.4 N epilayers grown on (0001) AlN substrates for thickness as large as 3.5  μm, three-orders of magnitude beyond the Matthews–Blakeslee critical thickness for the formation of misfit dislocations (MDs). A steady-state compressive stress of 3–4 GPa was observed throughout the AlGaN growth leading to a large lattice bow (a radius of curvature of 0.5 m −1 ) for the thickest sample. Despite the large lattice mismatch-induced strain energy, the epilayers exhibited a smooth and crack-free surface morphology. These results point to the presence of a large barrier for nucleation of MDs in Al-rich AlGaN epilayers. Compositionally graded AlGaN layers were investigated as potential strain relief layers by the intentional introduction of MDs. While the graded layers abetted MD formation, the inadequate length of these MDs correlated with insignificant strain relaxation. This study emphasizes the importance of developing strain management strategies for the implementation of the single-crystal AlN substrate platform for III-nitride deep-UV optoelectronics and power electronics. 

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