is a new culprit gene for IPAH ranking second to BMPR2. The rare deleterious mutations in BMP9, which lead to the reduction in BMP9 secretion and impairment in BMP9 function, account for 6.7% of IPAH cases.

We report here the syntheses of galactose-containing polymers via reversible addition-fragmentation chain transfer process. Diblock copolymers with one galactose-containing chain segment and one primary amine-containing or linear glucose-containing chain segment were prepared via chain extension technique. Primary amine pendant groups of the copolymer were further modified with biotinyl-N-hydroxysuccinimide ester. Subsequently, multifunctional glyconanoparticles were prepared and used in the study of biomolecular recognition processes. The biomolecular recognition of the biotin and galactose moiety on the surface of the glyconanoparticles toward avidin and Ricinus communis agglutinin lectin respectively was confirmed using UV-visible spectroscopy and diffractive optics techniques. It was found that both carbohydrate-carbohydrate and carbohydrate-protein interactions increased with increasing divalent salt (Ca 2þ and Mn 2þ ) concentration.

Abstract-In this paper, we propose a distributed multi-object tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized Covariance Intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this problem, we firstly approximate the fused posterior as the unlabeled version of δ-generalized labeled multi-Bernoulli (δ-GLMB) distribution, referred to as generalized multi-Bernoulli (GMB) distribution. Then, to allow the subsequent fusion with another multi-Bernoulli posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.

Abstract-This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yesobject probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including δ-GLMB, marginalized δ-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.