State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements.

This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms.

Pharmacomicrobiomics-Guided Precision Oncology: A New Frontier of P4 (Predictive, Personalized, Preventive, and Participatory) Medicine and Microbiome-Based Therapeutics.

Pharmacomicrobiomics is a rapidly developing field that promises to make significant contributions to predictive, personalized, preventive, and participatory (P4) medicine. This is becoming evident particularly in the field of precision (P4) oncology by taking seriously the crucial role microbiome plays in health and disease. Several studies have already shown that clinicians can harness insights from the microbiome to better predict treatment response, reduce side effects, and improve overall outcomes for cancer patients. Furthermore, pharmacomicrobiomics will undoubtedly play a crucial role in shaping the future of cancer treatment in the era of P4 oncology as we continue to unravel the intricate relationships between the microbiome and cancer. This perspective and innovation analysis discusses the emerging intersection of P4 medicine and P4 oncology, as seen through a lens of pharmacomicrobiomics. A key promise of pharmacomicrobiomics is the development of personalized microbiome-based therapeutics. In all, we suggest that optimizing cancer treatment and prevention by harnessing pharmacomicrobiomics has vast potentials for precision oncology, and personalized medicine using the right drug, at the right dose, for the right patient, and at the right time.

Innovation Analysis on Postgenomic Biomarkers: Glycomics for Chronic Diseases.

Despite decades of investment in biomarker research, we still do not have robust and field-tested biomarkers for many chronic diseases so as to anticipate clinical outcomes and thus move toward personalized medicine. Biomarker innovations have tended to focus on genomics, but next-generation biomarkers from the nascent field of glycomics now offer fresh vistas for innovation in chronic disease biomarkers and systems diagnostics. Glycosylation, regarded as a complex enzymatic process where sugars (glycans) bind to proteins and lipids, affects many human biological functions, including cell signaling, adhesion, and motility. Notably, and contrary to proteins, glycan biosynthesis does not require a template; rather its final structure is catalyzed by a repertoire of enzymes that attach or detach monosaccharides in the glycosylation pathway, making glycomics research more challenging than proteomics or genomics. Yet, given glycans' biological significance, alterations in their processing may be detrimental to human health and also offer insights for preventive medicine and wellness interventions. Therefore, studying glycans' structure and understanding their function and molecular interactions in the emerging field of glycomics are key to unraveling the pathogenesis of various common chronic diseases. This review summarizes the major concepts in glycomics, including glycan release methods, techniques for large-scale glycan analysis, and glycoinformatic tools for data handling and storage. In all, this analysis on glycomics offers strategies to build a robust postgenomic innovation roadmap for glycan-driven biomarkers as the field is anticipated to mature further and gain greater prominence in the near future.