Discrete Signal Processing on Graphs: Frequency Analysis

Signals and datasets that arise in physical and engineering applications, as well as social, genetics, biomolecular, and many other domains, are becoming increasingly larger and more complex. In contrast to traditional time and image signals, data in these domains are supported by arbitrary graphs. Signal processing on graphs extends concepts and techniques from traditional signal processing to data indexed by generic graphs. This paper studies the concepts of low and high frequencies on graphs, and low-, high-, and band-pass graph filters. In traditional signal processing, there concepts are easily defined because of a natural frequency ordering that has a physical interpretation. For signals residing on graphs, in general, there is no obvious frequency ordering. We propose a definition of total variation for graph signals that naturally leads to a frequency ordering on graphs and defines low-, high-, and band-pass graph signals and filters. We study the design of graph filters with specified frequency response, and illustrate our approach with applications to sensor malfunction detection and data classification

How Not To Drown in Data:A Guide for Biomaterial Engineers

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell–material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell–material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data

Dark SU(2) Stueckelberg portal

We study the non-abelian $SU(2)_D$ extension of the $U(1)_D$ Stueckelberg portal, which plays a role of the mediator between the Standard Model (SM) and Dark Sector (DS). This portal is specified by the Stueckelberg mechanism for generation of dark gauge boson masses. Proposed $U(1)_D\times SU(2)_D$ Stueckelberg portal has a connection with SM matter fields in analogy with Familon Model. We derive bounds on the couplings of dark portal bosons and SM particles, which govern diagonal and non-diagonal flavor transitions of quarks and leptons.Comment: 12 pages, 4 figures, journal versio

Lepton phenomenology of Stueckelberg portal to dark sector

We propose an extension of the Standard Model (SM) with a UA′(1) gauge-invariant dark sector connected to the SM via a new portal—the Stueckelberg portal, arising in the framework of dark photon A′ mass generation via the Stueckelberg mechanism. This portal opens through the effective dim=5 operators constructed from the covariant term of the auxiliary Stueckelberg scalar field σ providing flavor nondiagonal renormalizable couplings of both σ and A′ to the SM fermions ψ. The Stueckelberg scalar plays a role of Goldstone boson in the generation of mass of the dark photon. Contrary to the conventional kinetic mixing portal, in our scenario, flavor diagonal A′−ψ couplings are not proportional to the fermion charges and are, in general, flavor nondiagonal. These features drastically change the phenomenology of dark photon A′ relaxing or avoiding some previously established experimental constraints. We focus on the phenomenology of the described scenario of the Stueckelberg portal in the lepton sector and analyze the contribution of the dark sector fields A′ to the anomalous magnetic moment of muon (g−2)μ, lepton flavor–violating decays li→lkγ, and μ−e conversion in nuclei. We obtain limits on the model parameters from the existing data on the corresponding observables

Role of senescence marker p16INK4a measured in peripheral blood T-lymphocytes in predicting length of hospital stay after coronary artery bypass surgery in older adults

Adults older than 65 years undergo more than 120,000 coronary artery bypass (CAB) procedures each year in the United States. Chronological age alone, though commonly used in prediction models of outcomes after CAB, does not alone reflect variability in aging process; thus, the risk of complications in older adults. We performed a prospective study to evaluate a relationship between senescence marker p16INK4a expression in peripheral blood T-lymphocytes (p16 levels in PBTLs) with aging and with perioperative outcomes in older CAB patients. We included 55 patients age 55 and older, who underwent CAB in Johns Hopkins Hospital between September 1st, 2010 and March 25th, 2013. Demographic, clinical and laboratory data following outline of the Society of Thoracic Surgeons data collection form was collected, and p16 mRNA levels in PBTLs were measured using Taqman® qRT-PCR. Associations between p16 mRNA levels in PBTLs with length of hospital stay, frailty status, p16 protein levels in the aortic and left internal mammary artery tissue, cerebral oxygen saturation, and augmentation index as a measure of vascular stiffness were measured using regression analyses. Length of hospital stay was the primary outcome of interest, and major organ morbidity, mortality, and discharge to a skilled nursing facility were secondary outcomes. In secondary analysis, we evaluated associations between p16 mRNA levels in PBTLs and interleukin-6 levels using regression analyses. Median age of enrolled patients was 63.5 years (range 56-81 years), they were predominantly male (74.55%), of Caucasian descent (85.45%). Median log2(p16 levels in PBTLs) were 4.71 (range 1.10-6.82). P16 levels in PBTLs were significantly associated with chronological age (mean difference 0.06 for each year increase in age, 95% CI 0.01-0.11) and interleukin 6 levels (mean difference 0.09 for each pg/ml increase in IL-6 levels, 95% CI 0.01-0.18). There were no significant associations with frailty status, augmentation index, cerebral oxygenation and p16 protein levels in blood vessels. Increasing p16 levels in PBTLs did not predict length of stay in the hospital (HR 1.10, 95% CI 0.87-1.40) or intensive care unit (HR 1.02, 95% CI 0.79-1.32). Additional evaluation of p16 levels in PBTLs as predictor of perioperative outcomes is required and should include additional markers of immune system aging as well as different outcomes after CAB in addition to length of hospital stay

Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation

© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Macrophages play a central role in orchestrating immune responses to foreign materials, which are often responsible for the failure of implanted medical devices. Material topography is known to influence macrophage attachment and phenotype, providing opportunities for the rational design of “immune-instructive” topographies to modulate macrophage function and thus foreign body responses to biomaterials. However, no generalizable understanding of the inter-relationship between topography and cell response exists. A high throughput screening approach is therefore utilized to investigate the relationship between topography and human monocyte–derived macrophage attachment and phenotype, using a diverse library of 2176 micropatterns generated by an algorithm. This reveals that micropillars 5–10µm in diameter play a dominant role in driving macrophage attachment compared to the many other topographies screened, an observation that aligns with studies of the interaction of macrophages with particles. Combining the pillar size with the micropillar density is found to be key in modulation of cell phenotype from pro to anti-inflammatory states. Machine learning is used to successfully build a model that correlates cell attachment and phenotype with a selection of descriptors, illustrating that materials can potentially be designed to modulate inflammatory responses for future applications in the fight against foreign body rejection of medical devices

Designed Surface Topographies Control ICAM-1 Expression in Tonsil-Derived Human Stromal Cells

Fibroblastic reticular cells (FRCs), the T-cell zone stromal cell subtype in the lymph nodes, create a scaffold for adhesion and migration of immune cells, thus allowing them to communicate. Although known to be important for the initiation of immune responses, studies about FRCs and their interactions have been impeded because FRCs are limited in availability and lose their function upon culture expansion. To circumvent these limitations, stromal cell precursors can be mechanotranduced to form mature FRCs. Here, we used a library of designed surface topographies to trigger FRC differentiation from tonsil-derived stromal cells (TSCs). Undifferentiated TSCs were seeded on a TopoChip containing 2176 different topographies in culture medium without differentiation factors, then monitored cell morphology and the levels of ICAM-1, a marker of FRC differentiation. We identified 112 and 72 surfaces that upregulated and downregulated, respectively, ICAM-1 expression. By monitoring cell morphology, and expression of the FRC differentiation marker ICAM-1 via image analysis and machine learning, we discovered correlations between ICAM-1 expression, cell shape and design of surface topographies and confirmed our findings by using flow cytometry. Our findings confirmed that TSCs are mechano-responsive cells and identified particular topographies that can be used to improve FRC differentiation protocols

Dynamic adaptation of mesenchymal stem cell physiology upon exposure to surface micropatterns

Human mesenchymal stem (hMSCs) are defined as multi-potent colony-forming cells expressing a specific subset of plasma membrane markers when grown on flat tissue culture polystyrene. However, as soon as hMSCs are used for transplantation, they are exposed to a 3D environment, which can strongly impact cell physiology and influence proliferation, differentiation and metabolism. Strategies to control in vivo hMSC behavior, for instance in stem cell transplantation or cancer treatment, are skewed by the un-physiological flatness of the standard well plates. Even though it is common knowledge that cells behave differently in vitro compared to in vivo, only little is known about the underlying adaptation processes. Here, we used micrometer-scale defined surface topographies as a model to describe the phenotype of hMSCs during this adaptation to their new environment. We used well established techniques to compare hMSCs cultured on flat and topographically enhanced polystyreneand observed dramatically changed cell morphologies accompanied by shrinkage of cytoplasm and nucleus, a decreased overall cellular metabolism, and slower cell cycle progression resulting in a lower proliferation rate in cells exposed to surface topographies. We hypothesized that this reduction in proliferation rate effects their sensitivity to certain cancer drugs, which was confirmed by higher survival rate of hMSCs cultured on topographies exposed to paclitaxel. Thus, micro-topographies can be used as a model system to mimic the natural cell micro-environment, and be a powerful tool to optimize cell treatment in vitro