Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter

Passive multi-target tracking applications require the integration of multiple spatially distributed sensor measurements to distinguish true tracks from ghost tracks. A popular multi-target tracking approach for these applications is the particle filter implementation of Mahler's probability hypothesis density (PHD) filter, which jointly updates the union of all target state space estimates without requiring computationally complex measurement-to-track data association. Although this technique is attractive for implementation in computationally limited platforms, the performance benefits can be significantly overshadowed by inefficient sampling of the target birth particles over the region of interest. We propose a multi-sensor extension of the adaptive birth intensity PHD filter described in (Ristic, 2012) to achieve efficient birth particle sampling driven by online sensor measurements from multiple sensors. The proposed approach is demonstrated using distributed time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements, in which we describe exact techniques for sampling from the target state space conditioned on the observations. Numerical results are presented that demonstrate the increased particle density efficiency of the proposed approach over a uniform birth particle sampler.Comment: 21st International Conference on Information Fusio

Two interdependent mechanisms of antimicrobial activity allow for efficient killing in nylon-3-based polymeric mimics of innate immunity peptides

AbstractNovel synthetic mimics of antimicrobial peptides have been developed to exhibit structural properties and antimicrobial activity similar to those of natural antimicrobial peptides (AMPs) of the innate immune system. These molecules have a number of potential advantages over conventional antibiotics, including reduced bacterial resistance, cost-effective preparation, and customizable designs. In this study, we investigate a family of nylon-3 polymer-based antimicrobials. By combining vesicle dye leakage, bacterial permeation, and bactericidal assays with small-angle X-ray scattering (SAXS), we find that these polymers are capable of two interdependent mechanisms of action: permeation of bacterial membranes and binding to intracellular targets such as DNA, with the latter necessarily dependent on the former. We systemically examine polymer-induced membrane deformation modes across a range of lipid compositions that mimic both bacteria and mammalian cell membranes. The results show that the polymers' ability to generate negative Gaussian curvature (NGC), a topological requirement for membrane permeation and cellular entry, in model Escherichia coli membranes correlates with their ability to permeate membranes without complete membrane disruption and kill E. coli cells. Our findings suggest that these polymers operate with a concentration-dependent mechanism of action: at low concentrations permeation and DNA binding occur without membrane disruption, while at high concentrations complete disruption of the membrane occurs. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova

Metformin Decreases the Incidence of Pancreatic Ductal Adenocarcinoma Promoted by Diet-induced Obesity in the Conditional KrasG12D Mouse Model.

Pancreatic ductal adenocarcinoma (PDAC) is a particularly deadly disease. Chronic conditions, including obesity and type-2 diabetes are risk factors, thus making PDAC amenable to preventive strategies. We aimed to characterize the chemo-preventive effects of metformin, a widely used anti-diabetic drug, on PDAC development using the KrasG12D mouse model subjected to a diet high in fats and calories (HFCD). LSL-KrasG12D/+;p48-Cre (KC) mice were given control diet (CD), HFCD, or HFCD with 5 mg/ml metformin in drinking water for 3 or 9 months. After 3 months, metformin prevented HFCD-induced weight gain, hepatic steatosis, depletion of intact acini, formation of advanced PanIN lesions, and stimulation of ERK and mTORC1 in pancreas. In addition to reversing hepatic and pancreatic histopathology, metformin normalized HFCD-induced hyperinsulinemia and hyperleptinemia among the 9-month cohort. Importantly, the HFCD-increased PDAC incidence was completely abrogated by metformin (p < 0.01). The obesogenic diet also induced a marked increase in the expression of TAZ in pancreas, an effect abrogated by metformin. In conclusion, administration of metformin improved the metabolic profile and eliminated the promoting effects of diet-induced obesity on PDAC formation in KC mice. Given the established safety profile of metformin, our findings have a strong translational potential for novel chemo-preventive strategies for PDAC

The Phoenix Deep Survey: Extremely Red Galaxies and Cluster Candidates

We present the results of a study of a sample of 375 Extremely Red Galaxies (ERGs) in the Phoenix Deep Survey, 273 of which constitute a subsample which is 80% complete to K_s = 18.5 over an area of 1160 arcmin^2. The angular correlation function for ERGs is estimated, and the association of ERGs with faint radio sources explored. We find tentative evidence that ERGs and faint radio sources are associated at z > 0.5. A new overdensity-mapping algorithm has been used to characterize the ERG distribution, and identify a number of cluster candidates, including a likely cluster containing ERGs at 0.5 < z < 1. Our algorithm is also used in an attempt to probe the environments in which faint radio sources and ERGs are associated. We find limited evidence that the I - K_s > 4 criterion is more efficient than R - K_s > 5 at selecting dusty star-forming galaxies, rather than passively evolving ERGs.Comment: 14 emulateapj pages, 15 figures, 1 table, accepted for publication in Astronomical Journal. A version with full resolution figures is available at http://www.physics.usyd.edu.au/~asmith/research/ERGpaper.pd

Composition Operators and Endomorphisms

If $b$ is an inner function, then composition with $b$ induces an endomorphism, $\beta$, of $L^\infty(\mathbb{T})$ that leaves $H^\infty(\mathbb{T})$ invariant. We investigate the structure of the endomorphisms of $B(L^2(\mathbb{T}))$ and $B(H^2(\mathbb{T}))$ that implement $\beta$ through the representations of $L^\infty(\mathbb{T})$ and $H^\infty(\mathbb{T})$ in terms of multiplication operators on $L^2(\mathbb{T})$ and $H^2(\mathbb{T})$. Our analysis, which is based on work of R. Rochberg and J. McDonald, will wind its way through the theory of composition operators on spaces of analytic functions to recent work on Cuntz families of isometries and Hilbert $C^*$-modules

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability

Validity and reliability of seismocardiography for the estimation of cardiorespiratory fitness

BACKGROUND: Low cardiorespiratory fitness (ie, peak oxygen consumption [V.O2peak]) is associated with cardiovascular disease and all-cause mortality and is recognized as an important clinical tool in the assessment of patients. Cardiopulmonary exercise test (CPET) is the gold standard procedure for determination of V.O2peak but has methodological challenges as it is time-consuming and requires specialized equipment and trained professionals. Seismofit is a chest-mounted medical device for estimating V.O2peak at rest using seismocardiography.OBJECTIVE: The purpose of this study was to investigate the validity and reliability of Seismofit V.O2peak estimation in a healthy population.METHODS: On 3 separate days, 20 participants (10 women) underwent estimations of V.O2peak with Seismofit (×2) and Polar Fitness Test (PFT) in randomized order and performed a graded CPET on a cycle ergometer with continuous pulmonary gas exchange measurements.RESULTS: Seismofit V.O2peak showed a significant bias of -3.1 ± 2.4 mL·min-1·kg-1 (mean ± 95% confidence interval) and 95% limits of agreement (LoA) of ±10.8 mL·min-1·kg-1 compared to CPET. The mean absolute percentage error (MAPE) was 12.0%. Seismofit V.O2peak had a coefficient of variation of 4.5% ± 1.3% and an intraclass correlation coefficient of 0.95 between test days and a bias of 0.0 ± 0.4 mL·min-1·kg-1 with 95% LoA of ±1.6 mL·min-1·kg-1 in test-retest. In addition, Seismofit showed a 2.4 mL·min-1·kg-1 smaller difference in 95% LoA than PFT compared to CPET.CONCLUSION: The Seismofit is highly reliable in its estimation of V.O2peak. However, based on the measurement error and MAPE &gt;10%, the Seismofit V.O2peak estimation model needs further improvement to be considered for use in clinical settings.</p

Consequences of a covariant Description of Heavy Ion Reactions at intermediate Energies

Heavy ion collisions at intermediate energies are studied by using a new RQMD code, which is a covariant generalization of the QMD approach. We show that this new implementation is able to produce the same results in the nonrelativistic limit (i.e. 50MeV/nucl.) as the non-covariant QMD. Such a comparison is not available in the literature. At higher energies (i.e. 1.5 GeV/nucl. and 2 GeV/nucl.) RQMD and QMD give different results in respect to the time evolution of the phase space, for example for the directed transverse flow. These differences show that consequences of a covariant description of heavy ion reactions within the framework of RQMD are existing even at intermediate energies.Comment: LaTex-file, 28 pages, 8 figures (available upon request), accepted for publication in Physical Review

Spatiotemporal Control of Opioid Signaling and Behavior

SummaryOptogenetics is now a widely accepted tool for spatiotemporal manipulation of neuronal activity. However, a majority of optogenetic approaches use binary on/off control schemes. Here, we extend the optogenetic toolset by developing a neuromodulatory approach using a rationale-based design to generate a Gi-coupled, optically sensitive, mu-opioid-like receptor, which we term opto-MOR. We demonstrate that opto-MOR engages canonical mu-opioid signaling through inhibition of adenylyl cyclase, activation of MAPK and G protein-gated inward rectifying potassium (GIRK) channels and internalizes with kinetics similar to that of the mu-opioid receptor. To assess in vivo utility, we expressed a Cre-dependent viral opto-MOR in RMTg/VTA GABAergic neurons, which led to a real-time place preference. In contrast, expression of opto-MOR in GABAergic neurons of the ventral pallidum hedonic cold spot led to real-time place aversion. This tool has generalizable application for spatiotemporal control of opioid signaling and, furthermore, can be used broadly for mimicking endogenous neuronal inhibition pathways