Application of Poly(hydroxyalkanoate) In Food Packaging: Improvements by Nanotechnology

The environmental impact of plastic usage is of critical concern and too great to repair. A shift toward biodegradable food packaging is one option. The aim of this review paper is the study of the potential of biodegradable materials for food packaging. The main characteristics in relation to food usage can be narrowed down to mass transfer (gas and water vapor), thermal and mechanical properties. Among several kinds of biodegradable polymers, poly(hydroxyalkanoate) is one of the favorable candidates for food packaging due to its physical and mechanical properties, biodegradability, with low permeability for O2, H2O and CO2 without residues of catalysts and water solubility. The main focus of this article is to address poly(hydroxyalkanoate) as a potential candidate for food packaging. The need of applying biobased polymers in food packaging is presented in the introduction of this study. We also describe the most common biopolymers providing a brief overview of classification and application. This is followed by an outline of biopolymer production and a main section in which the properties of poly(hydroxybutyrate)-based nanocomposites of greatest relevance to food packaging are discussed. Furthermore, several approaches for improvement of poly(hydroxybutyrate) properties are described and the role of nanotechnology to improve its mechanical properties is presented. Finally, the article concludes with a summary as well as some possible future trends

Scalable learning of interpretable rules for the dynamic microbiome domain [preprint]

The microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understood by clinicians. Interpretable structure must take into account domainspecific information key to biologists and clinicians including evolutionary relationships (phylogeny) and dynamic behavior of the microbiome. A Bayesian model was previously developed in the field, which uses Markov Chain Monte Carlo inference to learn human interpretable rules for classifying the status of the human host based on microbiome time-series data, but that approach is not scalable to increasingly large microbiome datasets being produced. We present a new fully-differentiable model that also learns human-interpretable rules for the same classification task, but in an end-to-end gradient-descent based framework. We validate the performance of our model on human microbiome data sets and demonstrate our approach has similar predictive performance to the fully Bayesian method, while running orders-of-magnitude faster and moreover learning a larger set of rules, thus providing additional biological insight into the effects of diet and environment on the microbiome

On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor $\delta$-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.Comment: Accepted to the 2023 Proc. IEEE 26th Int. Conf. Inf. Fusio

FDG uptake and walking ability

Includes bibliographical references.Motor impairments of the upper and lower extremities are common symptoms of multiple sclerosis (MS). While some peripheral effects like muscle weakness and loss of balance have been shown to influence these symptoms, central nervous system activity has not been fully elucidated. The purpose of this study was to determine if alterations in glucose uptake were associated with motor impairments in patients with multiple sclerosis. Eight patients with multiple sclerosis (4 men) and 8 sex matched healthy controls performed 15 minutes of treadmill walking at a self-selected pace, during which ≈ 322 MBq of the positron emission tomography glucose analogue [18F]-Fluorodeoxyglucose was injected. Immediately after the cessation of walking, participants underwent positron emission tomography imaging. Patients with MS had lower FDG uptake in ≈ 40% of the brain compared to the healthy controls (pFWE-corr > 0.001, qFDR-corr -0.75, P < 0.032). Within patients with MS only 3 of the 15 regions showed significant correlations: insula (r = -0.74, P = 0.036), hippocampus (r = -0.72, P = 0.045), and calcarine sulcus (r = -0.77, P = 0.026). This data suggests that walking impairments in patients with MS may be due to network wide alterations in glucose metabolism. Understanding how brain activity and metabolism are altered in patients with MS may allow for better measures of disability and disease status within this clinical population.Published with support from the Colorado State University Libraries Open Access Research and Scholarship Fund