"Investment in Innovation, Corporate Governance and Employment: Is Prosperity Sustainable in the United States?"

Over the past decades foreign enterprises have gained competitive advantage over U.S. industrial corporations not by paying lower wages than American companies pay, Lazonick and O'Sullivan argue, but by developing and utilizing broader and deeper skill bases than American companies do. Since the 1970s corporate America has become obsessed with shedding employees to cut costs and with distributing revenue to stockholders. However, the way for it to regain its competitive edge and thus to restore the promise of secure and remunerative employment for its workers is to reform its system of governance. It must reject organizational segmentation and extraction of short-term returns and instead emphasize organizational integration and long-term value creation through financial commitment to investment in the collective and cumulative learning that is the foundation of industrial innovation.

Temporal and spatial differences in microbial composition during the manufacture of a Continental-type cheese

peer-reviewedWe sought to determine if the time, within a production day, that a cheese is manufactured has an influence on the microbial community present within that cheese. To facilitate this, 16S rRNA amplicon sequencing was used to elucidate the microbial community dynamics of brine salted Continental-type cheese in cheeses produced early and late in the production day. Differences in microbial composition of the core and rind of the cheese were also investigated. Throughout ripening, it was apparent that late production day cheeses had a more diverse microbial population than their early day equivalents. Spatial variation between the cheese core and rind was also noted in that cheese rinds were found to initially have a more diverse microbial population but thereafter the opposite was the case. Interestingly, the genera Thermus, Pseudoalteromonas and Bifidobacterium, not routinely associated with a Continental-type cheese produced from pasteurised milk were detected. The significance, if any, of the presence of these genera will require further attention. Ultimately, the use of high throughput sequencing has facilitated a novel and detailed analysis of the temporal and spatial distribution of microbes in this complex cheese system and established that the period during a production cycle at which a cheese is manufactured can influence its microbial composition.This work was funded by the Department of Agriculture, Food and the Marine under the Food Institutional Research Measure through the ‘Cheeseboard 2015’ project. Daniel J. O’Sullivan is in receipt of a Teagasc Walsh Fellowship, Grant Number: 201220

High-throughput DNA sequencing to survey bacterial histidine and tyrosine decarboxylases in raw milk cheeses

peer-reviewedBackground The aim of this study was to employ high-throughput DNA sequencing to assess the incidence of bacteria with biogenic amine (BA; histamine and tyramine) producing potential from among 10 different cheeses varieties. To facilitate this, a diagnostic approach using degenerate PCR primer pairs that were previously designed to amplify segments of the histidine (hdc) and tyrosine (tdc) decarboxylase gene clusters were employed. In contrast to previous studies in which the decarboxylase genes of specific isolates were studied, in this instance amplifications were performed using total metagenomic DNA extracts. Results Amplicons were initially cloned to facilitate Sanger sequencing of individual gene fragments to ensure that a variety of hdc and tdc genes were present. Once this was established, high throughput DNA sequencing of these amplicons was performed to provide a more in-depth analysis of the histamine- and tyramine-producing bacteria present in the cheeses. High-throughput sequencing resulted in generation of a total of 1,563,764 sequencing reads and revealed that Lactobacillus curvatus, Enterococcus faecium and E. faecalis were the dominant species with tyramine producing potential, while Lb. buchneri was found to be the dominant species harbouring histaminogenic potential. Commonly used cheese starter bacteria, including Streptococcus thermophilus and Lb. delbreueckii, were also identified as having biogenic amine producing potential in the cheese studied. Molecular analysis of bacterial communities was then further complemented with HPLC quantification of histamine and tyramine in the sampled cheeses. Conclusions In this study, high-throughput DNA sequencing successfully identified populations capable of amine production in a variety of cheeses. This approach also gave an insight into the broader hdc and tdc complement within the various cheeses. This approach can be used to detect amine producing communities not only in food matrices but also in the production environment itself.This work was funded by the Department of Agriculture, Food and the Marine under the Food Institutional Research Measure through the ‘Cheeseboard 2015’ project. Daniel J. O’Sullivan is in receipt of a Teagasc Walsh Fellowship, Grant Number: 2012205

Robust quantum state engineering through coherent localization in biased-coin quantum walks

We address the performance of a coin-biased quantum walk as a generator for non-classical position states of the walker. We exploit a phenomenon of coherent localisation in the position space --- resulting from the choice of small values of the coin parameter and assisted by post-selection --- to engineer large-size coherent superpositions of distinguishable position states of the walker. The protocol that we design appears to be remarkably robust against both the actual value taken by the coin parameter and strong dephasing-like noise acting on the spatial degree of freedom. We finally illustrate a possible linear-optics implementation of our proposal, suitable for both bulk and integrated-optics platforms.Comment: 7 pages, 7 figure

Nucleic acid-based approaches to investigate microbial-related cheese quality defects

peer-reviewedThe microbial profile of cheese is a primary determinant of cheese quality. Microorganisms can contribute to aroma and taste defects, form biogenic amines, cause gas and secondary fermentation defects, and can contribute to cheese pinking and mineral deposition issues. These defects may be as a result of seasonality and the variability in the composition of the milk supplied, variations in cheese processing parameters, as well as the nature and number of the non-starter microorganisms which come from the milk or other environmental sources. Such defects can be responsible for production and product recall costs and thus represent a significant economic burden for the dairy industry worldwide. Traditional non-molecular approaches are often considered biased and have inherently slow turnaround times. Molecular techniques can provide early and rapid detection of defects that result from the presence of specific spoilage microbes and, ultimately, assist in enhancing cheese quality and reducing costs. Here we review the DNA-based methods that are available to detect/quantify spoilage bacteria, and relevant metabolic pathways in cheeses and, in the process, highlight how these strategies can be employed to improve cheese quality and reduce the associated economic burden on cheese processors.This work was funded by the Department of Agriculture, Food and the Marine under the Food Institutional Research Measure. Daniel J. O’Sullivan is in receipt of a Teagasc Walsh Fellowship, Grant Number:2012205

Editorial

Editorial for New Zealand Studies vol. 9 no. 2 1999

Trending Paths: A New Semantic-level Metric for Comparing Simulated and Real Crowd Data

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise