Search

Analysis of genetic data opens up many opportunities for medical and scientific advances. The use of phenotypic information and polygenic risk scores to analyze genetic data is widespread. Most work on genetic privacy focuses on basic genetic data such as SNP values and specific genotypes. In this paper, we introduce a novel methodology to quantify and prevent privacy risks by focusing on polygenic scores and phenotypic information. Our methodology is based on the tool-supported privacy risk analysis method Privug. We demonstrate the use of Privug to assess privacy risks posed by disclosing a polygenic trait score for bitter taste receptors, encoded by TAS2R38 and TAS2R16, to a person's privacy in regards to their ethnicity. We provide an extensive privacy risks analysis of different programs for genetic data disclosure: taster phenotype, tasting polygenic score, and a polygenic score distorted with noise. Finally, we discuss the privacy/utility trade-offs of the polygenic score.

This submission is from a small group of colleagues from diverse backgrounds and roles in research who have convened around a common theme of data in research. The submitters come from a range of research sector backgrounds and roles. We embrace an inclusive definition of research including M��tauranga M��ori, which assumes governance, leadership, and priority setting in relevant spaces belongs with M��ori.These interests lie in exploring values and principles, addressing policies and practices, and promoting goals and investments across a wide range of communities and institutions within the research sector. As we embark, our vision for the future is somewhat constrained by a high degree of fragmentation across the sector - there is no holistic view of the research data landscape, nor a recognisable body that aspires to establish a vision for this essential aspect of a modern research sector. Aotearoa New Zealand is not advanced in the capability to exploit research data and therefore unable to maximise the value of research data as an asset/taonga, and ensure reuse of data or reproducibility to increase the trust in science. We need a strategic approach to data in research, one which builds the capabilities of communities, institutions, and individuals, and maximises the impacts and outcomes of such an investment for Aotearoa New Zealand as a small self-sufficient nation.

Funder: Charles Lee Powell Foundation; doi: http://dx.doi.org/10.13039/100008872, Unlimited access to a motorway network can, in overloaded conditions, cause a loss of throughput. Ramp metering, by controlling access to the motorway at onramps, can help avoid this loss of throughput. The queues that form at onramps are dependent on the metering rates chosen at the onramps, and these choices affect how the capacities of different motorway sections are shared amongst competing flows. In this paper we perform an analytical study of a fluid, or differential equation, model of a linear network topology with onramp queues. The model allows for adaptive arrivals, in the sense that the rate at which external traffic enters the queue at an onramp can depend on the current perceived delay in that queue. The model also includes a ramp metering policy which uses global onramp queue length information to determine the rate at which traffic enters the motorway from each onramp. This ramp metering policy minimizes the maximum delay over all onramps and produces equal delay times over many onramps. The paper characterizes both the dynamics and the equilibrium behavior of the system under this policy. While we consider an idealized model that leaves out many practical details, an aim of the paper is to develop analytical methods that yield interesting qualitative insights and might be adapted to more general contexts. The paper can be considered as a step in developing an analytical approach towards studying more complex network topologies and incorporating other model features.

Amid unprecedented times caused by COVID-19, healthcare systems all over the world are strained to the limits of, or even beyond, capacity. A similar event is experienced by some healthcare systems regularly, due to for instance seasonal spikes in the number of patients. We model this as a queueing system in heavy traffic (where the arrival rate is approaching the service rate from below) or in overload (where the arrival rate exceeds the service rate). In both cases we assume that customers (patients) may have different priorities and we consider two popular service disciplines: static priorities and accumulating priorities. It has been shown that the latter allows for patients of all classes to be seen in a timely manner as long as the system is stable. We demonstrate however that if accumulating priorities are used in the heavy traffic or overload regime, then all patients, including those with the highest priority, will experience very long waiting times. If on the other hand static priorities are applied, then one can ensure that the highest-priority patients will be seen in a timely manner even in overloaded systems.

We review the theory of loss networks, including recent results on their dynamical behaviour. We give also some new results., 22 pages