Inhibition of HMG CoA reductase reveals an unexpected role for cholesterol during PGC migration in the mouse

<p>Abstract</p> <p>Background</p> <p>Primordial germ cells (PGCs) are the embryonic precursors of the sperm and eggs. Environmental or genetic defects that alter PGC development can impair fertility or cause formation of germ cell tumors.</p> <p>Results</p> <p>We demonstrate a novel role for cholesterol during germ cell migration in mice. Cholesterol was measured in living tissue dissected from mouse embryos and was found to accumulate within the developing gonads as germ cells migrate to colonize these structures. Cholesterol synthesis was blocked in culture by inhibiting the activity of HMG CoA reductase (HMGCR) resulting in germ cell survival and migration defects. These defects were rescued by co-addition of isoprenoids and cholesterol, but neither compound alone was sufficient. In contrast, loss of the last or penultimate enzyme in cholesterol biosynthesis did not alter PGC numbers or position in vivo. However embryos that lack these enzymes do not exhibit cholesterol defects at the stage at which PGCs are migrating. This demonstrates that during gestation, the cholesterol required for PGC migration can be supplied maternally.</p> <p>Conclusion</p> <p>In the mouse, cholesterol is required for PGC survival and motility. It may act cell-autonomously by regulating clustering of growth factor receptors within PGCs or non cell-autonomously by controlling release of growth factors required for PGC guidance and survival.</p

Pseudonymization risk analysis in distributed systems

In an era of big data, online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data in the form of pseudonymized data sets. It is crucial that such systems are engineered to both protect individual user (data subject) privacy and give back control of personal data to the user. In terms of pseudonymized data this means that unwanted individuals should not be able to deduce sensitive information about the user. However, the plethora of pseudonymization algorithms and tuneable parameters that currently exist make it difficult for a non expert developer (data controller) to understand and realise strong privacy guarantees. In this paper we propose a principled Model-Driven Engineering (MDE) framework to model data services in terms of their pseudonymization strategies and identify the risks to breaches of user privacy. A developer can explore alternative pseudonymization strategies to determine the effectiveness of their pseudonymization strategy in terms of quantifiable metrics: i) violations of privacy requirements for every user in the current data set; ii) the trade-off between conforming to these requirements and the usefulness of the data for its intended purposes. We demonstrate through an experimental evaluation that the information provided by the framework is useful, particularly in complex situations where privacy requirements are different for different users, and can inform decisions to optimize a chosen strategy in comparison to applying an off-the-shelf algorithm

Digital Quantum Simulation of the Statistical Mechanics of a Frustrated Magnet

Many interesting problems in physics, chemistry, and computer science are equivalent to problems of interacting spins. However, most of these problems require computational resources that are out of reach by classical computers. A promising solution to overcome this challenge is to exploit the laws of quantum mechanics to perform simulation. Several "analog" quantum simulations of interacting spin systems have been realized experimentally. However, relying on adiabatic techniques, these simulations are limited to preparing ground states only. Here we report the first experimental results on a "digital" quantum simulation on thermal states; we simulated a three-spin frustrated magnet, a building block of spin ice, with an NMR quantum information processor, and we are able to explore the phase diagram of the system at any simulated temperature and external field. These results serve as a guide for identifying the challenges for performing quantum simulation on physical systems at finite temperatures, and pave the way towards large scale experimental simulations of open quantum systems in condensed matter physics and chemistry.Comment: 7 pages for the main text plus 6 pages for the supplementary material

Ergodic properties of a generic non-integrable quantum many-body system in thermodynamic limit

We study a generic but simple non-integrable quantum {\em many-body} system of {\em locally} interacting particles, namely a kicked $t-V$ model of spinless fermions on 1-dim lattice (equivalent to a kicked Heisenberg XX-Z chain of 1/2 spins). Statistical properties of dynamics (quantum ergodicity and quantum mixing) and the nature of quantum transport in {\em thermodynamic limit} are considered as the kick parameters (which control the degree of non-integrability) are varied. We find and demonstrate {\em ballistic} transport and non-ergodic, non-mixing dynamics (implying infinite conductivity at all temperatures) in the {\em integrable} regime of zero or very small kick parameters, and more generally and important, also in {\em non-integrable} regime of {\em intermediate} values of kicked parameters, whereas only for sufficiently large kick parameters we recover quantum ergodicity and mixing implying normal (diffusive) transport. We propose an order parameter (charge stiffness $D$) which controls the phase transition from non-mixing/non-ergodic dynamics (ordered phase, $D>0$) to mixing/ergodic dynamics (disordered phase, D=0) in the thermodynamic limit. Furthermore, we find {\em exponential decay of time-correlation function} in the regime of mixing dynamics. The results are obtained consistently within three different numerical and analytical approaches: (i) time evolution of a finite system and direct computation of time correlation functions, (ii) full diagonalization of finite systems and statistical analysis of stationary data, and (iii) algebraic construction of quantum invariants of motion of an infinite system, in particular the time averaged observables.Comment: 18 pages in REVTeX with 14 eps figures included, Submitted to Physical Review

Systems biology driven software design for the research enterprise

<p>Abstract</p> <p>Background</p> <p>In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.</p> <p>Results</p> <p>We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.</p> <p>Conclusion</p> <p>By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.</p

ER stress regulates myeloid-derived suppressor cell fate through TRAIL-R–mediated apoptosis

Myeloid-derived suppressor cells (MDSCs) dampen the immune response thorough inhibition of T cell activation and proliferation and often are expanded in pathological conditions. Here, we studied the fate of MDSCs in cancer. Unexpectedly, MDSCs had lower viability and a shorter half-life in tumor-bearing mice compared with neutrophils and monocytes. The reduction of MDSC viability was due to increased apoptosis, which was mediated by increased expression of TNF-related apoptosis–induced ligand receptors (TRAIL-Rs) in these cells. Targeting TRAIL-Rs in naive mice did not affect myeloid cell populations, but it dramatically reduced the presence of MDSCs and improved immune responses in tumor-bearing mice. Treatment of myeloid cells with proinflammatory cytokines did not affect TRAIL-R expression; however, induction of ER stress in myeloid cells recapitulated changes in TRAIL-R expression observed in tumor-bearing hosts. The ER stress response was detected in MDSCs isolated from cancer patients and tumor-bearing mice, but not in control neutrophils or monocytes, and blockade of ER stress abrogated tumor-associated changes in TRAIL-Rs. Together, these data indicate that MDSC pathophysiology is linked to ER stress, which shortens the lifespan of these cells in the periphery and promotes expansion in BM. Furthermore, TRAIL-Rs can be considered as potential targets for selectively inhibiting MDSCs

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure