Fabrication and Characterization of Borosilicate Glasses Containing Alpha-Radionuclides and Silver From Conversion and Mixed-Oxide Facilities Proposed for Russia

Liquid and solid radioactive wastes are formed during conversion of plutonium metal to oxide and during fabrication of weapons-grade plutonium into mixed-oxide (MOX) fuel. In Russia, these wastes are to be processed for disposition by immobilization in either borosilicate glass or cement matrices depending upon the waste stream-specific radionuclide contents. Vitrification is planned for the liquid high-level waste raffinate stream containing the bulk of the Am-241 produced from Pu-241 decay. Previous work on the Russian MOX Fuel Fabrication Facility (R-MFFF) by the Public Joint Stock Corporation (TVEL) [1] showed that this waste stream may contain significant amounts of silver derived from the electrochemical dissolution of PuO2 using a Ag(II) catalyst. The work reported here further investigated silver solubility limits, which, if exceeded in a production glass melter, allow discrete silver grains to form in the glass and also deposit over time on the bottom of a joule-heated ceramic melter. In melters with immersed electrodes, such as the Russian EP-100 for phosphate glasses or the US Duratek DP-100 type melters for borosilicate glasses that are being considered for use at the Siberian Chemical Combine (SCC) Tomsk site, the undissolved silver could cause a short circuit and an unacceptable production melter failure. The silver solubility limit of 3.85 wt% Ag{sub 2}O in liquid, alpha-bearing wastes determined in this work will guide the production scale use of borosilicate glass compositions, and effectively increase the capacity of the ceramic melters and reduce the total volume of solidified vitrified wastes at SCC Tomsk that require storage prior to geologic disposal

Biomarker of food intake for assessing the consumption of dairy and egg products

Foods of animal origin constitute one of the predominant food groups consumed in Western diets. They play an essential role in human nutrition as they represent an excellent source of high quality proteins, vitamins, minerals and fats. Foods of animal origin are highly diverse (e.g. meat, fish, dairy products and eggs) and their associations with a range of nutritional and health outcomes are therefore heterogeneous. Such associations are also often weak or debated due to the difficulty in establishing correct assessments of dietary intake. Therefore, in order to better characterize associations between the consumption of specific foods of animal origin and health outcomes, it is important to identify reliable biomarkers of food intake (BFIs). BFIs provide a more accurate measure of intake and are independent of the memory and sincerity of the subjects as well as of their knowledge about the consumed foods. To date, only a very limited number of compounds have been proposed as biomarkers of the intake of foods of animal origin and further studies are necessary to validate them and to discover new candidate BFIs. We have, therefore, conducted a systematic search of the scientific literature to evaluate the current status of potential BFIs for each category of foods of animal origin commonly consumed in Europe. This review reports on candidate biomarkers for dairy products and eggs intake, while biomarkers for fish and meat intake will be published separately. Remarkably, validated BFIs for dairy products and eggs are not available. A series of challenges hinders their identification and validation, in particular the heterogeneous composition of each food within a product category and the lack of specificity of the markers identified so far. Untargeted metabolomic strategies may allow the identification of novel food biomarkers, that, when taken separately or in combination, could be used to assess the intake of dairy products and eggs

Influence of degree correlations on network structure and stability in protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale <it>yeast </it>protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.</p> <p>Results</p> <p>For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks.</p> <p>Conclusion</p> <p>Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree correlations, biological PPI networks do not actually seem to make use of this possibility.</p

The Dichotomy in Degree Correlation of Biological Networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Search for Multimessenger Sources of Gravitational Waves and High-energy Neutrinos with Advanced LIGO during Its First Observing Run, ANTARES, and IceCube

Astrophysical sources of gravitational waves, such as binary neutron star and black hole mergers or core-collapse supernovae, can drive relativistic outflows, giving rise to non-thermal high-energy emission. High-energy neutrinos are signatures of such outflows. The detection of gravitational waves and high-energy neutrinos from common sources could help establish the connection between the dynamics of the progenitor and the properties of the outflow. We searched for associated emission of gravitational waves and high-energy neutrinos from astrophysical transients with minimal assumptions using data from Advanced LIGO from its first observing run O1, and data from the Antares and IceCube neutrino observatories from the same time period. We focused on candidate events whose astrophysical origins could not be determined from a single messenger. We found no significant coincident candidate, which we used to constrain the rate density of astrophysical sources dependent on their gravitational-wave and neutrino emission processes

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries