Relative concentrations of haemostatic factors and cytokines in solvent/detergent-treated and fresh-frozen plasma

Background Indications, efficacy, and safety of plasma products are highly debated. We compared the concentrations of haemostatic proteins and cytokines in solvent/detergent-treated plasma (SDP) and fresh-frozen plasma (FFP). Methods Concentrations of the following parameters were measured in 25 SDP and FFP samples: fibrinogen (FBG), factor (F) II, F V, F VII, F VIII, F IX, F X, F XIII, von Willebrand factor (vWF), D-Dimers, ADAMTS-13 protease, tumour necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-8, and IL-10. Results Mean FBG concentrations in SDP and FFP were similar, but in FFP, the range was larger than in SDP (P<0.01). Mean F II, F VII, F VIII, F IX, and F XIII levels did not differ significantly. Higher concentrations of F V (P<0.01), F X (P<0.05), vWF (P<0.01), and ADAMTS-13 (P<0.01) were found in FFP. With the exception of F VIII and F IX, the range of concentrations for all of these factors was smaller (P<0.05) in SDP than in FFP. Concentrations of TNF-α, IL-8, and IL-10 (all P<0.01) were higher in FFP than in SDP, again with a higher variability and thus larger ranges (P<0.01). Conclusions Coagulation factor content is similar for SDP and FFP, with notable exceptions of less F V, vWF, and ADAMTS-13 in SDP. Cytokine concentrations (TNFα, IL-8, and IL-10) were significantly higher in FFP. The clinical relevance of these findings needs to be established in outcome studie

Queensland SharkSmart Drone Trial Final Report

Remotely Piloted Aircraft Systems, commonly called drones, provide a high-definition aerial view of a wide expanse of ocean, allowing the detection of potentially dangerous sharks in real-time, whilst having a negligible impact on the environment and non-target species. In addition, they are capable of spotting a range of marine hazards and can assist in beach rescue operations, thus providing numerous safety benefits for water users. The beaches of South-East Queensland (SEQ) have relatively good water clarity and a high level of visitation, making them an ideal location to test drones for detecting sharks and improving the safety of water users (Cardno, 2019). North Queensland beaches typically have lower water clarity, although it is important to test drones under these conditions to assess whether they can be effective at detecting sharks. The Queensland SharkSmart drone trial commenced on 19 September 2020, as a partnership between the Queensland Government Department of Agriculture and Fisheries (DAF) and Surf Life Saving Queensland (SLSQ). The trial was part of the Queensland Government’s commitment to research and trialling alternatives to traditional shark control measures. Drones were operated at two beaches on the Sunshine Coast (Alexandra Headland and Coolum North), two beaches on the Gold Coast (Southport Main Beach and Burleigh Beach) and one beach on North Stradbroke Island (NSI; Ocean beach) between 19 September 2020 and 4 October 2021. Additionally, to assess the effectiveness of drones at detecting sharks under the different environmental conditions found at North Queensland (NQ) beaches, drones were operated at Palm Cove, Cairns and Alma Bay, Magnetic Island, from 26 June 2021 to 31 October 2021. Drones were operated on weekends, public holidays and school holidays by SLSQ pilots, with two flights per hour from approximately 8am until midday. Flights lasted 15 - 20 minutes and followed a 400 m transect behind the surf break. All footage was collected in 4K and securely archived for later analysis with key operational and environmental data collected for every flight. When a shark was sighted, the drone pilot lowered the aircraft to determine the species and size while estimating distance of the animal from water users. Data analysis quantified the numbers of sharks sighted at each beach and the rate of sightings as a percentage across the whole trial from 19 September 2020 to 31 October 2021. Generalised Linear Mixed Models (GLMMs) were applied to quantify the influence of environmental and operational factors on the sightability (probability of a shark being sighted) of sharks. The movement tracks of sharks were mapped to analyse their behaviour and identify if there was clustering of movements in certain areas. Sighting rates from drones were also compared with shark catch in adjacent nets and drumlines deployed as part of the Queensland Shark Control Program (SCP)

Uniform random generation of large acyclic digraphs

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of the structure itself is of great interest. As well as for the assessment of different structure learning algorithms in simulation studies, a uniform sample from the space of directed acyclic graphs is required to evaluate the prevalence of certain structural features. Here we analyse how to sample acyclic digraphs uniformly at random through recursive enumeration, an approach previously thought too computationally involved. Based on complexity considerations, we discuss in particular how the enumeration directly provides an exact method, which avoids the convergence issues of the alternative Markov chain methods and is actually computationally much faster. The limiting behaviour of the distribution of acyclic digraphs then allows us to sample arbitrarily large graphs. Building on the ideas of recursive enumeration based sampling we also introduce a novel hybrid Markov chain with much faster convergence than current alternatives while still being easy to adapt to various restrictions. Finally we discuss how to include such restrictions in the combinatorial enumeration and the new hybrid Markov chain method for efficient uniform sampling of the corresponding graphs.Comment: 15 pages, 2 figures. To appear in Statistics and Computin

Joint Elastic Side-Scattering Lidar and Raman Lidar Measurements of Aerosol Optical Properties in South East Colorado

We describe an experiment, located in south-east Colorado, USA, that measured aerosol optical depth profiles using two Lidar techniques. Two independent detectors measured scattered light from a vertical UV laser beam. One detector, located at the laser site, measured light via the inelastic Raman backscattering process. This is a common method used in atmospheric science for measuring aerosol optical depth profiles. The other detector, located approximately 40km distant, viewed the laser beam from the side. This detector featured a 3.5m2 mirror and measured elastically scattered light in a bistatic Lidar configuration following the method used at the Pierre Auger cosmic ray observatory. The goal of this experiment was to assess and improve methods to measure atmospheric clarity, specifically aerosol optical depth profiles, for cosmic ray UV fluorescence detectors that use the atmosphere as a giant calorimeter. The experiment collected data from September 2010 to July 2011 under varying conditions of aerosol loading. We describe the instruments and techniques and compare the aerosol optical depth profiles measured by the Raman and bistatic Lidar detectors.Comment: 34 pages, 16 figure

Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.</p> <p>Results</p> <p>In the present paper we analyze cell cycle regulated genes in <it>S. cerevisiae</it>. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.</p> <p>Conclusion</p> <p>Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.</p

Atmospheric Density Uncertainty Quantification for Satellite Conjunction Assessment

Conjunction assessment requires knowledge of the uncertainty in the predicted orbit. Errors in the atmospheric density are a major source of error in the prediction of low Earth orbits. Therefore, accurate estimation of the density and quantification of the uncertainty in the density is required. Most atmospheric density models, however, do not provide an estimate of the uncertainty in the density. In this work, we present a new approach to quantify uncertainties in the density and to include these for calculating the probability of collision Pc. For this, we employ a recently developed dynamic reduced-order density model that enables efficient prediction of the thermospheric density. First, the model is used to obtain accurate estimates of the density and of the uncertainty in the estimates. Second, the density uncertainties are propagated forward simultaneously with orbit propagation to include the density uncertainties for Pc calculation. For this, we account for the effect of cross-correlation in position uncertainties due to density errors on the Pc. Finally, the effect of density uncertainties and cross-correlation on the Pc is assessed. The presented approach provides the distinctive capability to quantify the uncertainty in atmospheric density and to include this uncertainty for conjunction assessment while taking into account the dependence of the density errors on location and time. In addition, the results show that it is important to consider the effect of cross-correlation on the Pc, because ignoring this effect can result in severe underestimation of the collision probability.Comment: 15 pages, 6 figures, 5 table

Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first