Biokinetics Of microbial consortia using biogenic sulfur as a novel electron donor for sustainable denitrification

In this study, the biokinetics of autotrophic denitrification with biogenic S0 (ADBIOS) for the treatment of nitrogen pollution in wastewaters were investigated. The used biogenic S0, a by-product of gas desulfurization, was an elemental microcrystalline orthorhombic sulfur with a median size of 4.69 µm and a specific surface area of 3.38 m2/g, which made S0 particularly reactive and bioavailable. During denitritation, the biomass enriched on nitrite (NO2–) was capable of degrading up to 240 mg/l NO2–-N with a denitritation activity of 339.5 mg NO2–-N/g VSS·d. The use of biogenic S0 induced a low NO2–-N accumulation, hindering the NO2–-N negative impact on the denitrifying consortia and resulting in a specific denitrification activity of 223.0 mg NO3–-N/g VSS·d. Besides Thiobacillus being the most abundant genus, Moheibacter and Thermomonas were predominantly selected for denitrification and denitritation, respectively

The X-ray spectrum of RX J1914.4+2456 revisited

It has been proposed that RX J1914.4+2456 is a stellar binary system with an orbital period of 9.5 mins. As such it shares many similar properties with RX J0806.3+1527 (5.4 mins). However, while the X-ray spectrum of RX J0806.3+1527 can be modelled using a simple absorbed blackbody, the X-ray spectrum of RX J1914.4+2456 has proved difficult to fit using a physically plausible model. In this paper we re-examine the available X-ray spectra of RX J1914.4+2456 taken using XMM-Newton. We find that the X-ray spectra can be fitted using a simple blackbody and an absorption component which has a significant enhancement of neon compared to the solar value. We propose that the material in the inter-binary system is significantly enhanced with neon. This makes its intrinsic X-ray spectrum virtually identical to RX J0806.3+1527. We re-access the X-ray luminosity of RX J1914.4+2456 and the implications of these results.Comment: Accepted for publication in MNRA

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Obtaining the nuclear gluon distribution from heavy quark decays to lepton pairs in pAA collisions

We have studied how lepton pairs from decays of heavy-flavoured mesons produced in p$A$ collisions can be used to determine the modifications of the gluon distribution in the nucleus. Since heavy quark production is dominated by the $gg$ channel, the ratio of correlated lepton pair cross sections from $D\bar D$ and $B\bar B$ decays in p$A$ and pp collisions directly reflects the ratio $R_g^A \equiv f_g^A/f_g^p$. We have numerically calculated the lepton pair cross sections from these decays in pp and p$A$ collisions at SPS, RHIC and LHC energies. We find that ratio of the p$A$ to pp cross sections agrees quite well with the input $R_g^A.$ Thus, sufficiently accurate measurements could be used to determine the nuclear modification of the gluon distribution over a greater range of $x$ and $Q^2$ than presently available, putting strong constraints on models.Comment: 19 pages, 6 figure

Collagen-related biomarkers in severe sepsis: a big stretch?

Biomedical scientists are aggressively investigating biomarkers of disease and injury. The rationale for identifying biomarkers during pathological states, such as severe sepsis, is to improve clinical prognostication and stratify therapeutic interventions for optimal recovery. An added benefit of biomarker studies is knowledge genesis on pathophysiological mechanisms, critical information that provides a basis for hypothesis-driven research. Unfortunately, biomarkers rarely alter our clinical approach in severe sepsis as they are often non-specific, lack adequate sensitivity and/or are difficult to measure and interpret accurately. Given the complexity and heterogeneity of severe sepsis, and the unique genetically derived susceptibilities of individuals, it is highly unlikely that one or even a handful of biomarkers will provide adequate biomedical information for clinical guidance. Thus, biomarkers will ultimately alter clinical decision making only once a panel of promising biomarkers is identified, maximizing sensitivity and specificity, and then adequately scrutinized with quantitative scoring methods over large populations of patients

Antiproton Production in p+Ap+A Collisions at AGS Energies

Inclusive and semi-inclusive measurements are presented for antiproton ($\bar{p}$) production in proton-nucleus collisions at the AGS. The inclusive yields per event increase strongly with increasing beam energy and decrease slightly with increasing target mass. The $\bar{p}$ yield in 17.5 GeV/c p+Au collisions decreases with grey track multiplicity, $N_g$, for $N_g>0$, consistent with annihilation within the target nucleus. The relationship between $N_g$ and the number of scatterings of the proton in the nucleus is used to estimate the $\bar{p}$ annihilation cross section in the nuclear medium. The resulting cross section is at least a factor of five smaller than the free $\bar{p}-p$ annihilation cross section when assuming a small or negligible formation time. Only with a long formation time can the data be described with the free $\bar{p}-p$ annihilation cross section.Comment: 8 pages, 6 figure

Covariance of Antiproton Yield and Source Size in Nuclear Collisions

We confront for the first time the widely-held belief that combined event-by-event information from quark gluon plasma signals can reduce the ambiguity of the individual signals. We illustrate specifically how the measured antiproton yield combined with the information from pion-pion HBT correlations can be used to identify novel event classes.Comment: 8 pages, 5 figures, improved title, references and readability; results unchange

Diabetic Ketoacidosis-Associated Stroke in Children and Youth

Diabetic ketoacidosis (DKA) is a state of severe insulin deficiency, either absolute or relative, resulting in hyperglycemia and ketonemia. Although possibly underappreciated, up to 10% of cases of intracerebral complications associated with an episode of DKA, and/or its treatment, in children and youth are due to hemorrhage or ischemic brain infarction. Systemic inflammation is present in DKA, with resultant vascular endothelial perturbation that may result in coagulopathy and increased hemorrhagic risk. Thrombotic risk during DKA is elevated by abnormalities in coagulation factors, platelet activation, blood volume and flow, and vascular reactivity. DKA-associated cerebral edema may also predispose to ischemic injury and hemorrhage, though cases of stroke without concomitant cerebral edema have been identified. We review the current literature regarding the pathogenesis of stroke during an episode of DKA in children and youth