Towards more Sustainable Peptide- based Antibiotics: Stable in Human Blood, Enzymatically Hydrolyzed in Wastewater?

The emergence and spread of antibiotic resistance is a major societal challenge and new antibiotics are needed to successfully fight bacterial infections. Because the release of antibiotics into wastewater and downstream environments is expected to contribute to the problem of antibiotic resistance, it would be beneficial to consider the environmental fate of antibiotics in the development of novel antibiotics. In this article, we discuss the possibility of designing peptide-based antibiotics that are stable during treatment (e.g. in human blood), but rapidly inactivated through hydrolysis by peptidases after their secretion into wastewater. In the first part, we review studies on the biotransformation of peptide-based antibiotics during biological wastewater treatment and on the specificity of dissolved extracellular peptidases derived from wastewater. In the second part, we present first results of our endeavour to identify peptide bonds that are stable in human blood plasma and susceptible to hydrolysis by the industrially produced peptidase Subtilisin A

A stochastic model for the evolution of the web allowing link deletion

Recently several authors have proposed stochastic evolutionary models for the growth of the web graph and other networks that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to the ``rich get richer'' phenomenon. We present a generalisation of the basic model by allowing deletion of individual links and show that it also gives rise to a power-law distribution. We derive the mean-field equations for this stochastic model and show that by examining a snapshot of the distribution at the steady state of the model, we are able to tell whether any link deletion has taken place and estimate the link deletion probability. Our model enables us to gain some insight into the distribution of inlinks in the web graph, in particular it suggests a power-law exponent of approximately 2.15 rather than the widely published exponent of 2.1

On variations in the fine-structure constant and stellar pollution of quasar absorption systems

At redshifts z_abs < 2, quasar absorption-line constraints on space-time variations in the fine-structure constant, alpha, rely on the comparison of MgII and FeII transition wavelengths. One potentially important uncertainty is the relative abundance of Mg isotopes in the absorbers which, if different from solar, can cause spurious shifts in the measured wavelengths and, therefore, alpha. Here we explore chemical evolution models with enhanced populations of intermediate-mass (IM) stars which, in their asymptotic giant branch (AGB) phase, are thought to be the dominant factories for heavy Mg isotopes at the low metallicities typical of quasar absorption systems. By design, these models partially explain recent Keck/HIRES evidence for a smaller alpha in z_abs < 2 absorption clouds than on Earth. However, such models also over-produce N, violating observed abundance trends in high-z_abs damped Lyman-alpha systems (DLAs). Our results do not support the recent claim of Ashenfelter, Mathews & Olive (2004b) that similar models of IM-enhanced initial mass functions (IMFs) may simultaneously explain the HIRES varying-alpha data and DLA N abundances. We explore the effect of the IM-enhanced model on Si, Al and P abundances, finding it to be much-less pronounced than for N. We also show that the 13C/12C ratio, as measured in absorption systems, could constitute a future diagnostic of non-standard models of the high-redshift IMF.Comment: Accepted by MNRAS. 13 pages, 14 ps figure

Effects of Climate Change on Peatland Reservoirs: A DOC Perspective

Peatland reservoirs are global hotspots for drinking water provision and are likely to become more important as demand per capita rises and the climate changes. Dissolved organic carbon (DOC) is associated with harmful disinfection byproducts and reduced aesthetic quality, and its removal is the major treatment cost. Littoral zones are known to be disproportionately important for DOC production through macrophyte inputs, and such communities are predicted to expand with warming in northern regions. However, little is known about autochthonous DOC contributions and their response to climatic change. Here we exposed mesocosms to elevated CO2 (eCO2), warming and a combined treatment across a trophic gradient. Regression analysis indicated that while sediments, macrophytes, and phytoplankton are important DOC sources (P < 0.05), benthic algal biomass showed the strongest relationship with DOC (P < 0.05), suggesting it is an underestimated source. DOC removal indicators, namely phenol oxidase (depolymerization) and respiration (mineralization) were inversely related to DOC concentration in oligohumic (P < 0.05) and oligotrophic (P < 0.1) systems, suggesting heterotrophic processes are important in DOC removal. DOC concentrations increased across all systems (P < 0.05), irrespective of trophic status, due to increased photoautotrophic inputs (macrophyte, pelagic, and benthic algae) under eCO2, warming, and combined scenarios, with inhibited depolymerization and mineralization under eCO2, even when combined with warming (P < 0.05 and P < 0.05 excepting the oligo-mesotrophic reservoir P < 0.1 respectively). Increased DOC loads of all fractions, regardless of provenance, are predicted in a future climate and, thus, investment in techniques to remove a greater range of DOC fractions is proposed to help “future proof” drinking water supplies

A stochastic evolutionary model generating a mixture of exponential distributions

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in \cite{FENN15} so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model

Quantum Optimization Problems

Krentel [J. Comput. System. Sci., 36, pp.490--509] presented a framework for an NP optimization problem that searches an optimal value among exponentially-many outcomes of polynomial-time computations. This paper expands his framework to a quantum optimization problem using polynomial-time quantum computations and introduces the notion of an ``universal'' quantum optimization problem similar to a classical ``complete'' optimization problem. We exhibit a canonical quantum optimization problem that is universal for the class of polynomial-time quantum optimization problems. We show in a certain relativized world that all quantum optimization problems cannot be approximated closely by quantum polynomial-time computations. We also study the complexity of quantum optimization problems in connection to well-known complexity classes.Comment: date change

A stochastic evolutionary model for capturing human dynamics

The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in various contexts. Here we propose a generative model to capture the dynamics of survival analysis, traditionally employed in clinical trials and reliability analysis in engineering. We derive a general solution for the model in the form of a product, and then a continuous approximation to the solution via the renewal equation describing age-structured population dynamics. This enables us to model a wide range of survival distributions, according to the choice of the mortality distribution. We provide empirical evidence for the validity of the model from a longitudinal data set of popular search engine queries over 114 months, showing that the survival function of these queries is closely matched by the solution for our model with power-law mortality