Monitoring of water quality from roof runoff: Interpretation using multivariate analysis

The quality of harvested rainwater used for toilet flushing in a private house in the south-west of France was assessed over a one-year period. Temperature, pH, conductivity, colour, turbidity, anions, cations, alkalinity, total hardness and total organic carbon were screened using standard analytical techniques. Total flora at 22°C and 36°C, total coliforms, Escherichia coli and enterococci were analysed. Overall, the collected rainwater had good physicochemical quality but did not meet the requirements for drinking water. The stored rainwater is characterised by low conductivity, hardness and alkalinity compared to mains water. Three widely used bacterial indicators - total coliforms, E. coli and enterococci - were detected in the majority of samples, indicating microbiological contamination of the water. To elucidate factors affecting the rainwater composition, principal component analysis and cluster analysis were applied to the complete data set of 50 observations. Chemical and microbiological parameters fluctuated during the course of the study, with the highest levels of microbiological contamination observed in roof runoffs collected during the summer. Escherichia coli and enterococci occurred simultaneously, and their presence was linked to precipitation. Runoff quality is also unpredictable because it is sensitive to the weather. Cluster analysis differentiated three clusters: ionic composition, parameters linked with the microbiological load and indicators of faecal contamination. In future surveys, parameters from these three groups will be simultaneously monitored to more accurately characterise roof collected rainwater

Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton.

Primers targeting the 16S small subunit ribosomal RNA marker gene, used to characterize bacterial and archaeal communities, have recently been re-evaluated for marine planktonic habitats. To investigate whether primer selection affects the ecological interpretation of bacterioplankton populations and community dynamics, amplicon sequencing with four primer sets targeting several hypervariable regions of the 16S rRNA gene was conducted on both mock communities constructed from cloned 16S rRNA genes and a time-series of DNA samples from the temperate coastal Santa Barbara Channel. Ecological interpretations of community structure (delineation of depth and seasonality, correlations with environmental factors) were similar across primer sets, while population dynamics varied. We observed substantial differences in relative abundances of taxa known to be poorly resolved by some primer sets, such as Thaumarchaeota and SAR11, and unexpected taxa including Roseobacter clades. Though the magnitude of relative abundances of common OTUs differed between primer sets, the relative abundances of the OTUs were nonetheless strongly correlated. We do not endorse one primer set but rather enumerate strengths and weaknesses to facilitate selection appropriate to a system or experimental goal. While 16S rRNA gene primer bias suggests caution in assessing quantitative population dynamics, community dynamics appear robust across studies using different primers

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

On Simulating the Proton-Irradiation of O2_2 and H2_2O Ices Using Astrochemical-type Models, with Implications for Bulk Reactivity

Many astrochemical models today explicitly consider the species that comprise the bulk of interstellar dust grain ice-mantles separately from those in the top few monolayers. Bombardment of these ices by ionizing radiation - whether in the form of cosmic rays, stellar winds, or radionuclide emission - represents an astrochemically viable means of driving a rich chemistry even in the bulk of the ice-mantle, now supported by a large body of work in laboratory astrophysics. In this study, using an existing rate equation-based astrochemical code modified to include a method of considering radiation chemistry recently developed by us, we attempted to simulate two such studies in which (a) pure O$_2$ ice at 5 K and, (b) pure H$_2$O ice at 16 K and 77 K, were bombarded by keV H$^+$ ions. Our aims are twofold: (1) to test the capability of our newly developed method to replicate the results of ice-irradiation experiments, and (2) to determine in such a well-constrained system how bulk chemistry is best handled using the same gas-grain codes that are used to model the interstellar medium (ISM). We find that our modified astrochemical model is able to reproduce both the abundance of O$_3$ in the 5 K pure O$_2$ ice, as well as both the abundance of H$_2$O$_2$ in the 16 K water ice and the previously noted decrease of hydrogen peroxide at higher temperatures. However, these results require the assumption that radicals and other reactive species produced via radiolysis react quickly and non-diffusively with neighbors in the ice.Comment: ApJ, accepted. 30 pages, 5 figure

Dynamics of trace metal sorption by an ion-exchange chelating resin described by a mixed intraparticle/film diffusion transport model. The Cd/Chelex case

The time-evolution of Cd2+ ion sorption by Chelex 100 resin was studied in batch experiments as a function of time, pH, ionic strength, stirring rate, mass of resin and initial metal ion concentration. In the experimental conditions, the amount of resin sites are in excess with respect to the amount of metal ion, leading to extensive depletion of metal in bulk solution when equilibrium is reached. The data were described using a mixed control mass transport model in finite volume conditions (MCM) that includes explicitly both intraparticle and film diffusion steps. Exact numerical computations and a new approximate analytical expression of this model are reported here. MCM successfully predicts the influence of pH and ionic strength on the experimental Cd(II)/Chelex kinetic profiles (which cannot be justified by a pure film diffusion controlled mechanism) with a minimum number of fitting parameters. The overall diffusion coefficient inside the resin was modelled in terms of the Donnan factor and the resin/cation binding stability constant. The values of the latter coefficient as a function of pH and ionic strength were estimated from the Gibbs-Donnan model. Even though MCM is numerically more involved than models exclusively restricted to film or intraparticle diffusion control, it proves to be accurate in a wider range of values of the mass transfer Biot number and solution/resin metal ratios.The authors gratefully acknowledge support for this research from the Spanish Ministry MINECO (Projects CTM2013-48967 and CTM2016-78798) and by the “Comissionat d'Universitats i Recerca de la Generalitat de Catalunya” (2014SGR1132). FQ acknowledges a grant from AGAUR

Probing the Solute-Solvent Interaction of an Azo-Bonded Prodrug in Neat and Binary Media: Combined Experimental and Computational Study

Preferential solvation has significant importance in interpreting the molecular physicochemical properties of wide spectrum of materials in solution. In this work, the solute-solvent interaction of pro-drug Sulfasalazine (SSZ) in neat and binary media was investigated experimentally and computationally. The solute-solvent interactions of interest were spectrophotometrically probed and computationally investigated for providing insights concerning the molecular aspects of SSZ:media interaction. Experimentally, the obtained results in 1,4-dioxane:water binary mixture demonstrated a dramatic non-linear changes in the spectral behavior of SSZ indicative of the dependency of its molecular behaviors on the compositions of the molecular microenvironment in the essence of solute-solvent interaction. Computationally, geometry optimization and simulation of the absorption spectra of SSZ in media of interest were performed employing DFT and TD-DFT methods, respectively, where the solvent effects on the absorption were examined implicitly using IEFPCM method. Obtained results revealed a nonpolar nature of the molecular orbitals that are directly involved in the SSZ:medium interaction. As in good correspondence with the experimental results, these simulations demonstrated that these orbitals are of non-polar nature and hence minimally affected by polarity of the media and in turn favoring the non-polar molecular environments. On the other hand, the molecular origin of SSZ:media interaction was demonstrated explicitly through complexation of SSZ with water molecules revealing a cooperative hydrogen bonding stabilization with an average length of 1.90 Å. The findings of this work demonstrate the significance of the preferential solvation and composition of the molecular microenvironment on the physicochemical properties of molecules of pharmaceutical importance. © 2019, The Author(s).Scopu