Developing a risk assessment model using fuzzy logic to assess groundwater contamination from hydraulic fracturing

Technological advances in directional drilling has led to rapid exploitation of onshore unconventional hydrocarbons using a technique known as hydraulic fracturing. This process took off initially in the US, with Canada following closely behind, but brought with it controversial debates over environmental protection, particularly in relation to groundwater contamination and well integrity failure. Prospective shale gas regions lie across areas in Europe but countries such as the UK are facing public and government turmoil surrounding their potential exploitation. This extent of energy development requires detailed risk analysis to eliminate or mitigate damage to the natural environment. Subsurface energy activities involve complex processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. A new, alternative methodology was applied to onshore hydraulic fracturing to assess the risk of groundwater contamination during well injection and production. The techniques used deterministic models to construct failure scenarios with respect to groundwater contamination, stochastic approaches to determine component failures of a well, and fuzzy logic to address insufficiency or complexity in data. The framework was successfully developed using available data and regulations in British Columbia (BC), Canada. Fuzzy Fault Tree Analysis (FFTA) was demonstrated as a more robust technique compared with conventional Fault Tree Analysis (FTA) and implemented successfully to quantify cement failure. A collection of known risk analysis methods such as Event Tree Analysis (ETA), Time at Risk Failure (TRF) and Mean Time To Failure (MTTF) models were successfully applied to well integrity failure during injection, with the novel addition of quantifying cement failures. An analytical model for Surface Casing Pressure (SCP) during well production highlighted data gaps on well constructions so a fuzzy logic model was built to a 93% accuracy to determine the location of cement in a well. This novel application of fuzzy logic allowed the calculation of gas flow rate into an annulus and hence the probability of well integrity failure during production using ETA. The framework quantified several risk pathways across multiple stages of a well using site-specific data, but was successfully applied to a UK case study where there existed significant differences in geology, well construction and regulations. The application required little extra work and demonstrated the success and limitations of the model and where future work could improve model development. This research indicated that risks to groundwater from hydraulic fracturing differ substantially depending on well construction. Weighing up the risk to groundwater compared with financial gain for well construction will be essential for decision-makers and policy. To reduce the social anxiety of hydraulic fracturing in the UK, decision-makers who face criticism must ensure information is disseminated properly to the public with a well-defined risk analysis which can be interpreted easily without prerequisite knowledge. Finally, although this research is based on onshore hydraulic fracturing, the risk assessment techniques are generic enough to allow application of this research to other subsurface activities such as CO2 sequestration, waste injection disposal and geothermal energy.Engineering and Physical Sciences Research Council (EPSRC

Production of bioactive secondary metabolites by marine Vibrionaceae

Abstract: Bacteria belonging to the Vibrionaceae family are widespread in the marine environment. Today, 128 species of vibrios are known. Several of them are infamous for their pathogenicity or symbiotic relationships. Despite their ability to interact with eukaryotes, the vibrios are greatly underexplored for their ability to produce bioactive secondary metabolites and studies have been limited to only a few species. Most of the compounds isolated from vibrios so far are non-ribosomal peptides or hybrids thereof, with examples of N-containing compounds produced independent of nonribosomal peptide synthetases (NRPS). Though covering a limited chemical space, vibrios produce compounds with attractive biological activities, including antibacterial, anticancer, and antivirulence activities. This review highlights some of the most interesting structures from this group of bacteria. Many compounds found in vibrios have also been isolated from other distantly related bacteria. This cosmopolitan occurrence of metabolites indicates a high incidence of horizontal gene transfer, which raises interesting questions concerning the ecological function of some of these molecules. This account underlines the pending potential for exploring new bacterial sources of bioactive compounds and the challenges related to their investigation

Proteorhodopsin Phototrophy Promotes Survival of Marine Bacteria during Starvation

Mutational analysis provides direct evidence for the link between proteorhodopsin light-harvesting and enhanced survival of marine bacteria

Identification of simple sequence repeat markers for sweetpotato weevil resistance

The development of sweetpotato [Ipomoea batatas (L.) Lam] germplasm with resistance to sweetpotato weevil (SPW) requires an understanding of the biochemical and genetic mechanisms of resistance to optimize crop resistance. The African sweetpotato landrace, ‘New Kawogo’, was reported to be moderately resistant to two species of SPW, Cylas puncticollis and Cylas brunneus. Resistance has been associated with the presence of hydroxycinnamic acids esters (HCAs), but the underlying genetic basis remains unknown. To determine the genetic basis of this resistance, a bi-parental sweetpotato population from a cross between the moderately resistant, white-fleshed ‘New Kawogo’ and the highly susceptible, orange-fleshed North American variety ‘Beauregard’ was evaluated for SPW resistance and genotyped with simple sequence repeat (SSR) markers to identify weevil resistance loci. SPW resistance was measured on the basis of field storage root SPW damage severity and total HCA ester concentrations. Moderate broad sense heritability (H2 = 0.49) was observed for weevil resistance in the population. Mean genotype SPW severity scores ranged from 1.0 to 9.0 and 25 progeny exhibited transgressive segregation for SPW resistance. Mean genotype total HCA ester concentrations were significantly different (P < 0.0001). A weak but significant correlation (r = 0.103, P = 0.015) was observed between total HCA ester concentration and SPW severity. A total of five and seven SSR markers were associated with field SPW severity and total HCA ester concentration, respectively. Markers IBS11, IbE5 and IbJ544b showed significant association with both field and HCA-based resistance, representing potential markers for the development of SPW resistant sweetpotato cultivars

State-level tracking of COVID-19 in the United States

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

Estimating the Cost of Type 1 Diabetes in the U.S.: A Propensity Score Matching Method

Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature.Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated$10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided.We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S

Animal Ca2+ release-activated Ca2+ (CRAC) channels appear to be homologous to and derived from the ubiquitous cation diffusion facilitators

<p>Abstract</p> <p>Background</p> <p>Antigen stimulation of immune cells triggers Ca²⁺entry through Ca²⁺release-activated Ca²⁺(CRAC) channels, promoting an immune response to pathogens. Defects in a CRAC (Orai) channel in humans gives rise to the hereditary Severe Combined Immune Deficiency (SCID) syndrome. We here report results that define the evolutionary relationship of the CRAC channel proteins of animals, and the ubiquitous Cation Diffusion Facilitator (CDF) carrier proteins.</p> <p>Findings</p> <p>CDF antiporters derived from a primordial 2 transmembrane spanner (TMS) hairpin structure by intragenic triplication to yield 6 TMS proteins. Four programs (IC/GAP, GGSEARCH, HMMER and SAM) were evaluated for identifying sequence similarity and establishing homology using statistical means. Overall, the order of sensitivity (similarity detection) was IC/GAP = GGSEARCH > HMMER > SAM, but the use of all four programs was superior to the use of any two or three of them. Members of the CDF family appeared to be homologous to members of the 4 TMS Orai channel proteins.</p> <p>Conclusions</p> <p>CRAC channels derived from CDF carriers by loss of the first two TMSs of the latter. Based on statistical analyses with multiple programs, TMSs 3-6 in CDF carriers are homologous to TMSs 1-4 in CRAC channels, and the former was the precursor of the latter. This is an unusual example of how a functionally and structurally more complex protein may have predated a simpler one.</p

The Bacterial Intimins and Invasins: A Large and Novel Family of Secreted Proteins

Gram-negative bacteria have developed a limited repertoire of solutions for secreting proteins from the cytoplasmic compartment to the exterior of the cell. Amongst the spectrum of secreted proteins are the intimins and invasins (the Int/Inv family; TC# 1.B.54) which are characterized by an N-terminal β-barrel domain and a C-terminal surface localized passenger domain. Despite the important role played by members of this family in diseases mediated by several species of the Enterobacteriaceae, there has been little appreciation for the distribution and diversity of these proteins amongst Gram-negative bacteria. Furthermore, there is little understanding of the molecular events governing secretion of these proteins to the extracellular milieu.In silico approaches were used to analyze the domain organization and diversity of members of this secretion family. Proteins belonging to this family are predominantly associated with organisms from the γ-proteobacteria. Whilst proteins from the Chlamydia, γ-, β- and ε-proteobacteria possess β-barrel domains and passenger domains of various sizes, Int/Inv proteins from the α-proteobacteria, cyanobacteria and chlorobi possess only the predicted β-barrel domains. Phylogenetic analyses revealed that with few exceptions these proteins cluster according to organismal type, indicating that divergence occurred contemporaneously with speciation, and that horizontal transfer was limited. Clustering patterns of the β-barrel domains correlate well with those of the full-length proteins although the passenger domains do so with much less consistency. The modular subdomain design of the passenger domains suggests that subdomain duplication and deletion have occurred with high frequency over evolutionary time. However, all repeated subdomains are found in tandem, suggesting that subdomain shuffling occurred rarely if at all. Topological predictions for the β-barrel domains are presented.Based on our in silico analyses we present a model for the biogenesis of these proteins. This study is the first of its kind to describe this unusual family of bacterial adhesins

Why do banks promise to pay par on demand?

We survey the theories of why banks promise to pay par on demand and examine evidence about the conditions under which banks have promised to pay the par value of deposits and banknotes on demand when holding only fractional reserves. The theoretical literature can be broadly divided into four strands: liquidity provision, asymmetric information, legal restrictions, and a medium of exchange. We assume that it is not zero cost to make a promise to redeem a liability at par value on demand. If so, then the conditions in the theories that result in par redemption are possible explanations of why banks promise to pay par on demand. If the explanation based on customers’ demand for liquidity is correct, payment of deposits at par will be promised when banks hold assets that are illiquid in the short run. If the asymmetric-information explanation based on the difficulty of valuing assets is correct, the marketability of banks’ assets determines whether banks promise to pay par. If the legal restrictions explanation of par redemption is correct, banks will not promise to pay par if they are not required to do so. If the transaction explanation is correct, banks will promise to pay par value only if the deposits are used in transactions. After the survey of the theoretical literature, we examine the history of banking in several countries in different eras: fourth-century Athens, medieval Italy, Japan, and free banking and money market mutual funds in the United States. We find that all of the theories can explain some of the observed banking arrangements, and none explain all of them