Saltwater intrusion induces shifts in soil microbial diversity and carbon use efficiency in a coastal grassland ecosystem

Salt accumulation and salinisation of coastal soils is a global issue. Further, climate change is likely to increase the amount of land affected by salinity due to the increasing frequency and severity of coastal flooding and brackish water ingress. The impact of this on the ability of soils to deliver ecosystem services, particularly carbon (C) storage, however, remains unclear. We hypothesized that coastal inundation would negatively affect C storage by lowering plant C inputs and by placing greater osmotic stress on the microbial community leading to a reduced C use efficiency (CUE). Here, we use a coastal grassland ecosystem, which is becoming increasingly subjected to sea and brackish water flooding, to explore the relationship between plant/microbial growth and CUE along a natural salinity gradient. To reflect steady state conditions, we traced the turnover and partitioning of a low (ambient) dose and high (growth stimulation) dose of 14C-labelled glucose into microbial anabolic and catabolic pools, from which microbial CUE was calculated. This was supported by measurements of the diversity of the bacterial community across the salinity gradient using 16S metabarcoding. Our results showed that coastal flooding significantly reduced plant growth (p < 0.005), increased soil C content (p < 0.05) and induced an increase in microbial CUE under low glucose-C conditions (p < 0.05). Conversely, no significant difference in CUE or microbial growth was apparent when a high glucose-C dose was used. Soil bacterial community alpha (α) diversity increased with soil salinity while beta (β) diversity also shifted in response to the higher saline conditions. Our analysis suggests that the largest impact of coastal flooding on soil C cycling was the inability of the plant community to adapt, leading to higher plant residue inputs as well as the decline in soil structure. Conversely, the microbial community had adapted to the increased salinity, resulting in only small changes in the uptake and metabolic partitioning of C

Systematic review of antimicrobial drug prescribing in hospitals.

Prudent antibiotic prescribing to hospital inpatients has the potential to reduce the incidences of antimicrobial resistance and healthcare-associated infection. We reviewed the literature from January 1980 to November 2003 to identify rigorous evaluations of interventions to improve hospital antibiotic prescribing. We identified 66 studies with interpretable data of which 16 reported 20 microbiological outcomes: Gram negative resistant bacteria (GNRB), 10 studies; Clostridium difficile associated diarrhoea (CDAD), 5 studies; vancomycin resistant enterococci (VRE), 3 studies and methicillin resistant Staphylococcus aureus (MRSA), 2 studies. Four studies provide good evidence that the intervention changed microbial outcomes with low risk of alternative explanations, eight studies provide less convincing evidence and four studies were negative. The strongest and most consistent evidence was for CDAD but we were able to analyse only the immediate impact of interventions because of nonstandardised durations of follow up. The ability to compare results of studies could be substantially improved by standardising methodology and reporting

Prediction of skin penetration using machine learning methods

Improving predictions of the skin permeability coefficient is a difficult problem. It is also an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we applyK-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the permeability coefficient. We obtain a considerable improvement over the quantitative structureactivity relationship (QSARs) predictors. We show that using five features, which are molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups, can produce better predictions than the one using only lipophilicity and the molecular weight. The Gaussian process regression with five compound features gives the best performance in this work

The perseveration of checking thoughts and mood–as–input hypothesis

This paper describes two experiments designed to investigate how a current model of task perseveration, the mood-as-input hypothesis, might be applied to activities relevant to compulsive checking. The mood-as-input hypothesis predicts that perseveration at an open-ended task will be determined by a combination of the “stop rules” adopted for the task, and the valency of the mood state in which the task is conducted. Experiment 1 required participants to generate items that should be checked for safety/security if they were leaving their home unattended. Experiment 2 used an analogue recall task, in which participants were asked to recall items from a comprehensive list of items that should be checked if they were to leave their home safe/secure. Both experiments found that perseveration at the tasks was determined by particular configurations of mood and stop rules for the task. Of most relevance to compulsive checking was the fact that facilitated perseveration occurred when participants were asked to undertake the tasks in a negative mood using “as many as can” stop rules. Implications for the factors that develop and maintain compulsive checking are discussed

The application of stochastic machine learning methods in the prediction of skin penetration

Original article can be found at: http://www.sciencedirect.com Copyright ElsevierImproving predictions of skin permeability is a significant problem for which mathematical solutions have been sought for around twenty years. However, the current approaches are limited by the nature of the models chosen and the nature of the dataset. This is an important problem, particularly with the increased use of transdermal and topical drug delivery systems. In this work, we apply K-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the skin permeability coefficient of penetrants. A considerable improvement, both statistically and in terms of the accuracy of predictions, over the current quantitative structure-permeability relationships (QSPRs) was found. Gaussian processes provided the most accurate predictions, when compared to experimentally generated results. It was also shown that using five molecular descriptors - molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups - can produce better predictions than when using only lipophilicity and the molecular weight, which is an approach commonly found with QSPRs. The Gaussian process regression with five compound features was shown to give the best performance in this work. Therefore, Gaussian processes would appear to provide a viable alternative to the development of predictive models for skin absorption and underpin more realistically mechanistic understandings of the physical process of the percutaneous absorption of exogenous chemicals. (C) 2010 Elsevier B.V. All rights reserved.Peer reviewe

Web-based textual analysis of free-text patient experience comments from a survey in primary care.

BACKGROUND: Open-ended questions eliciting free-text comments have been widely adopted in surveys of patient experience. Analysis of free text comments can provide deeper or new insight, identify areas for action, and initiate further investigation. Also, they may be a promising way to progress from documentation of patient experience to achieving quality improvement. The usual methods of analyzing free-text comments are known to be time and resource intensive. To efficiently deal with a large amount of free-text, new methods of rapidly summarizing and characterizing the text are being explored. OBJECTIVE: The aim of this study was to investigate the feasibility of using freely available Web-based text processing tools (text clouds, distinctive word extraction, key words in context) for extracting useful information from large amounts of free-text commentary about patient experience, as an alternative to more resource intensive analytic methods. METHODS: We collected free-text responses to a broad, open-ended question on patients' experience of primary care in a cross-sectional postal survey of patients recently consulting doctors in 25 English general practices. We encoded the responses to text files which were then uploaded to three Web-based textual processing tools. The tools we used were two text cloud creators: TagCrowd for unigrams, and Many Eyes for bigrams; and Voyant Tools, a Web-based reading tool that can extract distinctive words and perform Keyword in Context (KWIC) analysis. The association of patients' experience scores with the occurrence of certain words was tested with logistic regression analysis. KWIC analysis was also performed to gain insight into the use of a significant word. RESULTS: In total, 3426 free-text responses were received from 7721 patients (comment rate: 44.4%). The five most frequent words in the patients' comments were "doctor", "appointment", "surgery", "practice", and "time". The three most frequent two-word combinations were "reception staff", "excellent service", and "two weeks". The regression analysis showed that the occurrence of the word "excellent" in the comments was significantly associated with a better patient experience (OR=1.96, 95%CI=1.63-2.34), while "rude" was significantly associated with a worse experience (OR=0.53, 95%CI=0.46-0.60). The KWIC results revealed that 49 of the 78 (63%) occurrences of the word "rude" in the comments were related to receptionists and 17(22%) were related to doctors. CONCLUSIONS: Web-based text processing tools can extract useful information from free-text comments and the output may serve as a springboard for further investigation. Text clouds, distinctive words extraction and KWIC analysis show promise in quick evaluation of unstructured patient feedback. The results are easily understandable, but may require further probing such as KWIC analysis to establish the context. Future research should explore whether more sophisticated methods of textual analysis (eg, sentiment analysis, natural language processing) could add additional levels of understanding

The coordination of cell growth during fission yeast mating requires Ras1-GTP hydrolysis

The spatial and temporal control of polarity is fundamental to the survival of all organisms. Cells define their polarity using highly conserved mechanisms that frequently rely upon the action of small GTPases, such as Ras and Cdc42. Schizosaccharomyces pombe is an ideal system with which to study the control of cell polarity since it grows from defined tips using Cdc42-mediated actin remodeling. Here we have investigated the importance of Ras1-GTPase activity for the coordination of polarized cell growth during fission yeast mating. Following pheromone stimulation, Ras1 regulates both a MAPK cascade and the activity of Cdc42 to enable uni-directional cell growth towards a potential mating partner. Like all GTPases, when bound to GTP, Ras1 adopts an active conformation returning to an inactive state upon GTP-hydrolysis, a process accelerated through interaction with negative regulators such as GAPs. Here we show that, at low levels of pheromone stimulation, loss of negative regulation of Ras1 increases signal transduction via the MAPK cascade. However, at the higher concentrations observed during mating, hyperactive Ras1 mutations promote cell death. We demonstrate that these cells die due to their failure to coordinate active Cdc42 into a single growth zone resulting in disorganized actin deposition and unsustainable elongation from multiple tips. These results provide a striking demonstration that the deactivation stage of Ras signaling is fundamentally important in modulating cell polarity

Interventions to improve antibiotic prescribing practices for hospital inpatients

Background Antibiotic resistance is a major public health problem. Infections caused by multidrug-resistant bacteria are associated with prolonged hospital stay and death compared with infections caused by susceptible bacteria. Appropriate antibiotic use in hospitals should ensure effective treatment of patients with infection and reduce unnecessary prescriptions. We updated this systematic review to evaluate the impact of interventions to improve antibiotic prescribing to hospital inpatients. Objectives To estimate the effectiveness and safety of interventions to improve antibiotic prescribing to hospital inpatients and to investigate the effect of two intervention functions: restriction and enablement. Search methods We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library), MEDLINE, and Embase. We searched for additional studies using the bibliographies of included articles and personal files. The last search from which records were evaluated and any studies identified incorporated into the review was January 2015. Selection criteria We included randomised controlled trials (RCTs) and non-randomised studies (NRS). We included three non-randomised study designs to measure behavioural and clinical outcomes and analyse variation in the effects: non- randomised trials (NRT), controlled before-after (CBA) studies and interrupted time series (ITS) studies. For this update we also included three additional NRS designs (case control, cohort, and qualitative studies) to identify unintended consequences. Interventions included any professional or structural interventions as defined by the Cochrane Effective Practice and Organisation of Care Group. We defined restriction as 'using rules to reduce the opportunity to engage in the target behaviour (or increase the target behaviour by reducing the opportunity to engage in competing behaviours)'. We defined enablement as 'increasing means/reducing barriers to increase capability or opportunity'. The main comparison was between intervention and no intervention. Data collection and analysis Two review authors extracted data and assessed study risk of bias. We performed meta-analysis and meta-regression of RCTs and meta-regression of ITS studies. We classified behaviour change functions for all interventions in the review, including those studies in the previously published versions. We analysed dichotomous data with a risk difference (RD). We assessed certainty of evidence with GRADE criteria. Main results This review includes 221 studies (58 RCTs, and 163 NRS). Most studies were from North America (96) or Europe (87). The remaining studies were from Asia (19), South America (8), Australia (8), and the East Asia (3). Although 62% of RCTs were at a high risk of bias, the results for the main review outcomes were similar when we restricted the analysis to studies at low risk of bias. More hospital inpatients were treated according to antibiotic prescribing policy with the intervention compared with no intervention based on 29 RCTs of predominantly enablement interventions (RD 15%, 95% confidence interval (CI) 14% to 16%; 23,394 participants; high-certainty evidence). This represents an increase from 43% to 58% .There were high levels of heterogeneity of effect size but the direction consistently favoured intervention. The duration of antibiotic treatment decreased by 1.95 days (95% CI 2.22 to 1.67; 14 RCTs; 3318 participants; high-certainty evidence) from 11.0 days. Information from non-randomised studies showed interventions to be associated with improvement in prescribing according to antibiotic policy in routine clinical practice, with 70% of interventions being hospital-wide compared with 31% for RCTs. The risk of death was similar between intervention and control groups (11% in both arms), indicating that antibiotic use can likely be reduced without adversely affecting mortality (RD 0%, 95% CI -1% to 0%; 28 RCTs; 15,827 participants; moderate-certainty evidence). Antibiotic stewardship interventions probably reduce length of stay by 1.12 days (95% CI 0.7 to 1.54 days; 15 RCTs; 3834 participants; moderate-certainty evidence). One RCT and six NRS raised concerns that restrictive interventions may lead to delay in treatment and negative professional culture because of breakdown in communication and trust between infection specialists and clinical teams (low-certainty evidence). Both enablement and restriction were independently associated with increased compliance with antibiotic policies, and enablement enhanced the effect of restrictive interventions (high-certainty evidence). Enabling interventions that included feedback were probably more effective than those that did not (moderate-certainty evidence). There was very low-certainty evidence about the effect of the interventions on reducing Clostridium difficile infections (median -48.6%, interquartile range -80.7% to -19.2%; 7 studies). This was also the case for resistant gram-negative bacteria (median -12.9%, interquartile range -35.3% to 25.2%; 11 studies) and resistant gram-positive bacteria (median -19.3%, interquartile range -50.1% to +23.1%; 9 studies). There was too much variance in microbial outcomes to reliably assess the effect of change in antibiotic use. Heterogeneity of intervention effect on prescribing outcomes We analysed effect modifiers in 29 RCTs and 91 ITS studies. Enablement and restriction were independently associated with a larger effect size (high-certainty evidence). Feedback was included in 4 (17%) of 23 RCTs and 20 (47%) of 43 ITS studies of enabling interventions and was associated with greater intervention effect. Enablement was included in 13 (45%) of 29 ITS studies with restrictive interventions and enhanced intervention effect. Authors' conclusions We found high-certainty evidence that interventions are effective in increasing compliance with antibiotic policy and reducing duration of antibiotic treatment. Lower use of antibiotics probably does not increase mortality and likely reduces length of stay. Additional trials comparing antibiotic stewardship with no intervention are unlikely to change our conclusions. Enablement consistently increased the effect of interventions, including those with a restrictive component. Although feedback further increased intervention effect, it was used in only a minority of enabling interventions. Interventions were successful in safely reducing unnecessary antibiotic use in hospitals, despite the fact that the majority did not use the most effective behaviour change techniques. Consequently, effective dissemination of our findings could have considerable health service and policy impact. Future research should instead focus on targeting treatment and assessing other measures of patient safety, assess different stewardship interventions, and explore the barriers and facilitators to implementation. More research is required on unintended consequences of restrictive interventions

Approximating a Behavioural Pseudometric without Discount for<br> Probabilistic Systems

Desharnais, Gupta, Jagadeesan and Panangaden introduced a family of behavioural pseudometrics for probabilistic transition systems. These pseudometrics are a quantitative analogue of probabilistic bisimilarity. Distance zero captures probabilistic bisimilarity. Each pseudometric has a discount factor, a real number in the interval (0, 1]. The smaller the discount factor, the more the future is discounted. If the discount factor is one, then the future is not discounted at all. Desharnais et al. showed that the behavioural distances can be calculated up to any desired degree of accuracy if the discount factor is smaller than one. In this paper, we show that the distances can also be approximated if the future is not discounted. A key ingredient of our algorithm is Tarski's decision procedure for the first order theory over real closed fields. By exploiting the Kantorovich-Rubinstein duality theorem we can restrict to the existential fragment for which more efficient decision procedures exist