Strand-biased gene distribution, purine assymetry and environmental factors influence protein evolution in Bacillus

AbstractA strong purine asymmetry, along with strand-biased gene distribution and the presence of PolC, prevails in Bacillus and some other members of Firmicutes, Fusobacteria and Tenericutes. The analysis of protein features in 21 Bacillus species of diverse metabolic, virulence and ecological traits revealed that purine asymmetry in conjunction with lineage/niche specific constraints significantly influences protein evolution in Bacillus. All Bacillus species, except for Se-respiring Bacillus selenitireducens, display distinct strand-specific biases in amino acid usage, which may affect the isoelectric point or surface charge distribution of proteins with prevalence of acidic and basic residues in the leading and lagging strand proteins, respectively

The impact of different fertiliser management options and cultivars on nitrogen use efficiency and yield for rice cropping in the Indo-Gangetic Plain: two seasons of methane, nitrous oxide and ammonia emissions

This study presents detailed crop and gas flux data from two years of rice production at the experimental farm of the ICAR-Indian Agricultural Research Institute, New Delhi, India. In comparing 4 nitrogen (N) fertiliser regimes across 4 rice cultivars (CRD 310, IR-64, MTU 1010, P-44), we have added to growing evidence of the environmental costs of rice production in the region. The study shows that rice cultivar can impact yields of both grain, and total biomass produced in given circumstances, with the CRD 310 cultivar showing consistently high nitrogen use efficiency (NUE) for total biomass compared with other tested varieties, but not necessarily with the highest grain yield, which was P-44 in this experiment. While NUE of the rice did vary depending on experimental treatments (ranging from 41% to 73%), 73%), this did not translate directly into the reduction of emissions of ammonia (NH3) and nitrous oxide (N2O). Emissions were relatively similar across the different rice cultivars regardless of NUE. Conversely, agronomic practices that reduced total N losses were associated with higher yield. In terms of fertiliser application, the outstanding impact was of the very high methane (CH4) emissions as a result of incorporating farmyard manure (FYM) into rice paddies, which dominated the overall effect on global warming potential. While the use of nitrification and urease inhibiting substances decreased N2O emissions overall, NH3 emissions were relatively unaffected (or slightly higher). Overall, the greatest reduction in greenhouse gas (GHG) emissions came from reducing irrigation water added to the fields, resulting in higher N2O, but significantly less CH4 emissions, reducing net GHG emission compared with continuous flooding. Overall, genetic differences generated more variation in yield and NUE than agronomic management (excluding controls), whereas agronomy generated larger differences than genetics concerning gaseous losses. This study suggests that a mixed approach needs to be applied when attempting to reduce pollution and global warming potential from rice production and potential pollution swapping and synergies need to be considered. Finding the right balance of rice cultivar, irrigation technique and fertiliser type could significantly reduce emissions, while getting it wrong can result in considerably poorer yields and higher pollution

Effect of educational outreach on general practice prescribing of antibiotics and antidepressants: a two-year randomised controlled trial

Objective. Prescribing of broad spectrum antibiotics and antidepressants in general practice often does not accord with guidelines. The aim was to determine the effectiveness of educational outreach in improving the prescribing of selected antibiotics and antidepressants, and whether the effect is sustained for two years. Design. Single blind randomized trial. Setting. Twenty-eight general practices in Leicestershire, England. Intervention. Educational outreach visits were undertaken, tailored to barriers to change, 14 practices receiving visits for reducing selected antibiotics and 14 for improving antidepressant prescribing. Main outcome measures. Number of items prescribed per 1000 registered patients for amoxicillin with clavulanic acid (co-amoxiclav) and quinolone antibiotics, and average daily quantities per 1000 patients for lofepramine and fluoxetine antidepressants, measured at the practice level for six-month periods over two years. Results. There was no effect on the prescribing of co-amoxiclav, quinolones, or fluoxetine, but prescribing of lofepramine increased in accordance with the guidelines. The increase persisted throughout two years of follow-up. Conclusion. A simple, group-level educational outreach intervention, designed to take account of identified barriers to change, can have a modest but sustained effect on prescribing levels. However, outreach is not always effective. The context in which change in prescribing practice is being sought, the views of prescribers concerning the value of the drug, or other unrecognised barriers to change may influence the effectiveness of outreach

Genomic surveillance uncovers a pandemic clonal lineage of the wheat blast fungus

Wheat, one of the most important food crops, is threatened by a blast disease pandemic. Here, we show that a clonal lineage of the wheat blast fungus recently spread to Asia and Africa following two independent introductions from South America. Through a combination of genome analyses and laboratory experiments, we show that the decade-old blast pandemic lineage can be controlled by the Rmg8 disease resistance gene and is sensitive to strobilurin fungicides. However, we also highlight the potential of the pandemic clone to evolve fungicide-insensitive variants and sexually recombine with African lineages. This underscores the urgent need for genomic surveillance to track and mitigate the spread of wheat blast outside of South America and to guide preemptive wheat breeding for blast resistance

Consensus Recommendation for Mouse Models of Ocular Hypertension to Study Aqueous Humor Outflow and Its Mechanisms.

Due to their similarities in anatomy, physiology, and pharmacology to humans, mice are a valuable model system to study the generation and mechanisms modulating conventional outflow resistance and thus intraocular pressure. In addition, mouse models are critical for understanding the complex nature of conventional outflow homeostasis and dysfunction that results in ocular hypertension. In this review, we describe a set of minimum acceptable standards for developing, characterizing, and utilizing mouse models of open-angle ocular hypertension. We expect that this set of standard practices will increase scientific rigor when using mouse models and will better enable researchers to replicate and build upon previous findings

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

MINIMIZATION OF WORK IN PROCESS INVENTORY IN HYBRID FLOW SHOP SCHEDULING USING FUZZY LOGIC

This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the total work in process inventory. Job scheduling problems are one of the oldest and real world combinational optimization problems. It is multi objective and complex in nature. There exist some criteria that must be taken into consideration when evaluating the quality of the proposed schedule. Consideration of job and machine reliability is very important during assignment of jobs in each stage to get realistic hybrid flow shop schedule.  In this paper, flow shop problem concerns the sequencing of a given number of jobs through a series of machines in the exact same order on all machines with the aim to satisfy a set of constraint as much as possible and optimize a set of objectives. Fuzzy sets and logic can be used to tackle uncertainties inherent in actual flow shop scheduling problems.  Fuzzy due dates, cost over time and profit rate result the job priority and to determine the machine priority processing time of each machine is considered. MATLAB fuzzy tool box is used to calculate the priorities of jobs and machines at different stages. Finally, jobs are assigned into machines based on a grouping and sequencing algorithm that minimizes the total work in process inventory

Selection of the optimal number of shifts in fuzzy environment: manufacturing company’s facility application

This paper addresses the selection of optimal shift numbers considering inventory information, customer requirements and machine reliability using fuzzy logic. Number of shift is one of the most important criteria for the production planners to minimize the production costs and is essential for appropriate production planning. The main task involves optimizing the shift periods considering constraints of raw material, due date, demand, finished goods inventory and machine breakdown. A model is developed for any kind of manufacturing company where shift periods affect company’s profit and cost. Fuzzy control is used to optimize the number of shifts under the constraints of raw material, due date, demand, finished goods inventory and machine breakdown. MATLAB Fuzzy Logic Tool Box is used to develop the model