Antihypertensive drug class and dyslipidemia: risk association among Chinese patients with uncomplicated hypertension

Factors associated with dyslipidemia in Chinese patients with uncomplicated hypertension were investigated in 1,139 patients newly prescribed a single antihypertensive drug in the public primary healthcare setting in Hong Kong, where their fasting lipid profiles were measured 4 to 16 weeks after the first prescription. Multivariate logistic regression showed that thiazide users were more likely (OR 3.67, 95% C.I. 1.13, 11.88, p=0.030) to have adverse (> 6.2mmol/l) total cholesterol (TC) compared with drugs acting on the renin angiotensin system (RAS), but the absolute difference in mean TC between thiazide users and all patients was small ( 0.14 mmol/l), while advanced age and male gender were also associated with some aspects of dyslipidemia. Clinicians should be aware of the increased risk of dyslipidemia in these groups, but the mild dyslipidemic profile associated with thiazides should not in itself deter its use as a possible first-line antihypertensive agent among Chinese patients

Identification and microbial production of a terpene-based advanced biofuel

Rising petroleum costs, trade imbalances and environmental concerns have stimulated efforts to advance the microbial production of fuels from lignocellulosic biomass. Here we identify a novel biosynthetic alternative to D2 diesel fuel, bisabolane, and engineer microbial platforms for the production of its immediate precursor, bisabolene. First, we identify bisabolane as an alternative to D2 diesel by measuring the fuel properties of chemically hydrogenated commercial bisabolene. Then, via a combination of enzyme screening and metabolic engineering, we obtain a more than tenfold increase in bisabolene titers in Escherichia coli to >900 mg l−1. We produce bisabolene in Saccharomyces cerevisiae (>900 mg l−1), a widely used platform for the production of ethanol. Finally, we chemically hydrogenate biosynthetic bisabolene into bisabolane. This work presents a framework for the identification of novel terpene-based advanced biofuels and the rapid engineering of microbial farnesyl diphosphate-overproducing platforms for the production of biofuels

Stochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops

Control loops are the most critical components in many production processes. In this process, the economic yield is strongly linked to the performance of the control loops since aspects such as safety conditions, process quality, and energy and raw material consumption depend on this. However, experience has shown that most of the control loops can be improved by identifying and correcting the causes of the poor perfor-mance. The indices to evaluate the performance of the control loops can be divided into two groups, stochastic and deterministic. The most known of the former is the minimum variance index. Stochastic indices only require data collected under normal operating conditions and minimum knowledge of the process, making it possible to evaluate performance online. However, some disadvantages, such as scale and span problems, make performance analysis difficult. The deterministic indices (rise time, settling time, overshoot, phase and gain margins, etc.) are easy to interpret, facilitating the analysis; however, invasive plant tests are necessary to estimate them, making them impractical. Is it possible to link these two approaches? With that question in mind, in this work, it is proposed to build a model to estimate deterministic indices (to evaluate robustness and performance of control loops), considering stochastic indices and some process information as model inputs. This paper shows the procedure to build the inferential model by using machine learning techniques

BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts

Abstract Summary: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment. Availability and implementation: BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p

NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline.

BACKGROUND: Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response. RESULTS: Here we offer NeoPredPipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on predicted neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. CONCLUSIONS: Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe

Removal and mixing of the coronal gas from satellites in galaxy groups: cooling the intragoup gas

The existence of an extended hot gaseous corona surrounding clusters, groups and massive galaxies is well established by observational evidence and predicted by current theories of galaxy formation. When a small galaxy collides with a larger one, their coronae are the first to interact, producing disturbances that remove gas from the smaller system and settle it into the corona of the larger one. For a Milky-Way-size galaxy merging into a low-mass group, ram pressure stripping and the Kelvin-Helmholtz instability are the most relevant of these disturbances. We argue that the turbulence generated by the latter mixes the material of both coronae in the wake of the orbiting satellite creating a "warm phase" mixture with a cooling time a factor of several shorter than that of the ambient intragroup gas. We reach this conclusion using analytic estimates, as well as adiabatic and dissipative high resolution numerical simulations of a spherical corona subject to the ablation process of a constant velocity wind with uniform density and temperature. Although this is a preliminary analysis, our results are promising and we speculate that the mixture could potentially trigger in situ star formation and/or be accreted into the central galaxy as a cold gas flow resulting in a new mode of star formation in galaxy groups and clusters.Comment: 15 pages, 5 figures, accepted for publication in MNRA

Reproducibility of the airway response to an exercise protocol standardized for intensity, duration, and inspired air conditions, in subjects with symptoms suggestive of asthma

<p>Abstract</p> <p>Background</p> <p>Exercise testing to aid diagnosis of exercise-induced bronchoconstriction (EIB) is commonly performed. Reproducibility of the airway response to a standardized exercise protocol has not been reported in subjects being evaluated with mild symptoms suggestive of asthma but without a definite diagnosis. This study examined reproducibility of % fall in FEV₁and area under the FEV₁time curve for 30 minutes in response to two exercise tests performed with the same intensity and duration of exercise, and inspired air conditions.</p> <p>Methods</p> <p>Subjects with mild symptoms of asthma exercised twice within approximately 4 days by running for 8 minutes on a motorized treadmill breathing dry air at an intensity to induce a heart rate between 80-90% predicted maximum; reproducibility of the airway response was expressed as the 95% probability interval.</p> <p>Results</p> <p>Of 373 subjects challenged twice 161 were positive (≥10% fall FEV₁on at least one challenge). The EIB was mild and 77% of subjects had <15% fall on both challenges. Agreement between results was 76.1% with 56.8% (212) negative (< 10% fall FEV₁) and 19.3% (72) positive on both challenges. The remaining 23.9% of subjects had only one positive test. The 95% probability interval for reproducibility of the % fall in FEV₁and AUC_0-30min was ± 9.7% and ± 251% for all 278 adults and ± 13.4% and ± 279% for all 95 children. The 95% probability interval for reproducibility of % fall in FEV₁and AUC_{0-30 min}for the 72 subjects with two tests ≥10% fall FEV₁was ± 14.6% and ± 373% and for the 34 subjects with two tests ≥15% fall FEV₁it was ± 12.2% and ± 411%. Heart rate and estimated ventilation achieved were not significantly different either on the two test days or when one test result was positive and one was negative.</p> <p>Conclusions</p> <p>Under standardized, well controlled conditions for exercise challenge, the majority of subjects with mild symptoms of asthma demonstrated agreement in test results. Performing two tests may need to be considered when using exercise to exclude or diagnose EIB, when prescribing prophylactic treatment to prevent EIB and when designing protocols for clinical trials.</p

Exploring differential item functioning in the SF-36 by demographic, clinical, psychological and social factors in an osteoarthritis population

The SF-36 is a very commonly used generic measure of health outcome in osteoarthritis (OA). An important, but frequently overlooked, aspect of validating health outcome measures is to establish if items work in the same way across subgroup of a population. That is, if respondents have the same 'true' level of outcome, does the item give the same score in different subgroups or is it biased towards one subgroup or another. Differential item functioning (DIF) can identify items that may be biased for one group or another and has been applied to measuring patient reported outcomes. Items may show DIF for different conditions and between cultures, however the SF-36 has not been specifically examined in an osteoarthritis population nor in a UK population. Hence, the aim of the study was to apply the DIF method to the SF-36 for a UK OA population. The sample comprised a community sample of 763 people with OA who participated in the Somerset and Avon Survey of Health. The SF-36 was explored for DIF with respect to demographic, social, clinical and psychological factors. Well developed ordinal regression models were used to identify DIF items. Results: DIF items were found by age (6 items), employment status (6 items), social class (2 items), mood (2 items), hip v knee (2 items), social deprivation (1 item) and body mass index (1 item). Although the impact of the DIF items rarely had a significant effect on the conclusions of group comparisons, in most cases there was a significant change in effect size. Overall, the SF-36 performed well with only a small number of DIF items identified, a reassuring finding in view of the frequent use of the SF-36 in OA. Nevertheless, where DIF items were identified it would be advisable to analyse data taking account of DIF items, especially when age effects are the focus of interest

Synthetic RNA modules for fine-tuning gene expression levels in yeast by modulating RNase III activity

The design of synthetic gene networks requires an extensive genetic toolbox to control the activities and levels of protein components to achieve desired cellular functions. Recently, a novel class of RNA-based control modules, which act through post-transcriptional processing of transcripts by directed RNase III (Rnt1p) cleavage, were shown to provide predictable control over gene expression and unique properties for manipulating biological networks. Here, we increase the regulatory range of the Rnt1p control elements, by modifying a critical region for enzyme binding to its hairpin substrates, the binding stability box (BSB). We used a high throughput, cell-based selection strategy to screen a BSB library for sequences that exhibit low fluorescence and thus high Rnt1p processing efficiencies. Sixteen unique BSBs were identified that cover a range of protein expression levels, due to the ability of the sequences to affect the hairpin cleavage rate and to form active cleavable complexes with Rnt1p. We further demonstrated that the activity of synthetic Rnt1p hairpins can be rationally programmed by combining the synthetic BSBs with a set of sequences located within a different region of the hairpin that directly modulate cleavage rates, providing a modular assembly strategy for this class of RNA-based control elements

A review of wildland fire spread modelling, 1990-present, 1: Physical and quasi-physical models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behaviour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a physical or quasi-physical nature. These models are based on the fundamental chemistry and/or physics of combustion and fire spread. Other papers in the series review models of an empirical or quasi-empirical nature, and mathematical analogues and simulation models. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 31 pages + 8 pages references + 2 figures + 5 tables. Submitted to International Journal of Wildland Fir