A Combined High-Efficiency Region Controller to Improve Fuel Consumption of Power-Split HEVs

An improved controller for the energy management system of a power-split hybrid electric vehicle (HEV) is developed with the objectives of minimizing fuel consumption and improving drivability. Considering the specific application of vehicles plying on scheduled trips such as public transport, this paper assumes that the controller is privileged with a priori knowledge of the estimated total tractive energy requirement and the duration of the journey. In comparison to a recently introduced constant high-efficiency region (CHER)-based controller, this paper demonstrates that further reductions in fuel consumption can be achieved under certain driving cycles by limiting the internal-combustion-engine (ICE) operation to a dynamically varying high-efficiency region and adopting state-of-charge (SOC) swing control for battery energy storage. The frequency of engine on/off is therefore directly decided by the size of the energy storage, allowable swing of the SOC, and the tractive energy required. Performances of the CHER and dynamic high-efficiency region (DHER) controllers are compared through simulations against the existing controller of a commercial vehicle. The results reveal that the DHER controller outperforms the other two controllers in terms of fuel consumption in highway-style-driving scenarios. Therefore, to minimize fuel consumption while improving drivability under all driving scenarios, this paper proposes to combine the CHER controller with the DHER controller such that the best features of both controllers can be utilized

PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease.

BACKGROUND: Despite the availability of effective drug therapies that reduce low-density lipoprotein (LDL)-cholesterol (LDL-C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL-C reduction may be warranted, especially for patients who are unresponsive to, or unable to take, existing LDL-C-reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) may further reduce LDL-C, potentially reducing CVD risk as well. OBJECTIVES: Primary To quantify short-term (24 weeks), medium-term (one year), and long-term (five years) effects of PCSK9 inhibitors on lipid parameters and on the incidence of CVD. Secondary To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of type 2 diabetes, cognitive function, and cancer. Additionally, to determine if specific patient subgroups were more or less likely to benefit from the use of PCSK9 inhibitors. SEARCH METHODS: We identified studies by systematically searching the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and Web of Science. We also searched Clinicaltrials.gov and the International Clinical Trials Registry Platform and screened the reference lists of included studies. We identified the studies included in this review through electronic literature searches conducted up to May 2016, and added three large trials published in March 2017. SELECTION CRITERIA: All parallel-group and factorial randomised controlled trials (RCTs) with a follow-up time of at least 24 weeks were eligible. DATA COLLECTION AND ANALYSIS: Two review authors independently reviewed and extracted data. When data were available, we calculated pooled effect estimates. MAIN RESULTS: We included 20 studies with data on 67,237 participants (median age 61 years; range 52 to 64 years). Twelve trials randomised participants to alirocumab, three trials to bococizumab, one to RG7652, and four to evolocumab. Owing to the small number of trials using agents other than alirocumab, we did not differentiate between types of PCSK9 inhibitors used. We compared PCSK9 inhibitors with placebo (thirteen RCTs), ezetimibe (two RCTs) or ezetimibe and statins (five RCTs).Compared with placebo, PCSK9 inhibitors decreased LDL-C by 53.86% (95% confidence interval (CI) 58.64 to 49.08; eight studies; 4782 participants; GRADE: moderate) at 24 weeks; compared with ezetimibe, PCSK9 inhibitors decreased LDL-C by 30.20% (95% CI 34.18 to 26.23; two studies; 823 participants; GRADE: moderate), and compared with ezetimibe and statins, PCSK9 inhibitors decreased LDL-C by 39.20% (95% CI 56.15 to 22.26; five studies; 5376 participants; GRADE: moderate).Compared with placebo, PCSK9 inhibitors decreased the risk of CVD events, with a risk difference (RD) of 0.91% (odds ratio (OR) of 0.86, 95% CI 0.80 to 0.92; eight studies; 59,294 participants; GRADE: moderate). Compared with ezetimibe and statins, PCSK9 inhibitors appeared to have a stronger protective effect on CVD risk, although with considerable uncertainty (RD 1.06%, OR 0.45, 95% CI 0.27 to 0.75; three studies; 4770 participants; GRADE: very low). No data were available for the ezetimibe only comparison. Compared with placebo, PCSK9 probably had little or no effect on mortality (RD 0.03%, OR 1.02, 95% CI 0.91 to 1.14; 12 studies; 60,684 participants; GRADE: moderate). Compared with placebo, PCSK9 inhibitors increased the risk of any adverse events (RD 1.54%, OR 1.08, 95% CI 1.04 to 1.12; 13 studies; 54,204 participants; GRADE: low). Similar effects were observed for the comparison of ezetimibe and statins: RD 3.70%, OR 1.18, 95% CI 1.05 to 1.34; four studies; 5376 participants; GRADE: low. Clinical event data were unavailable for the ezetimibe only comparison. AUTHORS' CONCLUSIONS: Over short-term to medium-term follow-up, PCSK9 inhibitors reduced LDL-C. Studies with medium-term follow-up time (longest median follow-up recorded was 26 months) reported that PCSK9 inhibitors (compared with placebo) decreased CVD risk but may have increased the risk of any adverse events (driven by SPIRE-1 and -2 trials). Available evidence suggests that PCSK9 inhibitor use probably leads to little or no difference in mortality. Evidence on relative efficacy and safety when PCSK9 inhibitors were compared with active treatments was of low to very low quality (GRADE); follow-up times were short and events were few. Large trials with longer follow-up are needed to evaluate PCSK9 inhibitors versus active treatments as well as placebo. Owing to the predominant inclusion of high-risk patients in these studies, applicability of results to primary prevention is limited. Finally, estimated risk differences indicate that PCSK9 inhibitors only modestly change absolute risks (often to less than 1%)

A document classifier for medicinal chemistry publications trained on the ChEMBL corpus

Background  The large increase in the number of scientific publications has fuelled a need for semi- and fully automated text mining approaches in order to assist in the triage process, both for individual scientists and also for larger-scale data extraction and curation into public databases. Here, we introduce a document classifier, which is able to successfully distinguish between publications that are ‘ChEMBL-like’ (i.e. related to small molecule drug discovery and likely to contain quantitative bioactivity data) and those that are not. The unprecedented size of the medicinal chemistry literature collection, coupled with the advantage of manual curation and mapping to chemistry and biology make the ChEMBL corpus a unique resource for text mining.  Results  The method has been implemented as a data protocol/workflow for both Pipeline Pilot (version 8.5) and KNIME (version 2.9) respectively. Both workflows and models are freely available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/text-mining webcite. These can be readily modified to include additional keyword constraints to further focus searches.  Conclusions  Large-scale machine learning document classification was shown to be very robust and flexible for this particular application, as illustrated in four distinct text-mining-based use cases. The models are readily available on two data workflow platforms, which we believe will allow the majority of the scientific community to apply them to their own data.FWN – Publicaties zonder aanstelling Universiteit Leide

Flipping the odds of drug development success through human genomics

Drug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies. Errors in drug target specification contribute to the extremely high rates of drug development failure. Integrating knowledge of genes that encode druggable targets with those that influence susceptibility to common disease has the potential to radically improve the probability of drug development success

Distributed optical fibre sensing in synthetic fibre ropes and cables

Distributed strain measurements on synthetic fibre ropes as used in marine applications are reported. The ropes incorporate single mode fibre-optic sensors for strain measurement. A Brillouin-amplification-based distributed strain measuring system has been utilised to interrogate the fibre sensors incorporated into a parallel yarn aramid rope. Initial results are presented to conclusively demonstrate the technical feasibility of the approach

Optical fiber sensing of strain in synthetic fiber tension members: recent results

Strain measurements on synthetic fibre rope constructions ofthe type used in marine applications are reported. The ropes are of a novel construction by virtue of incorporation of fibre-optic sensors for strain measurement. Two instrumentation approaches -a Brillouin strain measuring system and a RF sub-carrier strain measuring system -have been utilised to interrogate the fibre sensors

Supervised selective kernel fusion for membrane protein prediction

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

The systematic annotation of the three main GPCR families in Reactome

Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand–receptor interactions to molecular readouts at the level of metabolic and transcriptional events. To this end, we have built the first catalogue of all human G protein-coupled receptors (GPCRs) known to bind endogenous or natural ligands. The UniProt database has records for 797 proteins classified as GPCRs and sorted into families A/1, B/2 and C/3 on the basis of amino accid sequence. To these records we have added details from the IUPHAR database and our own manual curation of relevant literature to create reactions in which 563 GPCRs bind ligands and also interact with specific G-proteins to initiate signalling cascades. We believe the remaining 234 GPCRs are true orphans. The Reactome GPCR pathway can be viewed as a detailed interactive diagram and can be exported in many forms. It provides a template for the orthology-based inference of GPCR reactions for diverse model organism species, and can be overlaid with protein–protein interaction and gene expression datasets to facilitate overrepresentation studies and other forms of pathway analysis