The positive soundscape project : a synthesis of results from many disciplines

This paper takes an overall view of ongoing findings from the Positive Soundscape Project, a large inter-disciplinary soundscapes study which is nearing completion. Qualitative fieldwork (soundwalks and focus groups) and lab-based listening tests have revealed that two key dimensions of the emotional response are calmness and vibrancy. In the lab these factors explain nearly 80% of the variance in listener response. Physiological validation is being sought using fMRI measurements, and these have so far shown significant differences in the response of the brain to affective and neutral soundscapes. A conceptual framework which links the key soundscape components and which could be used for future design is outlined. Metrics are suggested for some perceptual scales and possibilities for soundscape synthesis for design and user engagement are discussed, as are the applications of the results to future research and environmental noise policy

Imputation versus prediction: applications in machine learning for drug discovery

Imputation is a powerful statistical method that is distinct from the predictive modelling techniques more commonly used in drug discovery. Imputation uses sparse experimental data in an incomplete dataset to predict missing values by leveraging correlations between experimental assays. This contrasts with quantitative structure–activity relationship methods that use only descriptor – assay correlations. We summarize three recent imputation strategies – heterogeneous deep imputation, assay profile methods and matrix factorization – and compare these with quantitative structure–activity relationship methods, including deep learning, in drug discovery settings. We comment on the value added by imputation methods when used in an ongoing project and find that imputation produces stronger models, earlier in the project, over activity and absorption, distribution, metabolism and elimination end points. </jats:p

Evolution of the mammalian lysozyme gene family

<p>Abstract</p> <p>Background</p> <p>Lysozyme <it>c </it>(chicken-type lysozyme) has an important role in host defense, and has been extensively studied as a model in molecular biology, enzymology, protein chemistry, and crystallography. Traditionally, lysozyme <it>c </it>has been considered to be part of a small family that includes genes for two other proteins, lactalbumin, which is found only in mammals, and calcium-binding lysozyme, which is found in only a few species of birds and mammals. More recently, additional testes-expressed members of this family have been identified in human and mouse, suggesting that the mammalian lysozyme gene family is larger than previously known.</p> <p>Results</p> <p>Here we characterize the extent and diversity of the lysozyme gene family in the genomes of phylogenetically diverse mammals, and show that this family contains at least eight different genes that likely duplicated prior to the diversification of extant mammals. These duplicated genes have largely been maintained, both in intron-exon structure and in genomic context, throughout mammalian evolution.</p> <p>Conclusions</p> <p>The mammalian lysozyme gene family is much larger than previously appreciated and consists of at least eight distinct genes scattered around the genome. Since the lysozyme <it>c </it>and lactalbumin proteins have acquired very different functions during evolution, it is likely that many of the other members of the lysozyme-like family will also have diverse and unexpected biological properties.</p

Deep imputation on large‐scale drug discovery data

More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success-rate of pharmaceutical R&D. However this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure-activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest-to-date successful application of deep-learning imputation to datasetswhich arecomparablein sizetothe corporate data repository of a pharmaceutical company (678,994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; i) target activity data compiled from a range of drug discovery projects, ii) a high value and heterogeneous datasetcovering complex absorption, distribution, metabolism and elimination properties and, iii) high throughput screeningdata, testing thealgorithm’slimits on early-stage noisy and very sparse data.Achieving median coefficients of determination, 2, of 0.69, 0.36 and 0.43 respectively across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median 2 values of 0.28, 0.19 and 0.23 respectively.We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision-making based on the imputed values.Optibrium Ltd, Intellegens Ltd, Takeda, Royal Societ

GABA receptor associated protein changes the electrostatic environment around the GABA type A receptor.

Funder: Science and Technology Facilities Council; Id: http://dx.doi.org/10.13039/501100000271We have performed fully atomistic molecular dynamics simulations of the intracellular domain of a model of the GABAA receptor with and without the GABA receptor associated protein (GABARAP) bound. We have also calculated the electrostatic potential due to the receptor, in the absence and presence of GABARAP. We find that GABARAP binding changes the electrostatic properties around the GABAA receptor and could lead to increased conductivity of chloride ions through the receptor. We also find that ion motions that would result in conducting currents are observed nearly twice as often when GABARAP binds. These results are consistent with data from electrophysiological experiments

Ischemia monitoring in off-pump coronary artery bypass surgery using intravascular near-infrared spectroscopy

BACKGROUND: In off-pump coronary artery bypass surgery, manipulations on the beating heart can lead to transient interruptions of myocardial oxygen supply, which can generate an accumulation of oxygen-dependent metabolites in coronary venous blood. The objective of this study was to evaluate the reliability of intravascular near-infrared spectroscopy as a monitoring method to detect possible ischemic events in off-pump coronary artery bypass procedures. METHODS: In 15 elective patients undergoing off-pump myocardial revascularization, intravascular near-infrared spectroscopic analysis of coronary venous blood was performed. NIR signals were transferred through a fiberoptic catheter for signal emission and collection. For data analysis and processing, a miniature spectrophotometer with multivariate statistical package was used. Signal acquisition and analysis were performed before and after revascularization. Spectroscopic data were compared with hemodynamic parameters, electrocardiogram, transesophageal echocardiography and laboratory findings. RESULTS: A conversion to extracorporeal circulation was not necessary. The mean number of grafts per patient was 3.1 ± 0.6. An intraoperative myocardial ischemia was not evident, as indicated by electrocardiogram and transesophageal echocardiography. Continuous spectroscopic analysis showed reproducible absorption spectra of coronary sinus blood. Due to uneventful intraoperative courses, clear ischemia-related changes could be detected in none of the patients. CONCLUSION: Our initial results show that intravascular near-infrared spectroscopy can reliably be used for an online intraoperative ischemia monitoring in off-pump coronary artery bypass surgery. However, the method has to be further evaluated and standardized to determine the role of spectroscopy in off-pump coronary artery bypass surgery

Marginalization of end-use technologies in energy innovation for climate protection

Mitigating climate change requires directed innovation efforts to develop and deploy energy technologies. Innovation activities are directed towards the outcome of climate protection by public institutions, policies and resources that in turn shape market behaviour. We analyse diverse indicators of activity throughout the innovation system to assess these efforts. We find efficient end-use technologies contribute large potential emission reductions and provide higher social returns on investment than energy-supply technologies. Yet public institutions, policies and financial resources pervasively privilege energy-supply technologies. Directed innovation efforts are strikingly misaligned with the needs of an emissions-constrained world. Significantly greater effort is needed to develop the full potential of efficient end-use technologies

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties

Phylogenetic relationships within Chamaecrista sect. Xerocalyx (Leguminosae, Caesalpinioideae) inferred from the cpDNA trnE-trnT intergenic spacer and nrDNA ITS sequences

Chamaecrista belongs to subtribe Cassiinae (Caesalpinioideae), and it comprises over 330 species, divided into six sections. The section Xerocalyx has been subjected to a profound taxonomic shuffling over the years. Therefore, we conducted a phylogenetic analysis using a cpDNA trnE-trnT intergenic spacer and nrDNA ITS/5.8S sequences from Cassiinae taxa, in an attempt to elucidate the relationships within this section from Chamaecrista. The tree topology was congruent between the two data sets studied in which the monophyly of the genus Chamaecrista was strongly supported. Our analyses reinforce that new sectional boundaries must be defined in the Chamaecrista genus, especially the inclusion of sections Caliciopsis and Xerocalyx in sect. Chamaecrista, considered here paraphyletic. The section Xerocalyx was strongly supported as monophyletic; however, the current data did not show C. ramosa (microphyllous) and C. desvauxii (macrophyllous) and their respective varieties in distinct clades, suggesting that speciation events are still ongoing in these specimens