Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The influence of greenhouse-integrated photovoltaics on crop production

Photovoltaics (PVs) have been particularly successful in many domestic and industrial settings where opaque PV-covered roofs provide renewable electricity. Modern farming, for an ever growing population, employs vast areas of greenhouses consuming considerable amounts of energy. The majority of greenhouses are not suited to coverage by opaque PVs. Herein, we describe the current-state-of-the-art in greenhouse-integrated opaque PVs and their limitations, particularly with respect to the compatibility with certain plant cultivars. We propose semi-transparent PVs (Dye-Sensitized solar Cells, DSCs) as alternative greenhouse glazing that, compared to conventional greenhouse glazing and currently marketed greenhouse integrated opaque PV materials, offers advantages including enhanced thermal stabilisation and similar or improved edible biomass yields. Large-scale-validation of DSCs in solar sharing for crop production (yield, appearance and nutritional content) is now in progress

Ruthenium(II) complexes of pyrrol-azo ligands: cytotoxicity, interaction with calf thymus DNA and bovine serum albumin

Two ruthenium(II) complexes of newly designed pyrrol-azo ligands(L) and bipyridine(bpy) formulated as [Ru(L)(bpy)2]ClO4, where HL1 = (4-chloro-phenyl)-(1H-pyrrol-2-yl)-diazene (1) complex 1 and HL2 = (4-nitro-phenyl)-(1H-pyrrol-2-yl)-diazene for 2, were isolated in pure form. The complexes were characterized by physicochemical and spectroscopic methods. The electrochemical behavior of the complexes showed the Ru(III)/Ru(II) couple at different potentials with quasi-reversible voltammograms. The study of cytotoxicity effects of 1 and 2 on human breast cancer cells (MCF 7, MDA-MB 231) and cervical cancer cell (HeLa) taking Cisplatin as a positive reference showed that 1 exhibited higher cytotoxicity against cancer cell lines than 2, but less activity than Cisplatin. The interaction of 1 with calf thymus DNA (CT-DNA) using absorption, emission spectral studies, viscosity-measurement, and electrochemical techniques has been used to determine the binding constant Kb and the linear Stern\u2013Volmer quenching constant KSV. The results indicate that 1 strongly interacts with CT-DNA in groove binding mode. The interaction of bovine serum albumin (BSA) with 1 was also investigated with the help of spectroscopic tools. Absorption spectroscopy proved the formation of a BSA-[Ru(L1)(bpy)2]ClO4 complex