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Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split (“Class-specific”). Via simulations with both real and simulated batch effects, we demonstrate that the “Class-specific” strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the “Class-specific” strategy.

Classical crystallography can determine structures as complicated as multi-component ribosomal assemblies with atomic resolution, but is inadequate for disordered systems—even those as simple as water ice—that occupy the complex middle ground between liquid-like randomness and crystalline periodic order. Correlated disorder nevertheless has clear crystallographic signatures that map to the type of disorder, irrespective of the underlying physical or chemical interactions and material involved. This mapping hints at a common language for disordered states that will help us to understand, control and exploit the disorder responsible for many interesting physical properties. Although classical crystallography is insufficient to determine disordered structure in crystals, correlated disorder does nevertheless contain clear crystallographic signatures that map to the type of disorder, which we are learning to decipher. The crystallography of disorder During the past century, crystallography has transformed our understanding of biological systems at the molecular level, with the crystalline structures of the eukaryotic ribosome, spliceosome, various transmembrane proteins and other multicomponent complexes characterized at atomic resolution. In this review David Keen and Andrew Goodwin consider one of the greatest challenges in modern crystallography: atomic structure in disordered states. The authors highlight the progress that has been made in probing and understanding correlated disorder, the complex middle ground between liquid-like randomness and crystalline order. They cite examples that demonstrate that correlated disorder can generate crystallographic signatures that map to the type of disorder, irrespective of the underlying physical or chemical interactions. This hints at a common language for elucidating the structure of, and driving forces for, disordered states — important first steps towards the ultimate goal of controlling and exploiting the disorder that is responsible for many interesting physical properties.

The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic and critical review of about one hundred and twenty-two (122) pertinent research works reported in academic journals over 11 years (2007–2018) in the area of stock market prediction using machine learning. The various techniques identified from these reports were clustered into three categories, namely technical, fundamental, and combined analyses. The grouping was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modelling. The results revealed that 66% of documents reviewed were based on technical analysis; whiles 23% and 11% were based on fundamental analysis and combined analyses, respectively. Concerning the number of data source, 89.34% of documents reviewed, used single sources; whiles 8.2% and 2.46% used two and three sources respectively. Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.

The authors show that rare genetic variants contribute to large gene expression changes across diverse human tissues and provide an integrative method for interpretation of rare variants in individual genomes. Genetic effects on gene expression across human tissues The GTEx (Genotype-Tissue Expression) Consortium has established a reference catalogue and associated tissue biobank for gene-expression levels across individuals for diverse tissues of the human body, with a broad sampling of normal, non-diseased human tissues from postmortem donors. The consortium now presents the deepest survey of gene expression across multiple tissues and individuals to date, encompassing 7,051 samples from 449 donors across 44 human tissues. Barbara Engelhardt and colleagues characterize the relationship between genetic variation and gene expression, and find that most genes are regulated by genetic variation near to the affected gene. In accompanying GTEx studies, Alexis Battle, Stephen Montgomery and colleagues examine the effect of rare genetic variation on gene expression across human tissues, Daniel MacArthur and colleagues systematically survey the landscape of X chromosome inactivation in human tissues, and Jin Billy Li and colleagues provide a comprehensive cross-species analysis of adenosine-to-inosine RNA editing in mammals. In an accompanying News & Views, Michelle Ward and Yoav Gilad put the latest results in context and discuss how these findings are helping to crack the regulatory code of the human genome. Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk 1 , 2 , 3 , 4 . While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants 1 , 5 . Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles 1 , 6 , 7 , but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues 8 , 9 , 10 , 11 , but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release 12 . We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Environmental stimuli often lead to heterogeneous cellular responses and transcriptional output. We developed single-cell RNA and Immunodetection (RAID) to allow combined analysis of the transcriptome and intracellular (phospho-)proteins from fixed single cells. RAID successfully recapitulated differentiation-state changes at the protein and mRNA level in human keratinocytes. Furthermore, we show that differentiated keratinocytes that retain high phosphorylated FAK levels, a feature associated with stem cells, also express a selection of stem cell associated transcripts. Our data demonstrates that RAID allows investigation of heterogeneous cellular responses to environmental signals at the mRNA and phospho-proteome level.

In 2003, Kechris, Pestov and Todorcevic showed that the structure of certain separable metric spaces - called ultrahomogeneous - is closely related to the combinatorial behavior of the class of their finite metric spaces. The purpose of the present paper is to explore different aspects of this connection.

Many proteins that bind specific DNA sequences search the genome by combining three-dimensional diffusion with one-dimensional sliding on nonspecific DNA 1 – 5 . Here we combine resonance energy transfer and fluorescence correlation measurements to characterize how individual lac repressor (LacI) molecules explore the DNA surface during the one-dimensional phase of target search. To track the rotation of sliding LacI molecules on the microsecond timescale, we use real-time single-molecule confocal laser tracking combined with fluorescence correlation spectroscopy (SMCT–FCS). The fluctuations in fluorescence signal are accurately described by rotation-coupled sliding, in which LacI traverses about 40 base pairs (bp) per revolution. This distance substantially exceeds the 10.5-bp helical pitch of DNA; this suggests that the sliding protein frequently hops out of the DNA groove, which would result in the frequent bypassing of target sequences. We directly observe such bypassing using single-molecule fluorescence resonance energy transfer (smFRET). A combined analysis of the smFRET and SMCT–FCS data shows that LacI hops one or two grooves (10–20 bp) every 200–700 μs. Our data suggest a trade-off between speed and accuracy during sliding: the weak nature of nonspecific protein–DNA interactions underlies operator bypassing, but also speeds up sliding. We anticipate that SMCT–FCS, which monitors rotational diffusion on the microsecond timescale while tracking individual molecules with millisecond resolution, will be applicable to the real-time investigation of many other biological interactions and will effectively extend the accessible time regime for observing these interactions by two orders of magnitude. Single-molecule fluorescence resonance energy transfer and real-time confocal laser tracking with fluorescence correlation spectroscopy together characterize how individual lac repressor molecules bypass operator sites while exploring the DNA surface at microsecond timescales.

This memoir is a refinement of the author’s PhD thesis — written at Cornell University (2006). It is primarily a desription of new research but we have also included a substantial amount of background material. At the heart of the memoir we introduce and study a poset In general, we show that In the case that Along the way we include a comprehensive introduction to related background material. Before defining our generalization Finally, it turns out that our poset

Brettanomyces bruxellensis is a unicellular fungus of increasing industrial and scientific interest over the past 15 years. Previous studies revealed high genotypic diversity amongst B. bruxellensis strains as well as strain-dependent phenotypic characteristics. Genomic assemblies revealed that some strains harbour triploid genomes and based upon prior genotyping it was inferred that a triploid population was widely dispersed across Australian wine regions. We performed an intraspecific diversity genotypic survey of 1488 B. bruxellensis isolates from 29 countries, 5 continents and 9 different fermentation niches. Using microsatellite analysis in combination with different statistical approaches, we demonstrate that the studied population is structured according to ploidy level, substrate of isolation and geographical origin of the strains, underlying the relative importance of each factor. We found that geographical origin has a different contribution to the population structure according to the substrate of origin, suggesting an anthropic influence on the spatial biodiversity of this microorganism of industrial interest. The observed clustering was correlated to variable stress response, as strains from different groups displayed variation in tolerance to the wine preservative sulfur dioxide (SO 2 ). The potential contribution of the triploid state for adaptation to industrial fermentations and dissemination of the species B. bruxellensis is discussed.

We examined the associations of gestational diabetes mellitus (GDM) and women’s weight status from pre-pregnancy through post-delivery with the risk of developing dysglycaemia [impaired fasting glucose, impaired glucose tolerance, and type 2 diabetes (T2D)] 4–6 years post-delivery. Using Poisson regression with confounder adjustments, we assessed associations of standard categorisations of prospectively ascertained pre-pregnancy overweight and obesity (OWOB), gestational weight gain (GWG) and substantial post-delivery weight retention (PDWR) with post-delivery dysglycaemia ( n  = 692). Women with GDM had a higher risk of later T2D [relative risk (95% CI) 12.07 (4.55, 32.02)] and dysglycaemia [3.02 (2.19, 4.16)] compared with non-GDM women. Independent of GDM, women with pre-pregnancy OWOB also had a higher risk of post-delivery dysglycaemia. Women with GDM who were OWOB pre-pregnancy and had subsequent PDWR (≥ 5 kg) had 2.38 times (1.29, 4.41) the risk of post-delivery dysglycaemia compared with pre-pregnancy lean GDM women without PDWR. No consistent associations were observed between GWG and later dysglycaemia risk. In conclusion, women with GDM have a higher risk of T2D 4–6 years after the index pregnancy. Pre-pregnancy OWOB and PDWR exacerbate the risk of post-delivery dysglycaemia. Weight management during preconception and post-delivery represent early windows of opportunity for improving long-term health, especially in those with GDM.