Non-coding RNAs associated with Prader-Willi syndrome regulate transcription of neurodevelopmental genes in human induced pluripotent stem cells.

Mutations and aberrant gene expression during cellular differentiation lead to neurodevelopmental disorders, such as Prader-Willi syndrome (PWS), which results from the deletion of an imprinted locus on paternally inherited chromosome 15. We analyzed chromatin-associated RNA in human induced pluripotent cells (iPSCs) upon depletion of hybrid small nucleolar long non-coding RNAs (sno-lncRNAs) and 5' snoRNA capped and polyadenylated long non-coding RNAs (SPA-lncRNAs) transcribed from the locus deleted in PWS. We found that rapid ablation of these lncRNAs affects transcription of specific gene classes. Downregulated genes contribute to neurodevelopment and neuronal maintenance, while upregulated genes are predominantly involved in the negative regulation of cellular metabolism and apoptotic processes. Our data reveal the importance of SPA-lncRNAs and sno-lncRNAs in controlling gene expression in iPSCs and provide a platform for synthetic experimental approaches in PWS studies. We conclude that ncRNAs transcribed from the PWS locus are critical regulators of a transcriptional signature, which is important for neuronal differentiation and development.

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.

For human-like models, train on human-like tasks.

Bowers et al. express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs' failures to account for results from psychological research. We argue that to fairly assess DNNs, we must first train them on more human-like tasks which we hypothesize will induce more human-like behaviors and representations.

Robust priors for regularized regression.

Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.

Single-objective selective-volume illumination microscopy enables high-contrast light-field imaging.

The performance of light-field microscopy is improved by selectively illuminating the relevant subvolume of the specimen with a second objective lens. Here we advance this approach to a single-objective geometry, using an oblique one-photon illumination path or two-photon illumination to accomplish selective-volume excitation. The elimination of the second orthogonally oriented objective to selectively excite the volume of interest simplifies specimen mounting; yet, this single-objective approach still reduces the out-of-volume background, resulting in improvements in image contrast, effective resolution, and volume reconstruction quality. We validate our new, to the best of our knowledge, approach through imaging live developing zebrafish, demonstrating the technology's ability to capture imaging data from large volumes synchronously with high contrast while remaining compatible with standard microscope sample mounting.

Relating causal and probabilistic approaches to contextuality.

A primary goal in recent research on contextuality has been to extend this concept to cases of inconsistent connectedness, where observables have different distributions in different contexts. This article proposes a solution within the framework of probabi- listic causal models, which extend hidden-variables theories, and then demonstrates an equivalence to the contextuality-by-default (CbD) framework. CbD distinguishes contextuality from direct influences of context on observables, defining the latter purely in terms of probability distributions. Here, we take a causal view of direct influences, defining direct influence within any causal model as the probability of all latent states of the system in which a change of context changes the outcome of a measurement. Model-based contextuality (M-contextuality) is then defined as the necessity of stronger direct influences to model a full system than when considered individually. For consistently connected systems, M-contextuality agrees with standard contextuality. For general systems, it is proved that M-contextuality is equivalent to the property that any model of a system must contain 'hidden influences', meaning direct influences that go in opposite directions for different latent states, or equivalently signalling between observers that carries no information. This criterion can be taken as formalizing the 'no-conspiracy' principle that has been proposed in connection with CbD. M-contextuality is then proved to be equivalent to CbD-contextuality, thus providing a new interpretation of CbD-contextuality as the non-existence of a model for a system without hidden direct influences. This article is part of the theme issue 'Contextuality and probability in quantum mechanics and beyond'.

The zebrafish reference genome sequence and its relationship to the human genome.

Zebrafish have become a popular organism for the study of vertebrate gene function. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.

Verbal and spatial acquisition as a function of distributed practice and code-specific interference.

Theories of memory must account for memory performance during both the acquisition (i.e., ongoing learning) and retention (i.e., following disuse) stages of training. One factor affecting both stages is whether repeated encounters with a set of material occur with no delay between blocks (massed) or alternating with another intervening task (spaced). Whereas the retention advantage for spaced over massed practice is well accounted for by some current theories of memory, theories of decay or general interference predict massed, rather than spaced, advantages during acquisition. In a series of 3 experiments, we show that the effects of spacing on acquisition depend on the relationship between primary and delay tasks. Specifically, massed acquisition advantages occur only in the presence of code-specific interference (the engagement in two alternating tasks both emphasizing the same processing code, such as verbal or spatial processing codes; e.g., learning letter-number pairs and reading text), whereas spaced acquisition advantages are observed only when code-specific interference is absent. These results present a challenge for major theories of memory. Furthermore, we argue that code-specific interference is important for researchers of the spacing and interleaving effects to take into consideration, as the relationship between the alternating tasks used has a substantial impact on acquisition performance.

Elevated success of multispecies bacterial invasions impacts community composition during ecological succession.

Successful microbial invasions are determined by a species' ability to occupy a niche in the new habitat whilst resisting competitive exclusion by the resident community. Despite the recognised importance of biotic factors in determining the invasiveness of microbial communities, the success and impact of multiple concurrent invaders on the resident community has not been examined. Simultaneous invasions might have synergistic effects, for example if resident species need to exhibit divergent phenotypes to compete with the invasive populations. We used three phylogenetically diverse bacterial species to invade two compositionally distinct communities in a controlled, naturalised in vitro system. By initiating the invader introductions at different stages of succession, we could disentangle the relative importance of resident community structure, invader diversity and time pre-invasion. Our results indicate that multiple invaders increase overall invasion success, but do not alter the successional trajectory of the whole community.

Temporal heterogeneity increases with spatial heterogeneity in ecological communities.

Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Under global change, understanding temporal community heterogeneity is necessary for predicting the stability of ecosystem functions and services. Indeed, spatial heterogeneity is commonly used in alternative stable state theory as a predictor of temporal heterogeneity and therefore an early indicator of regime shifts. To evaluate whether spatial heterogeneity in species composition is predictive of temporal heterogeneity in ecological communities, we analyzed 68 community data sets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Of the 68 data sets, 55 (81%) had a weak to strongly positive relationship between spatial and temporal heterogeneity, while in the remaining communities the relationship was weak to strongly negative (19%). Based on a mixed model analysis, we found a significant but weak overall positive relationship between spatial and temporal heterogeneity across all data sets combined, and within aquatic and terrestrial data sets separately. In addition, lifespan and successional stage were negatively and positively related to temporal heterogeneity, respectively. We conclude that spatial heterogeneity may be a predictor of temporal heterogeneity in ecological communities, and that this relationship may be a general property of many terrestrial and aquatic communities.

Narrow-sense heritability estimation of complex traits using identity-by-descent information.

Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while possibly avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole-genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). In simulations, the IBD-based approach produced unbiased heritability estimates, even when CVs were extremely rare, although precision was low. However, population stratification and non-genetic familial environmental effects shared across generations led to strong biases in IBD-based heritability. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in these real data, suggesting that more work may be required to understand and mitigate factors that influence IBD-based heritability estimates.

Heuristics as Bayesian inference under extreme priors.

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics.

SELAM: simulation of epistasis and local adaptation during admixture with mate choice.

UNLABELLED: SELAM is a forward time population genetic simulation program that provides a flexible framework for simulating admixture between any number of ancestral populations. The program can be used to simulate complex demographic and selection models, including dioecious or monoecious populations, autosomal or sex chromosomes, local adaptation, dominance, epistasis, and mate choice. AVAILABILITY AND IMPLEMENTATION: The SELAM package (C ++ source code, examples and manuals) is available via github at https://github.com/russcd/SELAM This package is distributed under version 3 of the GNU general public license. CONTACT: russcd@gmail.com.

Biotic resistance shapes the influence of propagule pressure on invasion success in bacterial communities.

The number of invaders and the timing of invasion are recognized as key determinants of successful invasions. Despite the recognized importance of "propagule pressure," invasion ecology has largely focused on how characteristics of the native community confer invasion resistance. We simultaneously manipulated community composition and invader propagule pressure in microcosm communities of freshwater bacteria. We show that high propagule pressures can be necessary to establish an invader population, but that the influence of propagule pressure depends on the composition of the resident species. In particular, the number of individuals invading was most important to invasion success when one of the species in a resident community is a strong competitor against other species. By contrast, the timing of invasion was most important when communities had lower growth rates. The results suggest that the importance of propagule pressure varies both between communities and within the same community over time, and therefore have implications for the way we understand the relationship between biotic resistance and invasion success.

A hidden Markov model for decoding and the analysis of replay in spike trains.

We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

On contextuality in behavioural data.

Dzhafarovet al.(Dzhafarovet al.2016Phil. Trans. R. Soc. A374, 20150099. (doi:10.1098/rsta.2015.0099)) reviewed several behavioural datasets imitating the formal design of the quantum-mechanical contextuality experiments. The conclusion was that none of these datasets exhibited contextuality if understood in the generalized sense proposed by Dzhafarovet al.(2015Found. Phys.7, 762-782. (doi:10.1007/s10701-015-9882-9)), while the traditional definition of contextuality does not apply to these data because they violate the condition of consistent connectedness (also known as marginal selectivity, no-signalling condition, no-disturbance principle, etc.). In this paper, we clarify the relationship between (in)consistent connectedness and (non)contextuality, as well as between the traditional and extended definitions of (non)contextuality, using as an example the Clauser-Horn-Shimony-Holt inequalities originally designed for detecting contextuality in entangled particles.

ECG-based PICC tip verification system: an evaluation 5 years on.

In 2011, the vascular access team at East Kent Hospitals University NHS Foundation Trust safely and successfully incorporated the use of electrocardiogram (ECG) guidance technology for verification of peripherally inserted central catheters (PICC) tip placement into their practice. This study, 5 years on, compared the strengths and limitations of using this ECG method with the previous gold-standard of post-procedural chest X-ray. The study was undertaken using an embedded case study approach, and the cost, accuracy and efficiency of both systems were evaluated and compared. Using ECG to confirm PICC tip position was found to be cheaper, quicker and more accurate than post-procedural chest X-ray.

ECG or X-ray as the 'gold standard' for establishing PICC-tip location?

Recently there has been an increase in evidence that the tip position of a peripherally inserted central catheter (PICC) needs to be accurately placed in the lower third of the superior vena cava at the junction with the right atrium in order to minimise potential complications ( Royal College of Nursing, 2010 ; Infusion Nurses Society, 2011 ). The current 'gold standard' practice of performing a chest X-ray post-insertion of PICC can be fraught with complications with regard to accurately placing the PICC in this position. The purpose of this evaluation is to discuss how using an electrocardiogram-guided PICC placement system may be a preferable method with which to accurately measure the exact position of the PICC within the venous system.