Overcoming inadvertent barriers to entry in large infrastructure projects

The history behind the award of Brisbane City Council's Legacy Way project is discussed and the possible impact of cognitive bias in the Expression of Interest (EOI) process together with the steps that were taken during the EOI development and evaluati

Measuring design investment in firms: conceptual foundations and exploratory UK survey

The importance of design to company and national performance has been widely discussed, with a number of studies investigating the value or impact of design on performance. However, none of these studies has measured design investment as an input against which performance can be compared. As yet, there is no established way in which design investment might be measured. Without such a method, we cannot develop a reliable picture, akin to that for R&D spending, on the impact of design spending on company performance. This paper presents a conceptual framework for the measurement of design investment and applies this framework in a survey of UK firms. The framework describes design as being part of the creation and commercialization of new products and services. The survey highlights some surprising patterns of design spend in the reported sample and demonstrates the viability of the underpinning framework. A revised framework is proposed that situates design investment in the context of R&D. The model has implications for policy makers trying to understand the role and scale of design in the private sector, for managers wishing to optimize their design investments and for academics seeking to measure the value of design.This work was carried out as part of the Design Scoreboard project funded by the Design for the 21st Century initiative of the Arts and Humanities Research Council (AHRC) and the Engineering and Physical Sciences Research Council (EPSRC). The Design Council of the UK also provided financial support for the survey as well as valuable input and commentary throughout

Interpreting protein variant effects with computational predictors and deep mutational scanning

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors

An attention modulated associative network.

We present an elemental model of associative learning that describes interactions between stimulus elements as a process of competitive normalization. Building on the assumptions laid out in Harris (2006), stimuli are represented as an array of elements that compete for attention according to the strength of their input. Elements form associations among each other according to temporal correlations in their activation but restricted by their connectivity. The model moves beyond its predecessor by specifying excitatory, inhibitory, and attention processes for each element in real time and describing their interaction as a form of suppressive gain control. Attention is formalized in this model as a network of mutually inhibitory units that moderate the activation of stimulus elements by controlling the level to which the elements are suppressed by their own inhibitory processes. The model is applied to a range of complex discriminations and related phenomena that have been taken as evidence for configural-learning processes.Australian Research Council: DP077115

Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells.

BACKGROUND: Phenotypically identical cells demonstrate predictable, robust behaviours. However, there is uncertainty as to whether phenotypically identical cells are equally similar at the underlying transcriptional level or if cellular systems are inherently noisy. To answer this question, it is essential to distinguish between technical noise and true variation in transcript levels. A critical issue is the contribution of sampling effects, introduced by the requirement to globally amplify the single cell mRNA population, to observed measurements of relative transcript abundance. RESULTS: We used single cell microarray data to develop simple mathematical models, ran Monte Carlo simulations of the impact of technical and sampling effects on single cell expression data, and compared these with experimental microarray data generated from single embryonic neural stem cells in vivo. We show that the actual distribution of measured gene expression ratios for pairs of neural stem cells is much broader than that predicted from our sampling effect model. CONCLUSION: Our results confirm that significant differences in gene expression levels exist between phenotypically identical cells in vivo, and that these differences exceed any noise contribution from global mRNA amplification.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Aversion, interpretation and determinability: Three factors of uncertainty that may play a role in psychopathology

This opinion piece considers the construct of tolerance of uncertainty and suggests that it should be viewed in the context of three psychological factors: uncertainty aversion, uncertainty interpretation, and uncertainty determinability. Uncertainty aversion refers to a dislike of situations in which the outcomes are not deterministic and is similar to conventional conceptions of (in)tolerance of uncertainty. Uncertainty interpretation refers to the extent to which variability in an observed outcome is interpreted as random fluctuation around a relatively stable base-rate versus frequent and rapid changes in the base-rate. Uncertainty determinability refers to the (actual or perceived) capacity of the individual to generate any meaningful expectancy of the uncertain outcome, which may be undeterminable if predictions are updated too quickly. We argue that uncertainty interpretation and determinability are psychological responses to the experience of probabilistic events that vary among individuals and can moderate negative affect experienced in response to uncertainty. We describe how individual differences in basic parameters of associative learning (modelled by a simple learning window) could lead to this variation. To explain these hypotheses, we utilise the distinction between aleatory uncertainty (the inherent unpredictability of individual stochastic events) and epistemic uncertainty (obtainable knowledge that the individual lacks or perceives to be lacking). We argue that when expectancies are updated quickly, epistemic uncertainty will dominate the individual’s representation of the events around them, leading to a subjective experience of the world as one that is volatile and unpredictable

Observed changes in stratospheric circulation: decreasing lifetime of N2O, 2005–2021

Using Aura Microwave Limb Sounder satellite observations of stratospheric nitrous oxide (N2O), ozone, and temperature from 2005 through 2021, we calculate the atmospheric lifetime of N2O to be decreasing at a rate of −2.1 ± 1.2 %/decade. This decrease is occurring because the N2O abundances in the middle tropical stratosphere, where N2O is photochemically destroyed, are increasing at a faster rate than the bulk N2O in the lower atmosphere. The cause appears to be a more vigorous stratospheric circulation, which models predict to be a result of climate change. If the observed trends in lifetime and implied emissions continue, then the change in N2O over the 21st century will be 27 % less than those projected with a fixed lifetime, and the impact on global warming and ozone depletion will be proportionately lessened. Because global warming is caused in part by N2O, this finding is an example of a negative climate–chemistry feedback.</p

Functional properties of in vitro excitatory cortical neurons derived from human pluripotent stem cells

The in vitro derivation of regionally defined human neuron types from patient‐derived stem cells is now established as a resource to investigate human development and disease. Characterization of such neurons initially focused on the expression of developmentally regulated transcription factors and neural markers, in conjunction with the development of protocols to direct and chart the fate of differentiated neurons. However, crucial to the understanding and exploitation of this technology is to determine the degree to which neurons recapitulate the key functional features exhibited by their native counterparts, essential for determining their usefulness in modelling human physiology and disease in vitro. Here, we review the emerging data concerning functional properties of human pluripotent stem cell‐derived excitatory cortical neurons, in the context of both maturation and regional specificity. [Image: see text

The content of compound conditioning

In three experiments using Pavlovian conditioning of magazine approach, rats were trained with a compound stimulus, AB, and were concurrently trained with stimulus B on its own. The reinforcement rate of B, rB, was either ½, ⅔, or ⅖ of rAB. After extended training, the conditioning strength of A was assessed using probe trials in which A was presented alone. Responding during A was compared with that during AB, B, and a third stimulus, C, for which rC = rAB – rB. In each experiment, the rats’ response rate during A was almost identical to that during C (and during B, when rB = ½rAB). This suggests that, during AB conditioning, the rats had learned about rA as being equal to [rAB – rB], and implies that the content of their learning was a linear function of r. The findings provide strong support for rate-based models of conditioning (e.g., Gallistel & Gibbon, 2000). They are also consistent with the associative account of learning defined in the Rescorla-Wagner (1972) model, but only if the learning rate during reinforcement equals that during non-reinforcement.This work was supported by grant DP1092695 from the Australian Research Council