Understanding self-reported difficulties in decision-making by people with autism spectrum disorders.

Autobiographical accounts and a limited research literature suggest that adults with autism spectrum disorders can experience difficulties with decision-making. We examined whether some of the difficulties they describe correspond to quantifiable differences in decision-making when compared to adults in the general population. The participants (38 intellectually able adults with autism spectrum disorders and 40 neurotypical adults) were assessed on three tasks of decision-making (Iowa Gambling Task, Cambridge Gamble Task and Information Sampling Task), which quantified, respectively, decision-making performance and relative attention to negative and positive outcomes, speed and flexibility, and information sampling. As a caution, all analyses were repeated with a subset of participants ( nASD = 29 and nneurotypical = 39) who were not taking antidepressant or anxiolytic medication. Compared to the neurotypical participants, participants with autism spectrum disorders demonstrated slower decision-making on the Cambridge Gamble Task, and superior performance on the Iowa Gambling Task. When those taking the medications were excluded, participants with autism spectrum disorders also sampled more information. There were no other differences between the groups. These processing tendencies may contribute to the difficulties self-reported in some contexts; however, the results also highlight strengths in autism spectrum disorders, such as a more logical approach to, and care in, decision-making. The findings lead to recommendations for how adults with autism spectrum disorders may be better supported with decision-making.The research reported here was carried out by the first author (Lydia Vella, née Luke) as part of her PhD in the Department of Psychiatry, University of Cambridge, and was supported by a Pinsent Darwin Studentship in Mental Health; University of Cambridge Domestic Research Studentship; the Charles Slater Fund; and the Marmaduke Sheild Fund. IC was supported during the preparation of this paper by the National Institute of Health Research (NIHR) Collaboration for Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire & Peterborough NHS Foundation Trust. We are grateful to all our funders for their support. The paper describes independent research and the views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202

Differential regulation of nuclear and mitochondrial Bcl-2 in T cell apoptosis

Activated T cells require anti-apoptotic cytokines for their survival. The anti-apoptotic effects of these factors are mediated by their influence on the balance of expression and localisation of pro- and anti-apoptotic members of the Bcl-2 family. Among the anti-apoptotic Bcl-2 family members, the expression level of Bcl-2 itself and its interaction with the pro-apoptotic protein Bim are now regarded as crucial for the regulation of survival in activated T cells. We studied the changes in Bcl-2 levels and its subcellular distribution in relation to mitochondrial depolarisation and caspase activation in survival factor deprived T cells. Intriguingly, the total Bcl-2 level appeared to remain stable, even after caspase 3 activation indicated entry into the execution phase of apoptosis. However, cell fractionation experiments showed that while the dominant nuclear pool of Bcl-2 remained stable during apoptosis, the level of the smaller mitochondrial pool was rapidly downregulated. Signals induced by anti-apoptotic cytokines continuously replenish the mitochondrial pool, but nuclear Bcl-2 is independent of such signals. Mitochondrial Bcl-2 is lost rapidly by a caspase independent mechanism in the absence of survival factors, in contrast only a small proportion of the nuclear pool of Bcl-2 is lost during the execution phase and this loss is a caspase dependent process. We conclude that these two intracellular pools of Bcl-2 are regulated through different mechanisms and only the cytokine-mediated regulation of the mitochondrial pool is relevant to the control of the initiation of apoptosis

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Simulation results: secondary latin-hypercube sampling sensitivity Analyis

Simulations produced during a secondary sensitivity analysis of environmental structure and number of cattle

Dawson_et_al.2008_Factorial_Simulations_Updated_92018

Results of factorial simulations from simulation experiment detailed in Dawson et al. (2018) Investigating behavioral drivers of seasonal Shiga-toxigenic Escherichia coli (STEC) patterns in grazing cattle using an agent-based model

LHS Simulations

Simulations conducted at 20,24,25, and 30C using Latin-Hypercube Sample values of 7 parameters. This was used as the basis for the calibration procedure

Calibration simulations: Increasing distance threshold at 30C

Simulations in which the value of the distance threshold for direct transmission was systematically increased at 30C using the calibrated value of grass infection factor determined at 20C

Calibration simulations: Increasing grass infection at 20C

Simulations run as part of model calibration. Sequentially increasing values of grass infection parameter were used at 20C