University student-led public engagement event: increasing audience diversity and impact in a non-science space.

There is a wealth of innovation in microbiology outreach events globally, including in the setting where the public engagement is hosted. Previous data indicate an underrepresentation of marginalized ethnic groups attending UK science-based public engagement events. This project engaged our student cohort, encompassing a diverse range of ethnic groups, to create an integrated art and science event within an existing series of adult education evenings. The study's objectives were to increase the proportion of visitors from marginalized ethnic groups and to gain a greater understanding of the impact of the event on the visitors' reported science capital. The participants' demographics, links to our students and University, and detailed impact on participants' science capital of the event were determined through analysis of exit questionnaires. There was an increase in the proportion of marginalized ethnic group visitors compared to similar previous events. A higher proportion of visitors from marginalized ethnic groups had links with our students and University compared to white/white British visitors. Elements of the exit questionnaire were mapped to the science capital framework and participants' science capital was determined. Both ethnically marginalized participants and white/white British visitors showed an increase in science capital, specifically dimensions of science-related social capital and science-related cultural capital, after the event. In conclusion, our study suggests that a student-led blended art and science public engagement can increase the ethnic diversity of those attending and can contribute towards creating more inclusive public engagement events.

Quantum deep learning by sampling neural nets with a quantum annealer.

We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times. We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.

Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability.

Learning a second language (L2) usually progresses faster if a learner's L2 is similar to their first language (L1). Yet global similarity between languages is difficult to quantify, obscuring its precise effect on learnability. Further, the combinatorial explosion of possible L1 and L2 language pairs, combined with the difficulty of controlling for idiosyncratic differences across language pairs and language learners, limits the generalizability of the experimental approach. In this study, we present a different approach, employing artificial languages, and artificial learners. We built a set of five artificial languages whose underlying grammars and vocabulary were manipulated to ensure a known degree of similarity between each pair of languages. We next built a series of neural network models for each language, and sequentially trained them on pairs of languages. These models thus represented L1 speakers learning L2s. By observing the change in activity of the cells between the L1-speaker model and the L2-learner model, we estimated how much change was needed for the model to learn the new language. We then compared the change for each L1/L2 bilingual model to the underlying similarity across each language pair. The results showed that this approach can not only recover the facilitative effect of similarity on L2 acquisition, but can also offer new insights into the differential effects across different domains of similarity. These findings serve as a proof of concept for a generalizable approach that can be applied to natural languages.

Neural network identification of people hidden from view with a single-pixel, single-photon detector.

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times.

Deep learning for real-time single-pixel video.

Single-pixel cameras capture images without the requirement for a multi-pixel sensor, enabling the use of state-of-the-art detector technologies and providing a potentially low-cost solution for sensing beyond the visible spectrum. One limitation of single-pixel cameras is the inherent trade-off between image resolution and frame rate, with current compressive (compressed) sensing techniques being unable to support real-time video. In this work we demonstrate the application of deep learning with convolutional auto-encoder networks to recover real-time 128 × 128 pixel video at 30 frames-per-second from a single-pixel camera sampling at a compression ratio of 2%. In addition, by training the network on a large database of images we are able to optimise the first layer of the convolutional network, equivalent to optimising the basis used for scanning the image intensities. This work develops and implements a novel approach to solving the inverse problem for single-pixel cameras efficiently and represents a significant step towards real-time operation of computational imagers. By learning from examples in a particular context, our approach opens up the possibility of high resolution for task-specific adaptation, with importance for applications in gas sensing, 3D imaging and metrology.

PTEN Loss-of-Function Alterations Are Associated With Intrinsic Resistance to BRAF Inhibitors in Metastatic Melanoma.

PURPOSE: The clinical use of BRAF inhibitors in patients with melanoma is limited by intrinsic and acquired resistance. We asked whether next-generation sequencing of pretreatment tumors could identify coaltered genes that predict for intrinsic resistance to BRAF inhibitor therapy in patients with melanoma as a prelude to rational combination strategies. PATIENTS AND METHODS: We analyzed 66 tumors from patients with metastatic BRAF-mutant melanoma collected before treatment with BRAF inhibitors. Tumors were analyzed for > 250 cancer-associated genes using a capture-based next-generation sequencing platform. Antitumor responses were correlated with clinical features and genomic profiles with the goal of identifying a molecular signature predictive of intrinsic resistance to RAF pathway inhibition. RESULTS: Among the 66 patients analyzed, 11 received a combination of BRAF and MEK inhibitors for the treatment of melanoma. Among the 55 patients treated with BRAF inhibitor monotherapy, objective responses, as assessed by Response Evaluation Criteria in Solid Tumors (RECIST), were observed in 30 patients (55%), with five (9%) achieving a complete response. We identified a significant association between alterations in PTEN that would be predicted to result in loss of function and reduced progression-free survival, overall survival, and response grade, a metric that combines tumor regression and duration of treatment response. Patients with melanoma who achieved an excellent response grade were more likely to have an elevated BRAF-mutant allele fraction. CONCLUSION: These results provide a rationale for cotargeting BRAF and the PI3K/AKT pathway in patients with BRAF-mutant melanoma when tumors have concurrent loss-of-function mutations in PTEN. Future studies should explore whether gain of the mutant BRAF allele and/or loss of the wild-type allele is a predictive marker of BRAFi sensitivity.

Wound healing and treating wounds: Chronic wound care and management.

In the United States, chronic ulcers--including decubitus, vascular, inflammatory, and rheumatologic subtypes--affect >6 million people, with increasing numbers anticipated in our growing elderly and diabetic populations. These wounds cause significant morbidity and mortality and lead to significant medical costs. Preventative and treatment measures include disease-specific approaches and the use of moisture retentive dressings and adjunctive topical therapies to promote healing. In this article, we discuss recent advances in wound care technology and current management guidelines for the treatment of wounds and ulcers.

A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana.

The circadian clock is an important molecular mechanism that enables many organisms to anticipate and adapt to environmental change. Pokhilko et al. recently built a deterministic ODE mathematical model of the plant circadian clock in order to understand the behaviour, mechanisms and properties of the system. The model comprises 30 molecular species (genes, mRNAs and proteins) and over 100 parameters. The parameters have been fitted heuristically to available gene expression time series data and the calibrated model has been shown to reproduce the behaviour of the clock components. Ongoing work is extending the clock model to cover downstream effects, in particular metabolism, necessitating further parameter estimation and model selection. This work investigates the challenges facing a full Bayesian treatment of parameter estimation. Using an efficient adaptive MCMC proposed by Haario et al. and working in a high performance computing setting, we quantify the posterior distribution around the proposed parameter values and explore the basin of attraction. We investigate if Bayesian inference is feasible in this high dimensional setting and thoroughly assess convergence and mixing with different statistical diagnostics, to prevent apparent convergence in some domains masking poor mixing in others.

Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease.

More than 20 human genetic diseases are associated with inheriting an unstable expanded DNA simple sequence tandem repeat, for example, CTG (cytosine-thymine-guanine) repeats in myotonic dystrophy type 1 (DM1) and CAG (cytosine-adenine-guanine) repeats in Huntington disease (HD). These sequences mutate by changing the number of repeats not just between generations, but also during the lifetime of affected individuals. Levels of somatic instability contribute to disease onset and progression but as changes are tissue-specific, age- and repeat length-dependent, interpretation of the level of somatic instability in an individual is confounded by these considerations. Mathematical models, fitted to CTG repeat length distributions derived from blood DNA, from a large cohort of DM1-affected or at risk individuals, have recently been used to quantify inherited repeat lengths and mutation rates. Taking into account age, the estimated mutation rates are lower than predicted among individuals with small alleles (inherited repeat lengths less than 100 CTGs), suggesting that these rates may be suppressed at the lower end of the disease-causing range. In this study, we propose that a length-specific effect operates within this range and tested this hypothesis using a model comparison approach. To calibrate the extended model, we used data derived from blood DNA from DM1 individuals and, for the first time, buccal DNA from HD individuals. In a novel application of this extended model, we identified individuals whose effective repeat length, with regards to somatic instability, is less than their actual repeat length. A plausible explanation for this distinction is that the expanded repeat tract is compromised by interruptions or other unusual features. We quantified effective length for a large cohort of DM1 individuals and showed that effective length better predicts age of onset than inherited repeat length, thus improving the genotype-phenotype correlation. Under the extended model, we removed some of the bias in mutation rates making them less length-dependent. Consequently, rates adjusted in this way will be better suited as quantitative traits to investigate cis- or trans-acting modifiers of somatic mosaicism, disease onset and progression.

Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity.

Deciphering the contribution of genetic instability in somatic cells is critical to our understanding of many human disorders. Myotonic dystrophy type 1 (DM1) is one such disorder that is caused by the expansion of a CTG repeat that shows extremely high levels of somatic instability. This somatic instability has compromised attempts to measure intergenerational repeat dynamics and infer genotype-phenotype relationships. Using single-molecule PCR, we have characterized more than 17 000 de novo somatic mutations from a large cohort of DM1 patients. These data reveal that the estimated progenitor allele length is the major modifier of age of onset. We find no evidence for a threshold above which repeat length does not contribute toward age at onset, suggesting pathogenesis is not constrained to a simple molecular switch such as nuclear retention of the DMPK transcript or haploinsufficiency for DMPK and/or SIX5. Importantly, we also show that age at onset is further modified by the level of somatic instability; patients in whom the repeat expands more rapidly, develop the symptoms earlier. These data establish a primary role for somatic instability in DM1 severity, further highlighting it as a therapeutic target. In addition, we show that the level of instability is highly heritable, implying a role for individual-specific trans-acting genetic modifiers. Identifying these trans-acting genetic modifiers will facilitate the formulation of novel therapies that curtail the accumulation of somatic expansions and may provide clues to the role these factors play in the development of cancer, aging and inherited disease in the general population.

High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations.

Several human genetic diseases are associated with inheriting an abnormally large unstable DNA simple sequence repeat. These sequences mutate, by changing the number of repeats, many times during the lifetime of those affected, with a bias towards expansion. These somatic changes lead not only to the presence of cells with different numbers of repeats in the same tissue, but also produce increasingly longer repeats, contributing towards the progressive nature of the symptoms. Modelling the progression of repeat length throughout the lifetime of individuals has potential for improving prognostic information as well as providing a deeper understanding of the underlying biological process. A large data set comprising blood DNA samples from individuals with one such disease, myotonic dystrophy type 1, provides an opportunity to parameterize a mathematical model for repeat length evolution that we can use to infer biological parameters of interest. We developed new mathematical models by modifying a proposed stochastic birth process to incorporate possible contraction. A hierarchical Bayesian approach was used as the basis for inference, and we estimated the distribution of mutation rates in the population. We used model comparison analysis to reveal, for the first time, that the expansion bias observed in the distributions of repeat lengths is likely to be the cumulative effect of many expansion and contraction events. We predict that mutation events can occur as frequently as every other day, which matches the timing of regular cell activities such as DNA repair and transcription but not DNA replication.

ERα-dependent E2F transcription can mediate resistance to estrogen deprivation in human breast cancer.

Most estrogen receptor α (ER)-positive breast cancers initially respond to antiestrogens, but many eventually become estrogen-independent and recur. We identified an estrogen-independent role for ER and the CDK4/Rb/E2F transcriptional axis in the hormone-independent growth of breast cancer cells. ER downregulation with fulvestrant or small interfering RNA (siRNA) inhibited estrogen-independent growth. Chromatin immunoprecipitation identified ER genomic binding activity in estrogen-deprived cells and primary breast tumors treated with aromatase inhibitors. Gene expression profiling revealed an estrogen-independent, ER/E2F-directed transcriptional program. An E2F activation gene signature correlated with a lesser response to aromatase inhibitors in patients' tumors. siRNA screening showed that CDK4, an activator of E2F, is required for estrogen-independent cell growth. Long-term estrogen-deprived cells hyperactivate phosphatidylinositol 3-kinase (PI3K) independently of ER/E2F. Fulvestrant combined with the pan-PI3K inhibitor BKM120 induced regression of ER(+) xenografts. These data support further development of ER downregulators and CDK4 inhibitors, and their combination with PI3K inhibitors for treatment of antiestrogen-resistant breast cancers.

Hyperactivation of phosphatidylinositol-3 kinase promotes escape from hormone dependence in estrogen receptor-positive human breast cancer.

Many breast cancers exhibit a degree of dependence on estrogen for tumor growth. Although several therapies have been developed to treat individuals with estrogen-dependent breast cancers, some tumors show de novo or acquired resistance, rendering them particularly elusive to current therapeutic strategies. Understanding the mechanisms by which these cancers develop resistance would enable the development of new and effective therapeutics. In order to determine mechanisms of escape from hormone dependence in estrogen receptor-positive (ER-positive) breast cancer, we established 4 human breast cancer cell lines after long-term estrogen deprivation (LTED). LTED cells showed variable changes in ER levels and sensitivity to 17beta-estradiol. Proteomic profiling of LTED cells revealed increased phosphorylation of the mammalian target of rapamycin (mTOR) substrates p70S6 kinase and p85S6 kinase as well as the PI3K substrate AKT. Inhibition of PI3K and mTOR induced LTED cell apoptosis and prevented the emergence of hormone-independent cells. Using reverse-phase protein microarrays, we identified a breast tumor protein signature of PI3K pathway activation that predicted poor outcome after adjuvant endocrine therapy in patients. Our data suggest that upon adaptation to hormone deprivation, breast cancer cells rely heavily on PI3K signaling. Our findings also imply that acquired resistance to endocrine therapy in breast cancer may be abrogated by combination therapies targeting both ER and PI3K pathways.

Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop.

BACKGROUND: Ordinary differential equations (ODEs) are an important tool for describing the dynamics of biological systems. However, for ODE models to be useful, their parameters must first be calibrated. Parameter estimation, that is, finding parameter values given experimental data, is an inference problem that can be treated systematically through a Bayesian framework.A Markov chain Monte Carlo approach can then be used to sample from the appropriate posterior probability distributions, provided that suitable prior distributions can be found for the unknown parameter values. Choosing these priors is therefore a vital first step in the inference process. We study here a negative feedback loop in gene regulation where an ODE incorporating a time delay has been proposed as a realistic model and where experimental data is available. Our aim is to show that a priori mathematical analysis can be exploited in the choice of priors. RESULTS: By focussing on the onset of oscillatory behaviour through a Hopf Bifurcation, we derive a range of analytical expressions and constraints that link the model parameters to the observed dynamics of the system. Computational tests on both simulated and experimental data emphasise the usefulness of this analysis. CONCLUSION: Mathematical analysis not only gives insights into the possible dynamical behaviour of gene expression models, but can also be used to inform the choice of priors when parameters are inferred from experimental data in a Bayesian setting.

Genetic variation in the urea cycle: a model resource for investigating key candidate genes for common diseases.

The urea cycle is the primary means of nitrogen metabolism in humans and other ureotelic organisms. There are five key enzymes in the urea cycle: carbamoyl-phosphate synthetase 1 (CPS1), ornithine transcarbamylase (OTC), argininosuccinate synthetase (ASS1), argininosuccinate lyase (ASL), and arginase 1 (ARG1). Additionally, a sixth enzyme, N-acetylglutamate synthase (NAGS), is critical for urea cycle function, providing CPS1 with its necessary cofactor. Deficiencies in any of these enzymes result in elevated blood ammonia concentrations, which can have detrimental effects, including central nervous system dysfunction, brain damage, coma, and death. Functional variants, which confer susceptibility for disease or dysfunction, have been described for enzymes within the cycle; however, a comprehensive screen of all the urea cycle enzymes has not been performed. We examined the exons and intron/exon boundaries of the five key urea cycle enzymes, NAGS, and two solute carrier transporter genes (SLC25A13 and SLC25A15) for sequence alterations using single-stranded conformational polymorphism (SSCP) analysis and high-resolution melt profiling. SSCP was performed on a set of DNA from 47 unrelated North American individuals with a mixture of ethnic backgrounds. High-resolution melt profiling was performed on a nonoverlapping DNA set of either 47 or 100 unrelated individuals with a mixture of backgrounds. We identified 33 unarchived polymorphisms in this screen that potentially play a role in the variation observed in urea cycle function. Screening all the genes in the pathway provides a catalog of variants that can be used in investigating candidate diseases.