Fast Bayesian parameter estimation for stochastic logistic growth models

The transition density of a stochastic, logistic population growth model with multiplicative intrinsic noise is analytically intractable. Inferring model parameter values by fitting such stochastic differential equation (SDE) models to data therefore requires relatively slow numerical simulation. Where such simulation is prohibitively slow, an alternative is to use model approximations which do have an analytically tractable transition density, enabling fast inference. We introduce two such approximations, with either multiplicative or additive intrinsic noise, each derived from the linear noise approximation of the logistic growth SDE. After Bayesian inference we find that our fast LNA models, using Kalman filter recursion for computation of marginal likelihoods, give similar posterior distributions to slow arbitrarily exact models. We also demonstrate that simulations from our LNA models better describe the characteristics of the stochastic logistic growth models than a related approach. Finally, we demonstrate that our LNA model with additive intrinsic noise and measurement error best describes an example set of longitudinal observations of microbial population size taken from a typical, genome-wide screening experiment.Comment: 24 pages, 5 figures and 2 table

Polarization switchable two-color plasmonic nano-pixels for creating optical surfaces encoded with dual information states

We demonstrate tunable, polarization-dependent, dual-color plasmonic filters based upon arrays of asymmetric cross-shaped nano-apertures. Acting as individual color emitting nano-pixels, each aperture can selectively transmit one of 2 colors, switched by controlling the polarization of white-light incident on the rear of each pixel. By tuning the dimensions of the pixels we build a polarization sensitive color palette at resolutions far beyond the diffraction limit. Using this switchable color palette we are able to generate complex optical surfaces encoded with dual color and information states; allowing us to embed two color images within the same unit area, using the same set of nanoapertures. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Bayesian hierarchical modelling for inferring genetic interactions in yeast

PhD ThesisIdentifying genetic interactions for a given microorganism, such as yeast, is difficult. Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computa tional methodology for quantifying the fitness of microbial cultures. QFA can be used to compare between fitness observations for different genotypes and thereby infer genetic interaction strengths. Current “naive” frequentist statistical approaches used in QFA do not model between-genotype variation or difference in genotype variation under differ ent conditions. In this thesis, a Bayesian approach is introduced to evaluate hierarchical models that better reflect the structure or design of QFA experiments. First, a two-stage approach is presented: a hierarchical logistic model is fitted to microbial culture growth curves and then a hierarchical interaction model is fitted to fitness summaries inferred for each genotype. Next, a one-stage Bayesian approach is presented: a joint hierarchi cal model which simultaneously models fitness and genetic interaction, thereby avoiding passing information between models via a univariate fitness summary. The new hierarchical approaches are then compared using a dataset examining the effect of telomere defects on yeast. By better describing the experimental structure, new evidence is found for genes and complexes which interact with the telomere cap. Various extensions of these models, including models for data transformation, batch effects and intrinsically stochastic growth models are also considered.Biotechnology and Biological Sciences Research Council, Medical Research Counci

Engineering molecularly-active nanoplasmonic surfaces for DNA detection via colorimetry and Raman scattering

We report a novel nanophotonic biosensor surface capable of both colorimetric detection and Raman-scattered detection of DNA infection markers at extreme sensitivities. Combining direct-write lithography, dip-pen nanolithography based DNA patterning, and molecular self-assembly, we create molecularly-active plasmonic nanostructures onto which metallic nanoparticles are located via DNA-hybridization. Arraying these structures enables optical surfaces that change state when contacted by specific DNA sequences; shifting the surface color while simultaneously generating strong Raman-scattering signals. Patterning the DNA markers onto the plasmonic surface as micro-scale symbols results in easily identifiable color shifts, making this technique applicable to multiplexed lab-on-a-chip and point-of-care diagnostic applications

Spitzer View of Young Massive Stars in the LMC HII Complexes. II. N159

The HII complex N159 in the Large Magellanic Cloud (LMC) is used to study massive star formation in different environments, as it contains three giant molecular clouds (GMCs) that have similar sizes and masses but exhibit different intensities of star formation. We identify candidate massive young stellar objects (YSOs) using infrared photometry, and model their SEDs to constrain mass and evolutionary state. Good fits are obtained for less evolved Type I, I/II, and II sources. Our analysis suggests that there are massive embedded YSOs in N159B, a maser source, and several ultracompact HII regions. Massive O-type YSOs are found in GMCs N159-E and N159-W, which are associated with ionized gas, i.e., where massive stars formed a few Myr ago. The third GMC, N159-S, has neither O-type YSOs nor evidence of previous massive star formation. This correlation between current and antecedent formation of massive stars suggests that energy feedback is relevant. We present evidence that N159-W is forming YSOs spontaneously, while collapse in N159-E may be triggered. Finally, we compare star formation rates determined from YSO counts with those from integrated H-alpha and 24 micron luminosities and expected from gas surface densities. Detailed dissection of extragalactic GMCs like the one presented here is key to revealing the physics underlying commonly used star formation scaling laws.Comment: 60 pages, 11 figures. Accepted for publication in Astrophysical Journa

New factors for protein transport identified by a genome-wide CRISPRi screen in mammalian cells

Protein and membrane trafficking pathways are critical for cell and tissue homeostasis. Traditional genetic and biochemical approaches have shed light on basic principles underlying these processes. However, the list of factors required for secretory pathway function remains incomplete, and mechanisms involved in their adaptation poorly understood. Here, we present a powerful strategy based on a pooled genome-wide CRISPRi screen that allowed the identification of new factors involved in protein transport. Two newly identified factors, TTC17 and CCDC157, localized along the secretory pathway and were found to interact with resident proteins of ER-Golgi membranes. In addition, we uncovered that upon TTC17 knockdown, the polarized organization of Golgi cisternae was altered, creating glycosylation defects, and that CCDC157 is an important factor for the fusion of transport carriers to Golgi membranes. In conclusion, our work identified and characterized new actors in the mechanisms of protein transport and secretion, and opens stimulating perspectives for the use of our platform in physiological and pathological contexts.Includes Wellcome Trust, MRC and H202

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

An Engineered Nano-Plasmonic Biosensing Surface for Colorimetric and SERS Detection of DNA-Hybridization Events

We report a versatile nanophotonic biosensing platform that enables both colorimetric detection and enhanced Raman spectroscopy detection of molecular binding events. Through the integration of electron-beam lithography, dip-pennanolithography and molecular self-assembly, we demonstrate plasmonic nanostructures which change geometry and plasmonic properties in response to molecularly-mediated nanoparticle binding events. These biologically-active nanostructured surfaces hold considerable potential for use as multiplexed sensor platforms for point-of-care diagnostics, and as scaffolds for a new generation of molecularly dynamic metamaterials. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only