Access to transport and life opportunities

This report presents the findings from a study conducted by NatCen and the University of the West of England (UWE Bristol), commissioned by the Department for Transport (DfT) to investigate how access to transport affects the life opportunities and wellbeing of people living in England. This has provided new evidence that access to public and private transport has wide-ranging positive impacts on people’s lives. The study involves analyses of two national longitudinal data sets: Understanding Society and the English Longitudinal Study of Ageing (ELSA). Overall, the study reveals that having personal car access opens up life opportunities including, employment, access to services and social participation. The majority (69%) of the population have personal access to cars and a larger proportion (87%) of the population often use cars (at least once a week). The study also highlights the risk of economic and social exclusion for those with no personal car access and no access to good public transport. Nearly a third of the population do not have personal access to a car and this is more common amongst young adults, those in BME groups, those with impairments, unemployed people and those with low incomes. Given the benefits of personal car access, it is important that barriers to car access are not disproportionate for those who are more reliant on cars, particularly people living in small towns and rural areas, people with mobility impairments and people on low incomes

Efficacy of bezlotoxumab in participants receiving metronidazole, vancomycin, or fidaxomicin for treatment of Clostridioides (Clostridium) difficile infection

Background: In phase 3 MODIFY I/II trials, bezlotoxumab significantly reduced recurrence of Methods: In MODIFY I/II (NCT01241552/NCT01513239), participants received a single infusion of bezlotoxumab (10 mg/kg) or placebo during anti-CDI treatment. Using pooled data from MODIFY I/II, initial clinical cure (ICC) and rCDI were assessed in metronidazole-, vancomycin-, and fidaxomicin-treated subgroups. Results: Of 1554 participants in MODIFY I/II, 753 (48.5%) received metronidazole, 745 (47.9%) vancomycin, and 56 (3.6%) fidaxomicin. Fewer participants receiving metronidazole had a prior CDI episode in the previous 6 months (12.9%) or ≥1 risk factor for rCDI (66.0%) vs participants receiving vancomycin (41.2% and 83.6%, respectively) and fidaxomicin (55.4% and 89.3%, respectively). ICC rates were similar in the bezlotoxumab (metronidazole, 81.0%; vancomycin, 78.5%; fidaxomicin, 86.7%) and placebo groups (metronidazole, 81.3%; vancomycin, 79.6%; fidaxomicin, 76.9%). In placebo-treated participants, the rCDI was lower in the metronidazole subgroup vs the vancomycin and fidaxomicin subgroups (metronidazole, 28.0%; vancomycin, 38.4%; fidaxomicin, 35.0%). When analyzed by subsets based on history of CDI, rCDI rates were similar in the metronidazole and vancomycin groups. rCDI rates were lower in all antibiotic subgroups for bezlotoxumab vs placebo (metronidazole: rate difference [RD], -9.7%; 95% confidence interval [CI], -16.4% to -3.1%; vancomycin: RD, -15.4%; 95% CI, -22.7% to -8.0%; fidaxomicin: RD, -11.9%; 95% CI, -38.1% to 14.3%). Conclusion: Bezlotoxumab reduces rCDI vs placebo in participants receiving metronidazole and vancomycin, with a similar effect size in participants receiving fidaxomicin

Sparse multinomial kernel discriminant analysis (sMKDA)

Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets

A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes

Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping

<p>Abstract</p> <p>Background</p> <p>The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.</p> <p>Results</p> <p>We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.</p> <p>Conclusions</p> <p>The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.</p

Modified glucose as a sensor to track the metabolism of individual living endothelial cells - observation of the 1602 cm−1 band called "Raman spectroscopic signature of life"

A relatively new approach to subcellular research is Raman microscopy with the application of sensors called Raman probes. This paper describes the use of the sensitive and specific Raman probe, 3-O-propargyl-d-glucose (3-OPG), to track metabolic changes in endothelial cells (ECs). ECs play a significant role in a healthy and dysfunctional state, the latter is correlated with a range of lifestyle diseases, particularly with cardiovascular disorders. The metabolism and glucose uptake may reflect the physiopathological conditions and cell activity correlated with energy utilization. To study metabolic changes at the subcellular level the glucose analogue, 3-OPG was used, which shows a characteristic and intense Raman band at 2124 cm−1.3-OPG was applied as a sensor to track both, its accumulation in live and fixed ECs and then metabolism in normal and inflamed ECs, by employing two spectroscopic techniques, i.e. spontaneous and stimulated Raman scattering microscopies. The results indicate that 3-OPG is a sensitive sensor to follow glucose metabolism, manifested by the Raman band of 1602 cm−1. The 1602 cm−1 band has been called the “Raman spectroscopic signature of life” in the cell literature, and here we demonstrate that it is attributed to glucose metabolites. Additionally, we have shown that glucose metabolism and its uptake are slowed down in the cellular inflammation. We showed that Raman spectroscopy can be classified as metabolomics, and its uniqueness lies in the fact that it allows the analysis of the processes of a single living cell. Gaining further knowledge on metabolic changes in the endothelium, especially in pathological conditions, may help in identifying markers of cellular dysfunction, and more broadly in cell phenotyping, better understanding of the mechanism of disease development and searching for new treatments

Measured estimates of semi-natural terrestrial NPP in Great Britain:comparison with modelled values, and dependence on atmospheric nitrogen deposition

Plant growth in nitrogen (N)-limited, unfertilised terrestrial ecosystems should respond to additional N inputs from atmospheric deposition (Ndep). We investigated this for sites in Great Britain (GB) by compiling 796 estimates of net primary productivity (NPP) from measured biomass production over the period 1932–2014, although the great majority were for 1990 onwards. The sites were largely vegetated with shrubs, grass and bracken, and had a wide range of Ndep (0.5–3.3 gN m−2 a−1 in 2000). The measured NPP estimates were compared with calculated values from the biogeochemical ecosystem model N14CP, which predicts that NPP depends strongly upon Ndep. The measured and modelled average total NPP values (gC m−2 a−1) from all data were 387 (standard deviation, SD = 193) and 377 (SD = 72) respectively. Measured and modelled averages for vegetation classes followed the sequence: broadleaved trees ~ needle-leaved trees > herbs (rough grassland + bracken) ~ shrubs. After averaging measured values for sites in individual model grid cells (5 km × 5 km) with 10 or more replicates, the measured and modelled NPP values were correlated (n = 26, r2 = 0.22, p = 0.011), with a slope close to unity. Significant linear relationships were found between measured ln NPP and cumulative Ndep for both herbs (n = 298, p = 0.021) and shrubs (n = 473, p = 0.006), with slopes comparable to those predicted with the model. The results suggest that semi-natural NPP in GB depends positively upon Ndep, in a manner that agrees quantitatively with N14CP predictions. Calculations with the model, using modelled temporal variation in Ndep, indicate that fertilisation by Ndep caused average increases in semi-natural NPP over the period 1800 to 2010 of 30% for shrubs, 71% for herbs, and 91% for broadleaved trees. Combined with previous published results for forests, our findings suggest a general and widespread vegetation response to fertilisation by Ndep

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets

The water vapour continuum in near-infrared windows – current understanding and prospects for its inclusion in spectroscopic databases

Spectroscopic catalogues, such as GEISA and HITRAN, do not yet include information on the water vapour continuum that pervades visible, infrared and microwave spectral regions. This is partly because, in some spectral regions, there are rather few laboratory measurements in conditions close to those in the Earth’s atmosphere; hence understanding of the characteristics of the continuum absorption is still emerging. This is particularly so in the near-infrared and visible, where there has been renewed interest and activity in recent years. In this paper we present a critical review focusing on recent laboratory measurements in two near-infrared window regions (centred on 4700 and 6300 cm−1) and include reference to the window centred on 2600 cm−1 where more measurements have been reported. The rather few available measurements, have used Fourier transform spectroscopy (FTS), cavity ring down spectroscopy, optical-feedback – cavity enhanced laser spectroscopy and, in very narrow regions, calorimetric interferometry. These systems have different advantages and disadvantages. Fourier Transform Spectroscopy can measure the continuum across both these and neighbouring windows; by contrast, the cavity laser techniques are limited to fewer wavenumbers, but have a much higher inherent sensitivity. The available results present a diverse view of the characteristics of continuum absorption, with differences in continuum strength exceeding a factor of 10 in the cores of these windows. In individual windows, the temperature dependence of the water vapour self-continuum differs significantly in the few sets of measurements that allow an analysis. The available data also indicate that the temperature dependence differs significantly between different near-infrared windows. These pioneering measurements provide an impetus for further measurements. Improvements and/or extensions in existing techniques would aid progress to a full characterisation of the continuum – as an example, we report pilot measurements of the water vapour self-continuum using a supercontinuum laser source coupled to an FTS. Such improvements, as well as additional measurements and analyses in other laboratories, would enable the inclusion of the water vapour continuum in future spectroscopic databases, and therefore allow for a more reliable forward modelling of the radiative properties of the atmosphere. It would also allow a more confident assessment of different theoretical descriptions of the underlying cause or causes of continuum absorption

In vivo single cell analysis reveals Gata2 dynamics in cells transitioning to hematopoietic fate

Cell fate is established through coordinated gene expression programs in individual cells. Regulatory networks that include the Gata2 transcription factor play central roles in hematopoietic fate establishment. Although Gata2 is essential to the embryonic development and function of hematopoietic stem cells that form the adult hierarchy, little is known about the in vivo expression dynamics of Gata2 in single cells. Here, we examine Gata2 expression in single aortic cells as they establish hematopoietic fate in Gata2Venus mouse embryos. Time-lapse imaging reveals rapid pulsatile level changes in Gata2 reporter expression in cells undergoing endothelial-to-hematopoietic transition. Moreover, Gata2 reporter pulsatile expression is dramatically altered in Gata2+/- aortic cells, which undergo fewer transitions and are reduced in hematopoietic potential. Our novel finding of dynamic pulsatile expression of Gata2 suggests a highly unstable genetic state in single cells concomitant with their transition to hematopoietic fate. This reinforces the notion that threshold levels of Gata2 influence fate establishment and has implications for transcription factor-related hematologic dysfunctions