Cosmology with redshift surveys of radio sources

We use the K-z relation for radio galaxies to illustrate why it has proved difficult to obtain definitive cosmological results from studies based entirely on catalogues of the brightest radio sources, e.g. 3C. To improve on this situation we have been undertaking redshift surveys of complete samples drawn from the fainter 6C and 7C radio catalogues. We describe these surveys, and illustrate the new studies they are allowing. We also discuss our `filtered' 6C redshift surveys: these have led to the discovery of a radio galaxy at z=4.4, and are sensitive to similar objects at higher redshift provided the space density of these objects is not declining too rapidly with z. There is currently no direct evidence for a sharp decline in the space density of radio galaxies for z > 4, a result only barely consistent with the observed decline of flat-spectrum radio quasars.Comment: 8 pages Latex, To appear in the "Cosmology with the New Radio Surveys" Conference - Tenerife 13-15 January 199

Deep Learning Concepts and Applications for Synthetic Biology.

Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space

Spect perfusion imaging versus CT for predicting radiation injury to normal lung in lung cancer patients.

Objectives In non-small cell lung cancer (NSCLC) patients, to establish whether the fractional volumes of irradiated anatomic or perfused lung differed between those with and without deteriorating lung function or radiation associated lung injury (RALI).Methods 48 patients undergoing radical radiotherapy for NSCLC had a radiotherapy-planning CT scan and single photon emission CT lung perfusion imaging (99mTc-labelled macroaggregate albumin). CT defined the anatomic and the single photon emission CT scan (co-registered with CT) identified the perfused (threshold 20 % of maximum) lung volumes. Fractional volumes of anatomic and perfused lung receiving more than 5, 10, 13, 20, 30, 40, 50 Gy were compared between patients with deteriorating (>median decline) vs stable (vs stable FEV1 ( p = 0.005, 0.005 and 0.025 respectively) but did not differ for higher doses of radiation (>30, 40, 50 Gy). Fractional volumes of anatomic and perfused lung receiving > 10 Gy best predicted decline in FEV1 (Area under receiver operating characteristic curve (Az = 0.77 and 0.76 respectively); sensitivity/specificity 75%/81 and 80%/71%) for a 32.7% anatomic and 33.5% perfused volume cut-off. Irradiating an anatomic fractional volume of 4.7% to > 50 Gy had a sensitivity/specificity of 83%/89 % for indicating RALI (Az = 0.83).Conclusion A 10-20 Gy radiation dose to anatomic or perfused lung results in decline in FEV1. A fractional anatomic volume of >5% receiving >50 Gy influences development of RALI.Advances in knowledge Extent of low-dose radiation to normal lung influences functional respiratory decline

Sensitive detection of nitric oxide using seeded parametric four-wave mixing

A sensitive near-resonant four-wave mixing technique based on two-photon parametric four-wave mixing has been developed. Seeded parametric four-wave mixing requires only a single laser as an additional phase matched seeder field is generated via parametric four-wave mixing of the pump beam in a high gain cell. The seeder field travels collinearly with the pump beam providing efficient nondegenerate four-wave mixing in a second medium. This simple arrangement facilitates the detection of complex molecular spectra by simply scanning the pump laser. Seeded parametric four-wave mixing is demonstrated in both a low pressure cell and an air/acetylene flame with detection of the two-photon C (2) Pi(upsilon'=0)<--X (2) Pi(upsilon =0) spectrum of nitric oxide. From the cell data a detection limit of 10(12) molecules/cm(3) is established. A theoretical model of seeded parametric four-wave mixing is developed from existing parametric four-wave mixing theory. The addition of the seeder field significantly modifies the parametric four-wave mixing behaviour such that in the small signal regime, the signal intensity can readily be made to scale as the cube of the laser pump power while the density dependence follows a more familiar square law dependence, In general, we find excellent agreement between theory and experiment. Limitations to the process result from an ac Stark shift of the two-photon resonance in the high pressure seeder cell caused by the generation of a strong seeder field, as well as a reduction in phase matching efficiency due to the presence of certain buffer species. Various optimizations are suggested which should overcome these limitations, providing even greater detection sensitivity. (C) 1998 American Institute of Physics, [S0021-9606(98)01014-9]

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Evolution of star formation in the UKIDSS Ultra Deep Survey Field - I. Luminosity functions and cosmic star formation rate out to z = 1.6

We present new results on the cosmic star formation history in the Subaru/XMM–Newton Deep Survey (SXDS)–Ultra Deep Survey (UDS) field out to z = 1.6. We compile narrowband data from the Subaru Telescope and the Visible and Infrared Survey Telescope for Astronomy (VISTA) in conjunction with broad-band data from the SXDS and UDS, to make a selection of 5725 emission-line galaxies in 12 redshift slices, spanning 10 Gyr of cosmic time. We determine photometric redshifts for the sample using 11-band photometry, and use a spectroscopically confirmed subset to fine tune the resultant redshift distribution. We use the maximum-likelihood technique to determine luminosity functions in each redshift slice and model the selection effects inherent in any narrow-band selection statistically, to obviate the retrospective corrections ordinarily required. The deep narrow-band data are sensitive to very low star formation rates (SFRs), and allow an accurate evaluation of the faint end slope of the Schechter function, α. We find that α is particularly sensitive to the assumed faintest broad-band magnitude of a galaxy capable of hosting an emission line, and propose that this limit should be empirically motivated. For this analysis, we base our threshold on the limiting observed equivalent widths of emission lines in the local Universe. We compute the characteristic SFR of galaxies in each redshift slice, and the integrated SFR density, ρSFR. We find our results to be in good agreement with the literature and parametrize the evolution of the SFR density as ρSFR ∝ (1 + z)4.58 confirming a steep decline in star formation activity since z ∼ 1.6. Keywords: surveys – galaxies: evolution – galaxies: formation – galaxies: high-redshift – galaxies: star formation – cosmology: observations

One health: the importance of companion animal vector-borne diseases

The international prominence accorded the 'One Health' concept of co-ordinated activity of those involved in human and animal health is a modern incarnation of a long tradition of comparative medicine, with roots in the ancient civilizations and a golden era during the 19th century explosion of knowledge in the field of infectious disease research. Modern One Health tends to focus on zoonotic pathogens emerging from wildlife and production animal species, but one of the most significant One Health challenges is rabies for which there is a canine reservoir. This review considers the role of small companion animals in One Health and specifically addresses the major vector-borne infectious diseases that are shared by man, dogs and cats. The most significant of these are leishmaniosis, borreliosis, bartonellosis, ehrlichiosis, rickettsiosis and anaplasmosis. The challenges that lie ahead in this field of One Health are discussed, together with the role of the newly formed World Small Animal Veterinary Association One Health Committee

A model for improving microbial biofuel production using a synthetic feedback loop

Cells use feedback to implement a diverse range of regulatory functions. Building synthetic feedback control systems may yield insight into the roles that feedback can play in regulation since it can be introduced independently of native regulation, and alternative control architectures can be compared. We propose a model for microbial biofuel production where a synthetic control system is used to increase cell viability and biofuel yields. Although microbes can be engineered to produce biofuels, the fuels are often toxic to cell growth, creating a negative feedback loop that limits biofuel production. These toxic effects may be mitigated by expressing efflux pumps that export biofuel from the cell. We developed a model for cell growth and biofuel production and used it to compare several genetic control strategies for their ability to improve biofuel yields. We show that controlling efflux pump expression directly with a biofuel-responsive promoter is a straightforward way of improving biofuel production. In addition, a feed forward loop controller is shown to be versatile at dealing with uncertainty in biofuel production rates

Automatic reconstruction of the delivered dose of the day using MR-linac treatment log files and online MR imaging

Background and purpose Anatomical changes during external beam radiotherapy prevent the accurate delivery of the intended dose distribution. Resolving the delivered dose, which is currently unknown, is crucial to link radiotherapy doses to clinical outcomes and ultimately improve the standard of care. Material and methods In this study, we present a dose reconstruction workflow based on data routinely acquired during MR-guided radiotherapy. It employs 3D MR images, 2D cine MR images and treatment machine log files to calculate the delivered dose taking intrafractional motion into account. The developed pipeline was used to measure anatomical changes and assess their dosimetric impact in 89 prostate radiotherapy fractions delivered with a 1.5 T MR-linac at our institute. Results Over the course of radiation delivery, the CTV shifted 0.6 mm ± 2.1 mm posteriorly and 1.3 mm ± 1.5 mm inferiorly. When extrapolating the dose changes in each case to 20 fractions, the mean clinical target volume and clinical target volume dose-volume metrics decreased by 1.1 Gy ± 1.6 Gy and 0.1 Gy ± 0.2 Gy, respectively. Bladder did not change (0.0 Gy ± 1.2 Gy), while rectum decreased by 1.0 Gy ± 2.0 Gy. Although anatomical changes and their dosimetric impact were small in the majority of cases, large intrafractional motion caused the delivered dose to substantially deviate from the intended plan in some fractions. Conclusions The presented end-to-end workflow is able to reliably, non-invasively and automatically reconstruct the delivered prostate radiotherapy dose by processing MR-linac treatment log files and online MR images. In the future, we envision this workflow to be adapted to other cancer sites and ultimately to enter widespread clinical use