Soil acidity - high rainfall pastures

A. Lime on old land pastures. 80BU13, 80BU14, 80BU15, 80BU16, 80BU17, 80BY7, 80BY16, 81AL10, 81AL11, 8IAL12, 81AL13, 81AL14, 81AL15, 81AL16, 81BU18, 81BY15, 81BY16, 81BY17, 81BY18, 81BY19, 81BY24, 81BY25, 81BY16, 81MA12, 81W9, 81Wl0, 81Wll, 82AL2, 82AL3, 82AL4, 82ALS, 82AL6, 82ALSS, 82BU6, 82BU7, 82BU8, 82BY37, 82HA35, 82HA36, 82HA38, 82MA20, 82PE1, 83AL7, 83AL8, 83AL9, 83AL10, 83AL11, 83AL12, 83AL13, 83AL14, 83BU20, 83BU24, 83BU25, 83BU26, 83BY29, 83HA19, 83HA40, 83HA41. B. Lime on new land pastures 82AL7, 82AL8

Soil acidity - high rainfall pastures.

Aims of the Project (i) To establish the current pH of the cultivated soils of the high rainfall areas of south-west Western Australia, and the extent to which pH has altered since clearing. (ii) To examine the responsiveness of old land pastures with low current soil pH levels (\u3c 5.5 water) to applied lime. (iii) To relate the responsiveness of subterranean clover-based pastures to measured soil parameters. 80BU14, 81AL10, 81AL12, 81BU18, 81BY18, 81BY25, 81BY26, 82AL4, 82AL5, 82AL55, 82BU7, 82HA35, 82HA36, 82PE1, 82MA20, 83AL7, 83AL9, 83AL10, 83ALll, 83BY29, 84BU9, 84BU10, 84BY37, 84HA21, 84HA37, 84MA21

Skin microvascular vasodilatory capacity in offspring of two parents with Type 2 diabetes

Aims<br/> Microvascular dysfunction occurs in Type 2 diabetes and in subjects with fasting hyperglycaemia. It is unclear whether this dysfunction relates to dysglycaemia. This study investigated in normogylcaemic individuals whether a genetic predisposition to diabetes, or indices of insulin resistance including endothelial markers, were associated with impaired microvascular function.<br/> Methods<br/> Maximum microvascular hyperaemia to local heating of the skin was measured using laser Doppler flowmetry in 21 normoglycaemic subjects with no family history of diabetes (Group 1) and 21 normoglycaemic age, sex and body mass index-matched offspring of two parents with Type 2 diabetes (Group 2). <br/>Results<br/> Although Group 2 had normal fasting plasma glucose and glucose tolerance tests, the 120-min glucose values were significantly higher at 6.4 (5.3-6.6) mmol/l (median (25th-75th centile)) than the control group at 4.9 (4.6-5.9) mmol/l (P=0.005) and the insulinogenic index was lower at 97.1 (60.9-130.8) vs. 124.0 (97.2-177.7) (P=0.027). Skin maximum microvascular hyperaemia (Group 1: 1.56 (1.39- 1.80) vs. Group 2: 1.53 (1.30-1.98) V, P=0.99) and minimum microvascular resistance which normalizes the hyperaemia data for blood pressure (Group 1: 52.0 (43.2-67.4) vs. Group 2: 56.0 (43.7-69.6) mmHgN, P=0.70) did not differ in the two groups. Significant positive associations occurred between minimum microvascular resistance and indices of the insulin resistance syndrome; plasminogen activator inhibitor type 1 (R-s=0.46, P=0.003), t-PA (R-s=0.36, P=0.03), total cholesterol (R-s=0.35, P=0.02), and triglyceride concentration (R-s=0.35, P=0.02), and an inverse association with insulin sensitivity (R-s=-0.33, P=0.03).<br/> Conclusions<br/> In normoglycaemic adults cutaneous microvascular vasodilatory capacity is associated with features of insulin resistance syndrome, particularly with plasminogen activator inhibitor type 1. A strong family history of Type 2 diabetes alone does not result in impairment in the maximum hyperaemic response

Soil acidity - high rainfall pastures. Lime on old land pastures - field & glasshouse experiments

Soil Acidity - High Rainfall Pastures (funded by the Australian Meat Research Committee). Lime on old land pastures. 1. Field experiments - 80BU13, 80BU14, 81AL10, 81AL12, 81AL16, 81BU18, 81BY18, 81BY19, 81BY25, 81BY26, 82AL4, 82AL5, 82AL55, 82BU7, 82BU8, 82HA35, 82HA36, 82PE1, 83AL7, 83AL9, 83AL10, 83AL11, 83AL13, 83AL14, 83BU25, 83BU26, 83BY29, 83HA19, 83HA41, 84BU9, 84BY36, 84BY37, 84HA21. 2. Glasshouse experiments - 84GL4. Investigation of factors involved in lime responses on a new land acid peaty sand. 84GL7, 84GL8. Investigation of factors involved in lime responses on old land high rainfall area pastures

Clinical experience in T cell deficient patients

T cell disorders have been poorly understood until recently. Lack of knowledge of underlying molecular mechanisms together with incomplete data on long term outcome have made it difficult to assess prognosis and give the most effective treatment. Rapid progress in defining molecular defects, improved supportive care and much improved results from hematopoietic stem cell transplantation (HSCT) now mean that curative treatment is possible for many patients. However, this depends on prompt recognition, accurate diagnosis and careful treatment planning

Does dissemination extend beyond publication: a survey of a cross section of public funded research in the UK

Background: In the UK, most funding bodies now expect a commitment or effort on the part of grant holders to disseminate the findings of their research. The emphasis is on ensuring that publicly funded research is made available, can be used to support decision making, and ultimately improve the quality and delivery of healthcare provided. In this study, we aimed to describe the dissemination practices and impacts of applied and public health researchers working across the UK.Methods: We conducted a survey of 485 UK-based principal investigators of publicly funded applied and public health research. Participants were contacted by email and invited to complete an online questionnaire via an embedded URL. Gift vouchers were given to all participants who completed the questionnaire. Four reminder emails were sent out to non-respondents at one, two, three, and four weeks; a fifth postal reminder was also undertaken.Results: A total of 243/485 (50%) questionnaires were returned (232 completed, 11 declining to participate). Most researchers recognise the importance of and appear committed to research dissemination. However, most dissemination activity beyond the publishing of academic papers appears to be undertaken an ad hoc fashion. There is some evidence that access to dissemination advice and support may facilitate more policy interactions; though access to such resources is lacking at an institutional level, and advice from funders can be variable. Although a minority of respondents routinely record details about the impact of their research, when asked about impact in relation to specific research projects most were able to provide simple narrative descriptions.Conclusions: Researchers recognise the importance of and appear committed to disseminating the findings of their work. Although researchers are focussed on academic publication, a range of dissemination activities are being applied albeit in an ad hoc fashion. However, what constitutes effective dissemination (in terms of impact and return on investment) remains unclear. Researchers need greater and clearer guidance on how best to plan, resource, and facilitate their dissemination activities

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at mL∼16 and ∼200 mmag at mL∼18. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We presentan approach for creating a training set by using all detections in the science images to be thesample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21-by-21pixel stamps centered at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95% prediction accuracy on the real detections at a false alarm rate of 1%