Modified approach to estimating daily methane emissions of dairy cows by measuring filtered eructations during milking

The aim of this study was to compare metrics for quantifying enteric methane (CH4) emissions from individual cows during milking using frequent spot measurements and peak analysis methods. An infrared gas analyser was used to measure the CH4 emitted by cows, and eructation peaks were identified using a Signal Processing Toolbox provided by Matlab. CH4 emissions were quantified by gas peak height, peak amplitude and average concentration, and were expressed in grams per day and CH4 yield (grams per kilogram of dry matter intake (DMI)). Peak analysis measurements of CH4 were obtained from 36 cows during 2,474 milkings, during which cows were fed a ration containing between 39 and 70 % forage. Spot measurements of CH4 were compared to a separate dataset of 196 chamber CH4 records from another group of 105 cows, which were fed a ration containing between 25 and 80 % forage. The results showed that the metrics of CH4 peak height and CH4 peak amplitude demonstrated similar positive relationships between daily CH4 emissions and DMI (both r = 0.37), and a negative relationship between CH4 yield and DMI (r = -0.43 and -0.38 respectively) as observed in the chamber measurements (r = 0.57 for daily emissions and r = -0.40 for CH4 yield). The CH4 metrics of peak height and peak amplitude were highly repeatable (ranging from 0.76 to 0.81), comparable to the high repeatability of production traits (ranging from 0.63 to 0.99) and were more repeatable than chamber CH4 measurements (0.31 for daily emissions and 0.03 for CH4 yield). This study recommends quantifying CH4 emissions from the maximum amplitude of an eructation

AKA-TPG: A Program for Kinetic and Epidemiological Analysis of Data from Labeled Glucose Investigations Using the Two-Pool Model and Database Technology

Background: The Two-Pool Glucose (TPG) model has an important role to play in diabetes research since it enables analysis of data obtained from the frequently sampled labeled (hot) glucose tolerance test (FSHGT). TPG modeling allows determination of the separate effects of insulin on the disposal of glucose and on the hepatic production of glucose. It therefore provides a basis for the accurate estimation of glucose effectiveness, insulin sensitivity, and the profile of the rate of endogenous glucose production. Until now, there has been no program available dedicated to the TPG model, and a number of technical reasons have deterred researchers from performing TPG analysis. Methods and Results: In this paper, we describe AKA-TPG, a new program that combines automatic kinetic analysis of the TPG model data with database technologies. AKA-TPG enables researchers who have no expertise in modeling to quickly fit the TPG model to individual FSHGT data sets consisting of plasma concentrations of unlabeled glucose, labeled glucose, and insulin. Most importantly, because the entire process is automated, parameters are almost always identified, and parameter estimates are accurate and reproducible. AKA-TPG enables the demographic data of hundreds of individual subjects, their individual unlabeled and labeled glucose and insulin data, and each subject\u27s parameters and indices derived from AKA-TPG to be securely stored in, and retrieved from, a database. We describe how the stratification and population analysis tools in AKA-TPG are used and present population estimates of TPG model parameters for young, healthy (without diabetes) Nordic men. Conclusion: Researchers now have a practical tool to enable kinetic and epidemiological analysis of TPG data sets

Field Observations on the Effect of a Mannan Oligosaccharide on Mortality and Intestinal Integrity of Sole (Solea senegalensis, Kaup) Infected by Photobacterium damselae subsp. piscicida

This study was conducted in order to investigate the effect of a mannan oligosaccharide (MOS) on the intestinal morphology of sole (Solea senegalensis, Kaup) reared under commercial conditions. The dietary inclusion rate for MOS was 0.4% and it was used either alone or in combination with a vaccination regime against bacterial diseases (Pasteurella spp. and Vibrio spp.). One week after the start of the experimental period, a natural outbreak of pasteurellosis, caused by Photobacterium damselae subsp.piscicida,occurred in all the groups of fish.A two-way ANOVA showed that only MOS supplementation reduced fish mortality by ca. 8% (P = 0.050). Additionally, light microscopy examination of the intestine revealed that MOS supplementation significantly increased the mucosal folding by 29% (P = 0.016) in the anterior intestinal region and by 33% (P = 0.002) in the posterior intestinal region. Scanning electron microscopy demonstrated that both MOS supplementation and vaccination significantly increased microvilli density on the enterocyte surfaces in the anterior intestinal regionby 13% (P = 0.028) and 30% (P = 0.001) respectively. In the posterior intestinal region neither MOS supplementation nor vaccination significantly affected the microvilli density (P = 0.005).The present study suggests that dietary MOS supplementation protects the intestinal morphology of infected sole and hinders the development of pathogenic infection, possibly by binding with Photobacterium damselae subsp. piscicida, resulting in reduced mortality of infected fish

MINMOD Millennium: A Computer Program to Calculate Glucose Effectiveness and Insulin Sensitivity From the Frequently Sampled Intravenous Glucose Tolerance Test

The Bergman Minimal Model enables estimation of two key indices of glucose/insulin dynamics: glucose effectiveness and insulin sensitivity. In this paper we describe MINMOD Millennium, the latest Windows-based version of minimal model software. Extensive beta testing of MINMOD Millennium has shown that it is user-friendly, fully automatic, fast, accurate, reproducible, repeatable, and highly concordant with past versions of MINMOD. It has a simple interface, a comprehensive help system, an input file editor, a file converter, an intelligent processing kernel, and a file exporter. It provides publication-quality charts of glucose and insulin and a table of all minimal model parameters and their error estimates. In contrast to earlier versions of MINMOD and some other minimal model programs, Millennium provides identified estimates of insulin sensitivity and glucose effectiveness for almost every subject

Sidelobe suppression techniques for near-field multistatic SAR

Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery

The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO₂-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits

A universal equation to predict methane production of forage-fed cattle in Australia

The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (±0.28) × DMI (kg/day) (R2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (±0.008) × GEI (MJ/day) (R2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia’s national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO2-e reduction in calculated annual emissions from Australian cattle