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

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes