Estimates of daily oxygen consumption,carbon dioxide and methane emissions,and heat production for beef and dairy cattle using spot gas sampling

A simulation study was conducted to examine accuracy of estimating daily O₂ consumption, CO₂ and CH₄ emissions, and heat production (HP) using a spot sampling technique and to determine optimal spot sampling frequency (FQ). Data were obtained from 3 experiments where daily O₂ consumption, emissions of CO₂ and CH₄, and HP were measured using indirect calorimetry (respiration chamber or headbox system). Experiment 1 used 8 beef heifers (ad libitum feeding; gaseous exchanges measured every 30 min over 3 d in respiration chambers); Experiment 2 used 56 lactating Holstein-Friesian cows (restricted feeding; gaseous exchanges measured every 12 min over 3 d in respiration chambers); Experiment 3 used 12 lactating Jersey cows (ad libitum feeding; gaseous exchanges measured every hour for 1 d using headbox style chambers). Within experiment, averages of all measurements (FQALL) and averages of measurements selected at time points with 12, 8, 6, or 4 spot sampling FQ (i.e., sampling every 2, 3, 4, and 6 h in a 24-h cycle, respectively; FQ12, FQ8, FQ6, and FQ4, respectively) were compared. Within study a mixed model was used to compare gaseous exchanges and HP among FQALL, FQ12, FQ8, FQ6, and FQ4, and an interaction of dietary treatment by FQ was examined. A regression model was used to evaluate accuracy of spot sampling within study [i.e., FQALL (observed) vs. FQ12, FQ8, FQ6, or FQ4 (estimated)]. No interaction of diet by FQ was observed for any variables except for CH₄ production in experiment 1. No FQ effect was observed for gaseous exchanges and HP except in experiment 2 where CO₂ production was less (5,411 vs. 5,563 L/d) for FQ4 compared with FQALL, FQ12, and FQ8. A regression analysis between FQALL and each FQ within study showed that slopes and intercepts became farther from 1 and 0, respectively, for almost all variables as FQ decreased. Most variables for FQ12 and FQ8 had root mean square prediction error (RMSPE) less than 10% of the mean and concordance correlation coefficient (CCC) greater than 0.80, and RMSPE increased and CCC decreased as FQ decreased. When a regression analysis was conducted with combined data from the 3 experiments (mixed model with study as a random effect), results agreed with those from the analysis for the individual studies. Prediction errors increased and CCC decreased as FQ decreased. Generally, all the estimates from FQ12, FQ8, FQ6, and FQ4 had RMSPE less than 10% of the means and CCC greater than 0.90 except for FQ6 and FQ4 for O₂ consumption and CH₄ production. In conclusion, the spot sampling simulation with 3 indirect calorimetry experiments indicated that FQ of at least 8 samples (every 3 h in a 24-h cycle) was required to estimate daily O₂ consumption, CO₂ and CH₄ production, and HP and to detect changes in those in response to dietary treatments. This sampling FQ may be considered when using techniques that measure spot gas exchanges such as the GreenFeed and face mask systems.     

Effects of choline chloride on the ruminal microbiome at 2 dietary neutral detergent fiber concentrations in continuous culture

Our objective was to evaluate the effects of unprotected choline chloride (Cho) on the ruminal microbiome at 2 dietary neutral detergent fiber (NDF) concentrations. We hypothesized that the effects of Cho on ruminal bacterial populations would depend on NDF. Eight dual-flow continuous-culture fermentors were arranged in a duplicated 4 × 4 Latin square as a 2 × 2 factorial with the following treatments: (1) 30% NDF-control (30% NDF diet, no supplemental choline); (2) 30% NDF-Cho (30% NDF diet plus 1.9 g of choline ion per kg of dry matter); (3) 40% NDF-control (40% NDF diet, no supplemental choline); and (4) 40% NDF-Cho (40% NDF diet plus 1.9 g of choline ion per kg of dry matter). We did 4 fermentation periods of 10 d each and used the last 3 d for collection of samples of solid and liquid digesta effluents for DNA extraction. Overall, 32 solid and 32 liquid samples were analyzed by amplification of the V4 variable region of bacterial 16S rRNA. Data were analyzed with R (R Project for Statistical Computing) and SAS (SAS Institute Inc.) to determine effects of Cho, NDF, and NDF × Cho on taxa relative abundance. The correlation of propionate molar proportion with taxa relative abundance was also analyzed. At the phylum level, relative abundance of Firmicutes in the liquid fraction tended to be greater when Cho was supplemented with a 30% NDF diet. At the order level, Cho increased Coriobacteriales in solid fraction and decreased Fibrobacterales in liquid fraction. Moreover, Cho decreased abundance of Clostridiales and increased Selenomonadales in the solid fraction, only with the 30% NDF diet. For genera, lower abundance of Pseudobutyrivibrio resulted from Cho in solid and liquid fractions. Greater abundance of Succinivibrio in solid and Selenomonas and Selenomonas 1 in liquid resulted from Cho with the 30% NDF diet. Propionate molar proportion was positively correlated with relative abundance of order Selenomonadales in solid and liquid fractions, and with genus Succinivibrio in solid and genera Selenomonas and Selenomonas 1 in liquid. Our results indicate that Cho primarily decreases abundance of bacteria involved in fiber degradation and increases abundance of bacteria mainly involved in nonstructural carbohydrate degradation and synthesis of propionate, particularly when a diet with 30% NDF is provided.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Controlling Au electrode patterns for simultaneously monitoring electrical actuation and shape recovery in shape memory polymer

This paper presents an effective approach to achieve efficient electrical actuation and monitoring of shape recovery based on patterned Au electrodes on shape memory polymer (SMP). The electrically responsive shape recovery behavior was characterized and monitored by the evolution change in electrical resistance of patterned Au electrode. Both electrical actuation and temperature distribution in the SMP have been improved by optimizing the Au electrode patterns. The electrically actuated shape recovery behavior and temperature evolution during the actuation were monitored and characterized. The resistance changes could be used to detect beginning/finishing points of the shape recovery. Therefore, the Au electrode not only significantly enhances the electrical actuation performance to achieve a fast electrical actuation, but also enables the resistance signal to detect the free recovery process.     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.     

Hierarchical electromagnetic absorption in micro-waveguide stuffed with magnetic media for resin-based composites of graphene nanosheets and manganese oxides

Waveguide configurations of hierarchical system are proposed as new microstructures for composites in absorbing enhancement. Supercritical fluid (SCF) one-pot exfoliation of layered graphite and manganese oxide mixing materials is developed to obtain a hierarchical system, containing graphene nanosheets (GNS) and exfoliated manganese oxides (EMO) in different sizes. Composites with GNS–EMO embedded in epoxy resin matrix are produced for a design of dielectric and magnetic loss integrated absorber. Volume fraction of GNS–EMO in composites is given for an optimal quantity of resin epoxy in fixation and formation. The effect of mixing ratios between electric and magnetic components is provided for the design of dielectric and magnetic loss integrated absorbers. Frequency shifting phenomena are revealed in the component adjusting course. Excluding the offsetting sizes, reflection loss of composites is enhanced as thickness increases. Synergistic effect of electric and magnetic coordinated materials demonstrates the superiority of micro-waveguide structures in GNS–EMO composite absorber.     

Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

A handheld sensor assay for the identification of grouper as a safeguard against seafood mislabeling fraud

Increases in international trade and global seafood consumption, along with fluctuations in the supply of different seafood species, have resulted in fraudulent product mislabeling. Grouper species, due to their high demand and varied commercial availability, are common targets for mislabeling by exploiting inefficient inspection practices. Compounding this problem is the fact that there are currently 64 species of fish from eleven different genera allowed to be labeled “grouper” per U.S. Food and Drug Administration guidelines. This wide diversity makes it difficult for regulators to discern legally salable groupers from restricted species. To obviate taxonomic misidentification when relying on external phenotypic characteristics, regulatory agencies are now employing genetic authentication methods which typically offer species-level resolution. However, standard genetic methods such as DNA barcoding require technical expertise and long turnover times, and the required instrumentation is not amenable for on-site analysis of seafood. To obviate some of these limitations, we have developed a handheld genetic sensor that employs a real-time nucleic acid sequence-based amplification assay (RT-NASBA) previously devised in our lab, for the analysis of fish tissue in the field. The base RT-NASBA assay was validated using a lab-based, benchtop RNA purification method as well as non-portable, commercial RT-NASBA analyzer. Described herein, is an uncomplicated method for purifying RNA from fish tissue in the field, which had similar efficiency to the benchtop method demonstrated through direct comparisons. We have also demonstrated that the field sensor is only slightly less sensitive than the benchtop instrument, and could discern 80.3% of groupers (no target sequence available for three species) on the 2014 FDA Seafood List from potential impostors. The complete field assay requires fewer than 80 min for completion and can be performed outside of the lab in its entirety.