Regional carbon footprint analysis of dairy feeds for milk production in the USA

Purpose

A greenhouse gas emissions analysis (carbon footprint) was conducted for cultivation, harvesting, and production of common dairy feeds used for the production of dairy milk in the USA. The goal was to determine the carbon footprint (grams CO₂ equivalents (gCO₂e)/kg of dry feed) in the USA on a regional basis, identify key inputs, and make recommendations for emissions reduction.

Methods

Commonly used dairy feeds in the USA, such as soybeans, alfalfa, corn, and others, were identified based on a recent literature review and information from dairy farm surveys. The following input data for the cultivation and harvesting of dairy feeds were collected for five US regions: crop production data, energy input, soil amendments, and crop protection chemicals. Life cycle inventory input data were mainly collected from the US Department of Agriculture National Agricultural Statistical Service on a state-by-state basis as well as from state extension services forage crop budget estimates. In addition to consulting other life cycle assessment studies and published articles and reports, this cradle-to-farm gate carbon footprint analysis was conducted using the Ecoinvent? unit processes in SimaPro version 7.1? (PRé Consultants 2009).

Results

The final carbon footprint results (gCO₂e/kg of dry dairy feed) varied regionally depending on a number of factors such as lime and fertilizer application rates. The average national US carbon footprint results of the main feeds were: corn grain (390), corn silage (200), dried distillers grains with solubles (910 dry mill, 670 wet mill), oats (850), soybeans (390), soybean meal (410), winter wheat (430), alfalfa hay (170), and forage mix (160).

Conclusions and recommendations

The southeast dairy region generally showed a relatively high level of carbon footprint for most feeds, and this is attributable to the higher application rates of both synthetic fertilizers and lime. The highest contributor to carbon footprint for most regions (apart from soybeans and soybean meal) was due to the application of inorganic nitrogen fertilizer. Efficient transfer of knowledge to farmers with regards to fertilizer best management practices such as precision application of farm nutrients may contribute significantly to reducing regional crop carbon footprints.     

Differential protein pathways in 1,25-dihydroxyvitamin d(3) and dexamethasone modulated tolerogenic human dendritic cells

Tolerogenic dendritic cells (DC) that are maturation-resistant and locked in a semimature state are promising tools in clinical applications for tolerance induction. Different immunomodulatory agents have been shown to induce a tolerogenic DC phenotype, such as the biologically active form of vitamin D (1,25(OH)(2)D(3)), glucocorticoids, and a synergistic combination of both. In this study, we aimed to characterize the protein profile, function and phenotype of DCs obtained in vitro in the presence of 1,25(OH)(2)D(3), dexamethasone (DEX), and a combination of both compounds (combi). Human CD14(+) monocytes were differentiated toward mature DCs, in the presence or absence of 1,25(OH)(2)D(3) and/or DEX. Cells were prefractionated into cytoplasmic and microsomal fractions and protein samples were separated in two different pH ranges (pH 3-7NL and 6-9), analyzed by 2D-DIGE and differentially expressed spots (p < 0.05) were identified after MALDI-TOF/TOF analysis. In parallel, morphological and phenotypical analyses were performed, revealing that 1,25(OH)(2)D(3)- and combi-mDCs are closer related to each other than DEX-mDCs. This was translated in their protein profile, indicating that 1,25(OH)(2)D(3) is more potent than DEX in inducing a tolerogenic profile on human DCs. Moreover, we demonstrate that combining 1,25(OH)(2)D(3) with DEX induces a unique protein expression pattern with major imprinting of the 1,25(OH)(2)D(3) effect. Finally, protein interaction networks and pathway analysis suggest that 1,25(OH)(2)D(3), rather than DEX treatment, has a severe impact on metabolic pathways involving lipids, glucose, and oxidative phosphorylation, which may affect the production of or the response to ROS generation. These findings provide new insights on the molecular basis of DC tolerogenicity induced by 1,25(OH)(2)D(3) and/or DEX, which may lead to the discovery of new pathways involved in DC immunomodulation.     

HP1a targets the Drosophila KDM4A demethylase to a subset of heterochromatic genes to regulate H3K36me3 levels

The KDM4 subfamily of JmjC domain-containing demethylases mediates demethylation of histone H3K36me3/me2 and H3K9me3/me2. Several studies have shown that human and yeast KDM4 proteins bind to specific gene promoters and regulate gene expression. However, the genome-wide distribution of KDM4 proteins and the mechanism of genomic-targeting remain elusive. We have previously identified Drosophila KDM4A (dKDM4A) as a histone H3K36me3 demethylase that directly interacts with HP1a. Here, we performed H3K36me3 ChIP-chip analysis in wild type and dkdm4a mutant embryos to identify genes regulated by dKDM4A demethylase activity in vivo. A subset of heterochromatic genes that show increased H3K36me3 levels in dkdm4a mutant embryos overlap with HP1a target genes. More importantly, binding to HP1a is required for dKDM4A-mediated H3K36me3 demethylation at a subset of heterochromatic genes. Collectively, these results show that HP1a functions to target the H3K36 demethylase dKDM4A to heterochromatic genes in Drosophila.     

Dynamic Sensorimotor Planning during Long-Term Sequence Learning: The Role of Variability,Response Chunking and Planning Errors

Many everyday skills are learned by binding otherwise independent actions into a unified sequence of responses across days or weeks of practice. Here we looked at how the dynamics of action planning and response binding change across such long timescales. Subjects (N = 23) were trained on a bimanual version of the serial reaction time task (32-item sequence) for two weeks (10 days total). Response times and accuracy both showed improvement with time, but appeared to be learned at different rates. Changes in response speed across training were associated with dynamic changes in response time variability, with faster learners expanding their variability during the early training days and then contracting response variability late in training. Using a novel measure of response chunking, we found that individual responses became temporally correlated across trials and asymptoted to set sizes of approximately 7 bound responses at the end of the first week of training. Finally, we used a state-space model of the response planning process to look at how predictive (i.e., response anticipation) and error-corrective (i.e., post-error slowing) processes correlated with learning rates for speed, accuracy and chunking. This analysis yielded non-monotonic association patterns between the state-space model parameters and learning rates, suggesting that different parts of the response planning process are relevant at different stages of long-term learning. These findings highlight the dynamic modulation of response speed, variability, accuracy and chunking as multiple movements become bound together into a larger set of responses during sequence learning.     

Short-Term Stability in Refractive Status Despite Large Fluctuations in Glucose Levels in Diabetes Mellitus Type 1 and 2

Purpose

This work investigates how short-term changes in blood glucose concentration affect the refractive components of the diabetic eye in patients with long-term Type 1 and Type 2 diabetes.

Methods

Blood glucose concentration, refractive error components (mean spherical equivalent MSE, J0, J45), central corneal thickness (CCT), anterior chamber depth (ACD), crystalline lens thickness (LT), axial length (AL) and ocular aberrations were monitored at two-hourly intervals over a 12-hour period in: 20 T1DM patients (mean age ± SD) 38±14 years, baseline HbA1c 8.6±1.9%; 21 T2DM patients (mean age ± SD) 56±11 years, HbA1c 7.5±1.8%; and in 20 control subjects (mean age ± SD) 49±23 years, HbA1c 5.5±0.5%. The refractive and biometric results were compared with the corresponding changes in blood glucose concentration.

Results

Blood glucose concentration at different times was found to vary significantly within (p<0.0005) and between groups (p<0.0005). However, the refractive error components and ocular aberrations were not found to alter significantly over the day in either the diabetic patients or the control subjects (p>0.05). Minor changes of marginal statistical or optical significance were observed in some biometric parameters. Similarly there were some marginally significant differences between the baseline biometric parameters of well-controlled and poorly-controlled diabetic subjects.

Conclusion

This work suggests that normal, short-term fluctuations (of up to about 6 mM/l on a timescale of a few hours) in the blood glucose levels of diabetics are not usually associated with acute changes in refractive error or ocular wavefront aberrations. It is therefore possible that factors other than refractive error fluctuations are sometimes responsible for the transient visual problems often reported by diabetic patients.