Effects of Incremental Dietary Levels of Ground Flaxseed on Milk Production, Ruminal Metabolism, and Enteric Methane Emissions in Organic Dairy Cows

Ground Flaxseed (Linum uitatissimum) is a lipid supplement that is commonly fed to dairy cows. It is believed that supplemental lipid can change the Fatty Acid (FA) composition in the milk, and decrease methane production. Twenty lactating organic Jersey cows, housed at the UNH Organic Dairy Research Farm (ODRF), were randomly assigned to five replicated 4 × 4 Latin squares to investigate the effects of increasing dietary levels of ground flaxseed (0, 5, 10, or 15% of the diet dry matter) on animal fperformance (e.g., dry matter intake, milk production, milk composition), ruminal metabolism, and enteric methane emissions. Each period lasted 21 days with 14 days for diet adaptation and seven days for data and samples collection. Cows were fed twice daily (a.m. and p.m.) a total mixed ration containing 65% grass-legume baleage, and one of the following supplemental mixturess: 0% ground flaxseed, 27% corn meal, and 8% soybean meal 5% ground flaxeed, 24% corn meal, and 6% soybean meal 10% ground flaxseed, 21% corn meal, and 4% soybean meal 15% ground flaxseed, 17.5 corn meal, and 2.5% soybean meal. Feeding incremental dietary levels of ground flaxseed resulted in linear decreases of dry matter intake, yields of milk and milk components, ruminal molar proportion of acetate and butyrate, and enteric methane emissions. However, the molar proportion of propionate increased linearly with feeding incremental dietary levels of ground flaxseed. Further research is needed to investigate the long-term effects of ground flaxseed on milk yield and animal health

Boundary coupling of Lie algebroid Poisson sigma models and representations up to homotopy

A general form for the boundary coupling of a Lie algebroid Poisson sigma model is proposed. The approach involves using the Batalin-Vilkovisky formalism in the AKSZ geometrical version, to write a BRST-invariant coupling for a representation up to homotopy of the target Lie algebroid or its subalgebroids. These considerations lead to a conjectural description of topological D-branes on generalized complex manifolds, which includes A-branes and B-branes as special cases.Comment: 24 pages, no figures; v2: published versio

McKay correspondence for Landau-Ginzburg models

In this paper we prove an analogue of the McKay correspondence for Landau-Ginzburg models. Our proof is based on the ideas introduced by T. Bridgeland, A. King and M. Reid, which reformulate and generalize the McKay correspondence in the language of derived categories, along with the techniques introduced by J.-C. Chen

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting