Mineral and Phytic Acid Content as Well as Phytase Activity in Flours and Breads Made from Different Wheat Species.

Wheat is of high importance for a healthy and sustainable diet for the growing world population, partly due to its high mineral content. However, several minerals are bound in a phytate complex in the grain and unavailable to humans. We performed a series of trials to compare the contents of minerals and phytic acid as well as phytase activity in several varieties from alternative wheat species spelt, emmer and einkorn with common wheat. Additionally, we investigated the potential of recent popular bread making recipes in German bakeries to reduce phytic acid content, and thus increase mineral bioavailability in bread. For all studied ingredients, we found considerable variance both between varieties within a species and across wheat species. For example, whole grain flours, particularly from emmer and einkorn, appear to have higher mineral content than common wheat, but also a higher phytic acid content with similar phytase activity. Bread making recipes had a greater effect on phytic acid content in the final bread than the choice of species for whole grain flour production. Recipes with long yeast proofing or sourdough and the use of whole grain rye flour in a mixed wheat bread minimized the phytic acid content in the bread. Consequently, optimizing food to better nourish a growing world requires close collaboration between research organizations and practical stakeholders ensuring a streamlined sustainable process from farm to fork.

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1-3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.