Explore the vast range of titles available.

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 competitions 1 – 3 , the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems 4 . 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 networks 5 , 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. AlphaStar uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.

Chronic cough and phlegm are frequently reported chronic obstructive pulmonary disease (COPD) symptoms. Prior research classified chronic mucus hypersecretion (CMH) based on the presence of these symptoms for ≥3 months, called chronic bronchitis (CB) if respiratory infection symptoms were present for 1-2 years (Medical Research Council [MRC] definition). We explored whether the COPD Assessment Test (CAT), a simple measure developed for routine clinical use, captures CMH populations and outcomes similarly to MRC and St. George's Respiratory Questionnaire (SGRQ) definitions. We identified CMH in the SPIROMICS COPD cohort using (a) MRC definitions, (b) SGRQ questions for cough and phlegm (both as most/several days a week), and (c) CAT cough and phlegm questions. We determined optimal cut-points for CAT items and described exacerbation frequencies for different CMH definitions. Moderate exacerbations required a new prescription for antibiotics/oral corticosteroids or emergency department visit; severe exacerbations required hospitalization. Results were stratified by smoking status. In a population of 1431 participants (57% male; mean FEV % predicted 61%), 47% and 49% of evaluable participants had SGRQ- or CAT-defined CMH, respectively. A cut-point of ≥2 for cough and phlegm items defined CMH in CAT. Among SGRQ-CMH+ participants, 80% were also defined as CMH+ by the CAT. CMH+ participants were more likely to be current smokers. A higher exacerbation frequency was observed for presence of CMH+ versus CMH- in the year prior to baseline for all CMH definitions; this trend continued across 3 years of follow-up, regardless of smoking status. Items from the CAT identified SGRQ-defined CMH, a frequent COPD trait that correlated with exacerbation frequency. The CAT is a short, simple questionnaire and a potentially valuable tool for telemedicine or real-world trials. CAT-based CMH is a novel approach for identifying clinically important characteristics in COPD that can be ascertained in these settings.

Chronic pain and disability after limb injury are major public health problems. One key obstacle to addressing these adverse outcomes is that we do not know when exercise should be initiated or whether the beneficial effects of exercise can be reproduced using pharmacological tools. In these studies, we developed and used a murine model of orthopaedic trauma combining tibia fracture and pin fixation with tibialis anterior muscle damage. Behavioral measures included mechanical nociceptive thresholds and distances run on exercise wheels. Bone healing was quantified using microCT scanning, and muscle fiber size distribution as well as fibrosis were followed using immunohistochemistry. We found that the model provided robust mechanical allodynia, fibrosis and a shift to smaller average muscle fiber size lasting up to 5 weeks from injury. We also observed that allowing late (weeks 1-2) rather than early (weeks 0-1) wheel running after injury resulted in greater overall running activity and greater reversal of allodynia. In parallel, the late running paradigm was also associated with lower levels of muscle fibrosis and a return towards normal muscle fiber diameters. Providing the anti-fibrotic angiotensin receptor blocker losartan to mice in drinking water blocked TGFbeta production while reducing both allodynia and muscle fibrosis. Combining losartan and late exercise provided no additional benefit. We conclude that early healing after orthopaedic trauma must be allowed prior to the initiation of exercise to achieve optimal pain, functional and physiological outcomes. Losartan may provide many of the same pain, functional and physiological outcomes by its regulation of TGFbeta signaling and is a viable candidate for translational studies.

Readers both affirm and criticize the article's main idea that industry is largely liable and therefore responsible for environmental problems. In response, the author asserts that economic growth must be accompanied by concern for the environment.