Despite intensive anti-hypertensive therapy there was a high incidence of renal end-points in participants of the African American Study of Kidney Disease and Hypertension (AASK) cohort. To better understand this, coding variants in the apolipoprotein L1 (APOL1) and the non-muscle myosin heavy chain 9 (MYH9) genes were evaluated for an association with hypertension-attributed nephropathy and clinical outcomes in a case-control study. Clinical data and DNA were available for 675 AASK participant cases and 618 African American non-nephropathy control individuals. APOL1 G1 and G2, and MYH9 E1 variants along with 44 ancestry informative markers were genotyped with allele frequency differences between cases and controls analyzed by logistic regression multivariable models adjusting for ancestry, age, and gender. In recessive models, APOL1 risk variants were significantly associated with kidney disease in all cases compared to controls with an odds ratio of 2.57. In AASK cases with more advanced disease, such as a baseline urine protein to creatinine ratio over 0.6 g/g or a serum creatinine over 3 mg/dL during follow-up, the association was strengthened with odds ratios of 6.29 and 4.61, respectively. APOL1 risk variants were consistently associated with renal disease progression across medication classes and blood pressure targets. Thus, kidney disease in AASK participants was strongly associated with APOL1 renal risk variants.

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions:1. How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? 2. How could we teach AI topics at an early undergraduate or a secondary school level? 3. AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.

Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it may be too late for computing an interactive response. We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response. We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using off-the-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress.

The use of robots in stroke rehabilitation has become a popular trend in rehabilitation robotics. However, despite the acknowledged value of customized service for individual patients, research on programming adaptive therapy for individual patients has received little attention. The goal of the current study is to model teletherapy sessions in the form of a generative process for autonomous therapy that approximate the demonstrations of the therapist. The resulting autonomous programs for therapy may imitate the strategy that the therapist might have employed and reinforce therapeutic exercises between teletherapy sessions. We propose to encode the therapist's decision criteria in terms of the patient's motor performance features. Specifically, in this work, we apply Latent Dirichlet Allocation on the batch data collected during teletherapy sessions between a single stroke patient and a single therapist. Using the resulting models, the therapeutic exercise targets are generated and are verified with the same therapist who generated the data.

Auto battlers are a recent genre of online deck-building games where players choose and arrange cards that then compete against other players' cards in fully-automated battles. As in other deck-building games, such as trading card games, designers must balance the cards to permit a wide variety of competitive strategies. We present Ludus, a framework that combines automated playtesting with global search to optimize parameters for each card that will assist designers in balancing new content. We develop a sampling-based approximation to reduce the playtesting needed during optimization. To guide the global search, we define metrics characterizing the health of the metagame and explore their impacts on the results of the optimization process. Our research focuses on an auto battler game we designed for AI research, but our approach is applicable to other auto battler games.