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Recommending Points-of-Interest (POIs) is surfacing in many locationbased applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability and contextuality particularly in cold start situations. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs "mindsets", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness. CCS CONCEPTS • Information systems → Spatial-temporal systems.

The objective of this study was to develop a methodology for measuring hemolymph glucose levels in crayfish of the genus Orconectes using a human glucometer. A secondary objective was to confirm through the use of this methodology that subjecting crayfish to acute stress in the form of a short‐term exposure to a basic solution or to chronic stress in the form of prolonged exposure to a nitrate‐containing solution, would increase hemolymph glucose levels. To measure the effects of acute exposure to a basic solution on crayfish hemolymph glucose levels, crayfish were placed in a beaker containing a basic solution (Ringer's buffer, pH=8) or a control solution (Ringer's buffer, pH=7) for a period of 10 seconds, after which time they were removed from the beakers and hemolymph was collected with a syringe. A ReliOn Prime glucometer (Wal‐Mart Stores, Inc.) was used to measured glucose levels (mg/dl) directly from the hemolymph. In order for the glucose levels to be within the sensitivity of the glucometer, hemolymph was mixed with high glucose Dulbecco's Modified Eagle Medium (DMEM; Gibco) in a 3:1 ratio. Glucose levels were determined by subtracting the levels of glucose measured in the DMEM solution from the glucose levels measured in the DMEM+hemolymph solution and correcting for the dilution factor. Crayfish acutely stressed through immersion in a basic solution had significantly higher hemolymph glucose levels compared to control crayfish (45.8 ± 5.9 vs 12.4 ± 3.8 mg/dl, two‐tailed t‐test; p<0.0001). To measure the effects of chronic exposure to a solution containing nitrate on crayfish hemolymph glucose levels, crayfish were housed in control tanks (no nitrate) or tanks containing 14 mg/L nitrate for one week, after which time hemolymph was drawn and glucose levels measured using the methodology described above. Crayfish chronically stressed through exposure to a nitrate‐containing solution had significantly higher hemolymph glucose levels compared to control crayfish (45 ± 14.3 vs 28.4 ± 8.8 mg/dl, two‐tailed t‐test; p=0.045). These results show that implementing this methodology human glucometers can be used to determine crayfish hemolymph glucose levels, making them an easy, inexpensive, and reliable alternative for this type of measurement. We were also able to show that hemolymph glucose levels are increased in both chronically and acutely stressed crayfish compared to control crayfish. These experiments can be used in physiology classrooms to demonstrate and discuss the role of the autonomic nervous system in an animal's response to stressful environmental conditions.Support or Funding InformationThis research was funded by the Denison University Department of Biology.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Accessing mental health care can be challenging, and minority groups often face additional barriers. This study investigates whether digital tools can enhance equality of access to mental health treatment. We evaluated a novel AI-enabled self-referral tool (a chatbot) designed to make entry to mental health treatment more accessible in a real-world setting. In a multi-site observational study, data were collected from 129,400 patients who referred to 28 separate NHS Talking Therapies services across England. Our results indicate that the tool led to a 15% increase in total referrals, which was significantly larger than the 6% baseline increase observed in matched services using traditional self-referral methods during the same time period. Importantly, the tool was particularly effective for minority groups, which included non-binary (235% increase), bisexual (30% increase), and ethnic minority individuals (31% increase). This paints a promising picture for the use of AI chatbots in mental healthcare and suggests they may be especially beneficial for demographic groups that experience barriers to accessing treatment in the traditional care systems. To better understand the reasons for this disproportional benefit for minority groups, we used thematic analysis and Natural Language Processing (NLP) models to evaluate qualitative feedback from 42,332 individuals who referred through the AI-enabled tool. We found that the tool's human-free nature and its ability to improve the perceived need for treatment were the main drivers for improved diversity. These findings suggest that AI-enabled chatbots have the potential to increase accessibility to mental health services for all, and to alleviate barriers faced by disadvantaged populations. The results have important implications for healthcare policy, clinical practice, and technology development.