Background Urinary tract infections (UTIs) are common in female dogs and recurrent infections often require investigation by transurethral cystoscopy. Hypothesis/Objectives Describe the findings of transurethral cystoscopy in dogs presented for recurrent urinary tract infections (RUTI). Animals Fifty‐three client‐owned dogs with RUTI were included in the study. Methods Retrospective study. Data collected from medical records included signalment, clinical findings, bladder wall culture, cystoscopic, and histopathologic findings. UTI was defined as: presence of compatible clinical signs and at least 2 out of 3 of the following criteria: (1) pyuria, (2) positive urine culture, (3) resolution of clinical signs with antibiotic treatment. Recurrence of UTI was defined as at least 2 episodes of UTI within 6 months or at least 3 or more in 1 year. Results The mean age at presentation was 3.8 years with a majority of female dogs (48/53), 40/48 of which were spayed. Main breeds were Labrador (10/53), Australian Shepherd (4/53), and Miniature Schnauzer (3/53). A hooded vulva was noted in 33/48 of females. Transurethral cystoscopy showed anomalies in 45/53 of cases: mucosal edema (19/53), vestibulovaginal septal remnant (15/48), lymphoid follicles (8/53), short urethra (6/53), and ectopic ureter (5/53). Urine culture at the time of cystoscopy was positive in 13/49. Bladder wall edema and ulceration were the most common findings on histopathology (25/39). Conclusion and Clinical Importance RUTI occurred more frequently in spayed female dogs. Transurethral cystoscopy is useful in the diagnosis and treatment of anomalies in dogs with RUTIs.

We present Why Are We Like This? (WAWLT), a mixed-initiative, co-creative storytelling game in which two players develop a story transcript by selecting and editing actions to perform and narrativize in an ongoing simulation. In this paper, we lay out the major technical features of WAWLT 's AI architecture-including story sifting via Datalog queries, social simulation, action suggestions, and player-specified but system-understandable author goals-and discuss how these features work together to produce a play experience that facilitates player creativity. CCS CONCEPTS • Software and its engineering → Interactive games; • Applied computing → Computer games.

Background: Platelet function testing is important for monitoring the effects of antiplatelet therapy but is not readily used due to time constraints for testing and the need for specialized equipment.Objectives: This study evaluated the effects of various storage methods on selected platelet function tests to determine if delayed platelet function testing is feasible in canine blood samples. Our hypotheses were that platelet function would not decline during storage and, thus, no differences in test results would be found over time.Methods: Thirteen healthy dogs were studied. Citrated blood samples were tested with a Platelet Function Analyzer-200 (PFA), which mimics high-shear conditions, using P2Y and CADP cartridges, after being held at room temperature for 2 h and refrigerated for 24 and 48 h. Plateletworks (PW), which measures aggregation based on platelet counting, was performed on an optical hematology analyzer using 10-min-old native samples, citrated samples held at room temperature for 3-4 h and refrigerated for 24 and 48 h, and samples stored in the preservative solution, AGGFix, up to 7 days.Results: PFA closure times increased with storage, especially with the P2Y cartridge.Median aggregation with fresh PW was 94%, and this was maintained at all time points (range of median values 88%-94%). Most samples showed decreased, yet still robust (>70%), aggregation with longer storage. Spontaneous aggregation in citrate was noted in most dogs. AGGFix stabilized platelet aggregates to allow for delayed testing.Conclusions: Delayed platelet function testing is feasible, but ranges of expected values may differ from tests using fresh samples.

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Digital games are hindered as an artform by significant technical barriers to entry, which exclude many would-be game developers from participating in this medium of expression. Casual creators for game design attempt to mitigate these barriers, but—like conventional game development tools—often require users to “work their way up” from low-level mechanics to high-level rhetorical or expressive goals, rather than allowing them to start with high-level rhetorical goals and “work their way down.” The constraint-based game generator Gemini is well-suited to the generation of games that meet high-level expressive goals, but is difficult for casual users to work with. We present Germinate, a mixed-initiative casual creator for rhetorical games that extends Gemini with a more approachable graphical user interface. A preliminary expert evaluation revealed that Germinate affords a playful approach to rhetorical game design by generating games that successfully meet user intent in surprising and novel ways.

We present Loose Ends, a mixed-initiative co-creative storytelling play experience in which a human player and an AI system work together to compose a story. Loose Ends specifically aims to provide computational support for managing multiple parallel plot threads and bringing these threads to satisfying conclusions—something that has proven difficult in past attempts to facilitate playful mixed-initiative storytelling. We describe the overall human-AI interaction loop in Loose Ends, including the implementation of the rules-based AI system that enables this interaction loop; discuss four examples of desirable mixed-initiative interactions that are possible in Loose Ends, but not in similar systems; and present results from a preliminary expert evaluation of Loose Ends. Altogether, we find that Loose Ends shows promise for creating a sense of coauthorship in the player while also mitigating the directionlessness reported by players of earlier systems.

Story sifters attempt to automatically or semi-automatically extract nuggets of compelling narrative content from vast chronicles of game or simulation events. Though sifting has successfully been used to enable novel computational narrative play experiences, its utility is limited by the fundamentally retrospective nature of existing sifters, which can only recognize storyful event sequences once they have fully played out. To address this limitation, we introduce Winnow: a domain-specific language for specifying story sifting patterns that can be executed incrementally to detect potentially storyful event sequences while they are still playing out. We evaluate Winnow by applying it to several specific use cases and show that it is well-suited to the implementation of prospective as well as retrospective narrative intelligence.

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