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https://www.selleckchem.com/products/vx-661.html The adapted version of ISA allows the analysis of an individual classification dataset by a 2-D hardness embedding, which provides a visualization of the data according to the difficulty level of its individual observations. This allows deeper analyses of the relationships between instance hardness and predictive performance of classifiers. We also provide an open-access Python package named PyHard, which encapsulates the adapted ISA and provides an interactive visualization interface. We illustrate through case studies how our tool can provide insights about data quality and algorithm performance in the presence of challenges such as noisy and biased data. Clinical and population health recommendations are derived from studies that include self-report. Differences in question wording and response scales may significantly affect responses. We conducted a methodological review assessing variation in event definition(s), context (i.e., work- versus free-day), and timeframe (e.g., "in the past 4 weeks") of sleep timing/duration questions. We queried databases of sleep, medicine, epidemiology, and psychology for survey-based studies and/or publications with sleep duration/timing questions. The text of these questions was thematically analyzed. We identified 53 surveys with sample sizes ranging from 93 to 1,185,106. For sleep duration, participants reported nocturnal sleep (24/44), sleep in the past 24-hours (14/44), their major sleep episode (3/44), or answered unaided (3/44). For bedtime, participants reported time into bed (19/47), first attempt to sleep (16/40), or fall-asleep time (12/47). For wake-time, participants reported wake-up time (30/43), the time they "get up" (7/43), or their out-of-bed time (6/43). Context guidance appeared in 18/44 major sleep duration, 35/47 bedtime, and 34/43 wake-time questions. Timeframe was provided in 8/44 major sleep episode duration, 16/47 bedtime, and 10/43 wake-time questions. O

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