Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

Importance: Declining mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory.

Objective: Develop machine-learning models for identifying acquired neurologic morbidity while hospitalized with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers.

The role of temperature on the development of circadian rhythms in honey bee workers.

Circadian rhythms in honey bees are involved in various processes that impact colony survival. For example, young nurses take care of the brood constantly throughout the day and lack circadian rhythms. At the same time, foragers use the circadian clock to remember and predict food availability in subsequent days.

Parallel mechanisms of visual memory formation across distinct regions of the honey bee brain.

Visual learning is vital to the behavioral ecology of the Western honey bee (Apis mellifera). Honey bee workers forage for floral resources, a behavior that requires the learning and long-term memory of visual landmarks, but how these memories are mapped to the brain remains poorly understood. To address this gap in our understanding, we collected bees that successfully learned visual associations in a conditioned aversion paradigm and compared gene expression correlates of memory formation in the mushroom bodies, a higher-order sensory integration center classically thought to contribute to learning, as well as the optic lobes, the primary visual neuropil responsible for sensory transduction of visual information.

The Role of Colony Temperature in the Entrainment of Circadian Rhythms of Honey Bee Foragers.

Honey bees utilize their circadian rhythms to accurately predict the time of day. This ability allows foragers to remember the specific timing of food availability and its location for several days. Previous studies have provided strong evidence toward light/dark cycles being the primary Zeitgeber for honey bees.

Appetitive reversal learning differences of two honey bee subspecies with different foraging behaviors.

We aimed to examine mechanistically the observed foraging differences across two honey bee, , subspecies using the proboscis extension response assay. Specifically, we compared differences in appetitive reversal learning ability between honey bee subspecies: (Pollman), and (Skorikov) in a "common garden" apiary. It was hypothesized that specific learning differences could explain previously observed foraging behavior differences of these subspecies: switches between different flower color morphs in response to reward variability, and does not switch.