Foods and beverages provided in out of school hours care services: an observational study.

INTRODUCTION: Out of school hours care (OSHC) is a fast-growing childcare setting in Australia, however the types of foods and beverages offered are relatively unknown. This study describes the food and beverages offered and investigates sector-level and setting-level factors which may impact OSHC in meeting the Australian Dietary Guidelines (ADG). METHODS: This cross-sectional, observational study was conducted in 89 OSHC services (between 2018 and 2019). Food and beverages offered, kitchen facilities and menus were captured via direct observation. Foods were categorised into five food groups or discretionary foods, based on the ADG, and frequencies determined. Short interviews with OSHC directors ascertained healthy eating policies, staff training, food quality assessment methods and food budgets. Fisher's exact test explored the influence of sector-level and setting-level factors on food provision behaviours. RESULTS: Discretionary foods (1.5 ± 0.68) were offered more frequently than vegetables (0.82 ± 0.80) (p < .001), dairy (0.97 ± 0.81) (p = .013) and lean meats (0.22 ± 0.54) (p < .001). OSHC associated with long day care and reported using valid food quality assessment methods offered more lean meats (p= .002, and p= .004). Larger organisations offered more vegetables (p = .015) and discretionary foods (p= .007). Menus with clearly worded instructions to provide fruits and vegetables daily offered more fruit (p= .009), vegetables (p < .001) and whole grains (p= .003). No other sector or setting-level factors were associated with services aligning with the ADG. CONCLUSION: Future interventions could benefit from trialling menu planning training and tools to assist OSHC services in NSW meet the ADG requirements.

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

AIMS: To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. METHODS AND RESULTS: One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD: 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96 ° ) errors which were comparable to inter-reader differences (P > 0.71). 84-97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable (P > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. CONCLUSION: A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

Social vs. Nonsocial Housing Differentially Affects Perseverative Behavior in Rats (Ratus norvegicus).

Perseverance, also commonly referred to as grit or industriousness, is the continued effort exerted to complete goal-directed tasks. Many factors, such as stress, can contribute to perseverative behavior, but the role of sociality on perseverance in animal models has not been studied. In this experiment, perseverance was measured in Long-Evans rats; half of which were socially housed (SH) and the other half were nonsocially housed (NSH). Rats were placed in a continuous T-maze; one arm of the maze contained an unobstructed low value reward and the other arm contained a high value reward blocked by a barrier that progressively increased in height across testing sessions. We will hereon refer to the low value reward and high value reward as the low reward and the high reward, respectively. Perseverative behavior was assessed by time spent interacting with the barrier and trials were characterized as either adaptive perseverative trials (high reward obtainment) and maladaptive perseverative trials (low reward obtainment after abandoning attempts to overcome the high reward barrier). SH and NSH rats were equally proficient at overcoming a physical barrier to obtain a higher-valued reward, but the NSH rats spent more time interacting with the barriers during maladaptive perseverative trials than SH rats. NSH rats thus exhibited prolonged efforts to overcome the barrier only to ultimately travel to the low reward option. In contrast, SH rats selected the low reward option earlier in the trial and did not maladaptively perseverate without obtaining the high reward. Putative evidence for increased perseverance in NSH rats is explained in the context of maladaptive perseverative behavior rather than perseverance per se. Increased adaptability and acquisition of task-set in SH rats suggests a role of social housing in advantageous decision making.