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ight. The insights provided by these findings have important implications for planning anti-bullying strategies in school settings in the Nepalese context.Visually inferring material properties is crucial for many tasks, yet poses significant computational challenges for biological vision. Liquids and gels are particularly challenging due to their extreme variability and complex behaviour. We reasoned that measuring and modelling viscosity perception is a useful case study for identifying general principles of complex visual inferences. In recent years, artificial Deep Neural Networks (DNNs) have yielded breakthroughs in challenging real-world vision tasks. However, to model human vision, the emphasis lies not on best possible performance, but on mimicking the specific pattern of successes and errors humans make. We trained a DNN to estimate the viscosity of liquids using 100.000 simulations depicting liquids with sixteen different viscosities interacting in ten different scenes (stirring, pouring, splashing, etc). Vismodegib We find that a shallow feedforward network trained for only 30 epochs predicts mean observer performance better than most individual observers. Thisistributed vs. localized representations.

The Government of Ontario, Canada, announced hospital funding reforms in 2011, including Quality-based Procedures (QBPs) involving pre-set funds for managing patients with specific diagnoses/procedures. A key goal was to improve quality of care across the jurisdiction.

Interrupted time series evaluated the policy change, focusing on four QBPs (congestive heart failure, hip fracture surgery, pneumonia, prostate cancer surgery), on patients hospitalized 2010-2017. Outcomes included return to hospital or death within 30 days, acute length of stay (LOS), volume of admissions, and patient characteristics.

At 2 years post-QBPs, the percentage of hip fracture patients who returned to hospital or died was 3.13% higher in absolute terms (95% CI 0.37% to 5.89%) than if QBPs had not been introduced. There were no other statistically significant changes for return to hospital or death. For LOS, the only statistically significant change was an increase for prostate cancer surgery of 0.33 days (95% CI 0.07 to 0.59). Volume increased for congestive heart failure admissions by 80 patients (95% CI 2 to 159) and decreased for hip fracture surgery by 138 patients (95% CI -183 to -93) but did not change for pneumonia or prostate cancer surgery. The percentage of patients who lived in the lowest neighborhood income quintile increased slightly for those diagnosed with congestive heart failure (1.89%; 95% CI 0.51% to 3.27%) and decreased for those who underwent prostate cancer surgery (-2.08%; 95% CI -3.74% to -0.43%).

This policy initiative involving a change to hospital funding for certain conditions was not associated with substantial, jurisdictional-level changes in access or quality.

This policy initiative involving a change to hospital funding for certain conditions was not associated with substantial, jurisdictional-level changes in access or quality.Humans expect downwards moving objects to accelerate and upwards moving objects to decelerate. These results have been interpreted as humans maintaining an internal model of gravity. We have previously suggested an interpretation of these results within a Bayesian framework of perception earth gravity could be represented as a Strong Prior that overrules noisy sensory information (Likelihood) and therefore attracts the final percept (Posterior) very strongly. Based on this framework, we use published data from a timing task involving gravitational motion to determine the mean and the standard deviation of the Strong Earth Gravity Prior. To get its mean, we refine a model of mean timing errors we proposed in a previous paper (Jörges & López-Moliner, 2019), while expanding the range of conditions under which it yields adequate predictions of performance. This underscores our previous conclusion that the gravity prior is likely to be very close to 9.81 m/s2. To obtain the standard deviation, we identify different sources of sensory and motor variability reflected in timing errors. We then model timing responses based on quantitative assumptions about these sensory and motor errors for a range of standard deviations of the earth gravity prior, and find that a standard deviation of around 2 m/s2 makes for the best fit. This value is likely to represent an upper bound, as there are strong theoretical reasons along with supporting empirical evidence for the standard deviation of the earth gravity being lower than this value.Germinal studies have described the prevalence of sex-based harassment in high schools and its associations with adverse outcomes in adolescents. Studies have focused on students, with little attention given to the actions of high schools themselves. Though journalists responded to the #MeToo movement by reporting on schools' betrayal of students who report misconduct, this topic remains understudied by researchers. Gender harassment is characterized by sexist remarks, sexually crude or offensive behavior, gender policing, work-family policing, and infantilization. Institutional betrayal is characterized by the failure of an institution, such as a school, to protect individuals dependent on the institution. We investigated high school gender harassment and institutional betrayal reported retrospectively by 535 current undergraduates. Our primary aim was to investigate whether institutional betrayal moderates the relationship between high school gender harassment and current trauma symptoms. In our pre-registebetrayal are independently associated with trauma symptoms, suggesting that intervention should target both phenomena.

To develop and apply a natural language processing algorithm for characterization of patients diagnosed with chronic pancreatitis in a diverse integrated U.S. healthcare system.

Retrospective cohort study including patients initially diagnosed with chronic pancreatitis (CP) within a regional integrated healthcare system between January 1, 2006 and December 31, 2015. Imaging reports from these patients were extracted from the electronic medical record system and split into training, validation and implementation datasets. A natural language processing (NLP) algorithm was first developed through the training dataset to identify specific features (atrophy, calcification, pseudocyst, cyst and main duct dilatation) from free-text radiology reports. The validation dataset was applied to validate the performance by comparing against the manual chart review. The developed algorithm was then applied to the implementation dataset. We classified patients with calcification(s) or ≥2 radiographic features as advanced CP.