Tuesday, May 28, 2019

190601 Under Renovation: Large-Scale Societal Events Induce Shifts between Moral Ideologies

Title:
Under Renovation: Large-Scale Societal Events Induce Shifts between Moral Ideologies


Time:
06/01 (Sat.) 6 pm PDT, 7 pm MDT, 8 pm CDT, 9 pm EDT
06/02 (Sun.) 9 am Taiwan

Keywords:
Psychology, Social Psychology, Morality, Culture, Social change

  為配合本研究發表時程,本次錄影將延遲上傳

Abstract:
The literature of moral foundations theory has revealed a wide array of cross-sectional differences in how societies conceptualize morality. Yet less attention has been paid to cross-temporal differences—thus, changes—in collective moral ideology within a given society. Here, we tested the hypothesis that morality is consistently redefined—renovated—in the service of the interest of the society in the face of widespread social changes. Using the U.S. President’s congressional speeches (Study 1), social media big-data (Study 2), and multi-wave questionnaires (Study 3), we report evidence of this kind of moral renovations of Americans’ moral ideologies during the 9/11 terrorist attack (Study 1), the 2007 economic recession (Study 2), and the 2016 presidential power transition (Study 3). We address three alternative explanations—participants became conservative under threat, desired to justify the status quo, or were experiencing non-foundation-specific moral hyper-activation—and discuss the findings in terms of the malleable and functional nature of morality.

Sunday, May 12, 2019

190518 Machine Learning Approaches for Personalized Treatment Selection in Psychiatry

Title:
Machine Learning Approaches for Personalized Treatment Selection in Psychiatry

Speaker:
吳其炘 (Chi-Shin Wu), MD, PhD, National Taiwan University Hospital / Harvard

Time:
05/18 (Sat.) 5 pm PDT, 6 pm MDT, 7 pm CDT, 8 pm EDT
05/19 (Sun.) 8 am Taiwan

Keywords:
Medicine, Epidemiology, Pharmacoepidemiology



Abstract:
Identifying the best regimen for individual patient remains challenging, clinicians need to rely on trial and error. The effect of using machine learning to select personalized treatments needs to be assessed and validated.
This study utilized Taiwan's National Health Insurance Research Database. We focused on patients with schizophrenia, which is one of the most severe mental disorders. Prediction models based on Super Learner algorithms were used. We identified the top 3 antipsychotic regimens with the highest probabilities of treatment success. In the test dataset, we compared the rates of treatment success between patients treated with machine-selected regimens and those treated with non-selected regimens.
The results showed patients treated with machine-selected regimens had 43.0% treatment success, while those treated with nonselected regimens have 27.7% success. In conclusions, that the machine learning-based prediction models had acceptable prediction accuracies, which suggested that the use of machine-selected regimens will increase the treatment success rate for patients with schizophrenia.

Saturday, May 4, 2019

190511 Understanding Biology by Replaying the Tape of Life: Experimental Evolution of Escherichia coli as an Example

Title:
Understanding Biology by Replaying the Tape of Life: Experimental Evolution of Escherichia coli as an Example

Speaker:
何韋進 (Wei-Chin Ho), PhD, Arizona State University

Time:
05/11 (Sat.) 8 pm PDT, 9 pm MDT, 10 pm CDT, 11 pm EDT
05/12 (Sun.) 11 am Taiwan

Keywords:
Biology, Evolutionary Biology, Microbiology

Abstract:
Where does the diversity of life come from? One way to study this question is to analyze natural data. However, the variables in natural system may be convoluted, which can hinder our ability to identify fundamental principles in evolution. To overcome it, evolutionary biologists started to perform experimental evolution in the lab, where the abiotic and biotic factors can be well controlled, and the evolutionary outcomes can be easily tracked. Particularly, we experimentally evolved bacteria Escherichia coli and used high-throughput sequencing to study how mutation rates affect evolutionary outcomes. Interestingly, the strains with high mutation rates accumulate mutations in higher rates (4-20 folds) and exhibit higher levels of mutational parallelism but do not show significant evidence for faster fitness improvement. These results suggest that clonal interference and evolvability of lower mutation rates are important in evolution. Moreover, the predictability of genomic evolution and fitness evolution are not necessarily coupled.