The Incomplete Social Contract: Elites and Ideals in the England of John Locke (1632~1704) and the Korea of Jeong Dojeon (1342~1398) and Heo Gyun (1569~1618)

Based on thorough evaluations of John Locke’s England (1632~1704), Jeong Dojeon’s Korea (1342~1398), and Heo Gyun’s Korea (1569~1618), it can be concluded that a social contract between a ruler and a ruler’s subjects existed in both the English and Joseon kingdoms. The idea that both kingdoms could have all people such as servants, farmers, and kings all know their political freedom disproves Hegel’s argument that one person knows freedom in Asia, and all people know freedom in Europe. This also shows that there is a broader human context in Asia and Europe and that the desire and drive for political freedom is inherent in human beings, regardless of what hemisphere they are from

Mahatma Gandhi and His Involvement in the Indian Independence Movement

I. Synthesis Essay………………………………..1 II. Primary Documents and Headnotes………...19 III. Textbook Critique……………………………...29 IV. New Textbook Entry…………………………..33 V. Bibliography………………………………….....37https://digitalcommons.bard.edu/history_mat/1022/thumbnail.jp

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression:Prediction Model Development Study

BACKGROUND: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. OBJECTIVE: This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. METHODS: This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets. RESULTS: In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months). CONCLUSIONS: We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.</p

Unsupervised Episode Generation for Graph Meta-learning

In this paper, we investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes for training, which however may not be possible to obtain in the real-world. Although few studies have been proposed to tackle the label-scarcity problem, they still rely on a limited amount of labeled data, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of Self-Supervised Learning (SSL) approaches on FSNC without labels, they mainly learn generic node embeddings without consideration on the downstream task to be solved, which may limit its performance. In this work, we propose unsupervised episode generation methods to benefit from their generalization ability for FSNC tasks while resolving label-scarcity problem. We first propose a method that utilizes graph augmentation to generate training episodes called g-UMTRA, which however has several drawbacks, i.e., 1) increased training time due to the computation of augmented features and 2) low applicability to existing baselines. Hence, we propose Neighbors as Queries (NaQ), which generates episodes from structural neighbors found by graph diffusion. Our proposed methods are model-agnostic, that is, they can be plugged into any existing graph meta-learning models, while not sacrificing much of their performance or sometimes even improving them. We provide theoretical insights to support why our unsupervised episode generation methodologies work, and extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards FSNC problems.Comment: 11 pages, 9 figures, preprin

Spatial regulation of RBOHD via AtECA4‐mediated recycling and clathrin‐mediated endocytosis contributes to ROS accumulation during salt stress response but not flg22‐induced immune response

Various environmental stresses can induce production of reactive oxygen species (ROS) to turn on signaling for proper responses to those stresses. Plasma membrane (PM)-localized respiratory burst oxidase homologs (RBOHs), in particular RBOHD, produce ROS via the post-translational activation upon abiotic and biotic stresses. Although the mechanisms of RBOHD activation upon biotic stress have been elucidated in detail, it remains elusive how salinity stress activates RBOHD. Here, we present evidence that trafficking of PM-localized RBOHD to endosomes and then its recycling back to the PM is critical for ROS accumulation upon salinity stress. ateca4 plants that were defective in recycling of proteins from endosomes to the PM and clc2-1 and chc2-1 plants that were defective in endocytosis showed a defect in salinity stress-induced ROS production. In addition, ateca4 plants showed a defect in transient accumulation of GFP:RBOHD to the PM at the early stage of salinity stress. By contrast, ateca4 plants showed no defect in the increase in the ROS level and accumulation of RBOHD to the PM upon flg22 treatment as wild-type plants. Based on these observations, we propose that factors involved in the trafficking machinery such as AtECA4 and clathrin are important players in salt stress-induced, but not flg22-induced, ROS accumulation. © 2021 Society for Experimental Biology and John Wiley & Sons Ltd.11Nsciescopu

Discovery of GSK3β Inhibitors through In Silico Prediction-and-Experiment Cycling Strategy, and Biological Evaluation

Direct inhibitors of glycogen synthase kinase 3β (GSK3β) have been investigated and reported for the past 20 years. In the search for novel scaffold inhibitors, 3000 compounds were selected through structure-based virtual screening (SBVS), and then high-throughput enzyme screening was performed. Among the active hit compounds, pyrazolo [1,5-a]pyrimidin-7-amine derivatives showed strong inhibitory potencies on the GSK3β enzyme and markedly activated Wnt signaling. The result of the molecular dynamics (MD) simulation, enhanced by the upper-wall restraint, was used as an advanced structural query for the SBVS. In this study, strong inhibitors designed to inhibit the GSK3β enzyme were discovered through SBVS. Our study provides structural insights into the binding mode of the inhibitors for further lead optimization