The Impact of Sample Size in Cross-Classified Multiple Membership Multilevel Models

A simulation study was conducted to examine parameter recovery in a cross-classified multiple membership multilevel model. No substantial relative bias was identified for the fixed effect or level-one variance component estimates. However, the level-two cross-classification multiple membership factor variance components were substantially biased with relatively fewer groups

Effective Targeted Attacks for Adversarial Self-Supervised Learning

Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised AT have mostly focused on implementing self-supervised learning (SSL) frameworks, which maximize the instance-wise classification loss to generate adversarial examples. However, we observe that simply maximizing the self-supervised training loss with an untargeted adversarial attack often results in generating ineffective adversaries that may not help improve the robustness of the trained model, especially for non-contrastive SSL frameworks without negative examples. To tackle this problem, we propose a novel positive mining for targeted adversarial attack to generate effective adversaries for adversarial SSL frameworks. Specifically, we introduce an algorithm that selects the most confusing yet similar target example for a given instance based on entropy and similarity, and subsequently perturbs the given instance towards the selected target. Our method demonstrates significant enhancements in robustness when applied to non-contrastive SSL frameworks, and less but consistent robustness improvements with contrastive SSL frameworks, on the benchmark datasets.Comment: NeurIPS 202

Learning Transferable Adversarial Robust Representations via Multi-view Consistency

Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples. While some recent works have shown that a combination of adversarial learning and meta-learning could enhance the robustness of a meta-learner against adversarial attacks, they fail to achieve generalizable adversarial robustness to unseen domains and tasks, which is the ultimate goal of meta-learning. To address this challenge, we propose a novel meta-adversarial multi-view representation learning framework with dual encoders. Specifically, we introduce the discrepancy across the two differently augmented samples of the same data instance by first updating the encoder parameters with them and further imposing a novel label-free adversarial attack to maximize their discrepancy. Then, we maximize the consistency across the views to learn transferable robust representations across domains and tasks. Through experimental validation on multiple benchmarks, we demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains, achieving over 10\% robust accuracy improvements against previous adversarial meta-learning baselines.Comment: *Equal contribution (Author ordering determined by coin flip). NeurIPS SafetyML workshop 2022, Under revie

Uric acid regulates α-synuclein transmission in Parkinsonian models

Ample evidence demonstrates that α-synuclein (α-syn) has a critical role in the pathogenesis of Parkinson’s disease (PD) with evidence indicating that its propagation from one area of the brain to others may be the primary mechanism for disease progression. Uric acid (UA), a natural antioxidant, has been proposed as a potential disease modifying candidate in PD. In the present study, we investigated whether UA treatment modulates cell-to-cell transmission of extracellular α-syn and protects dopaminergic neurons in the α-syn-enriched model. In a cellular model, UA treatment decreased internalized cytosolic α-syn levels and neuron-to-neuron transmission of α-syn in donor-acceptor cell models by modulating dynamin-mediated and clathrin-mediated endocytosis. Moreover, UA elevation in α-syn-inoculated mice inhibited propagation of extracellular α-syn which decreased expression of phosphorylated α-syn in the dopaminergic neurons of the substantia nigra leading to their increased survival. UA treatment did not lead to change in markers related with autophagolysosomal and microglial activity under the same experimental conditions. These findings suggest UA may control the pathological conditions of PD via additive mechanisms which modulate the propagation of α-syn

S6 kinase 1 plays a key role in mitochondrial morphology and cellular energy flow

Mitochondrial morphology, which is associated with changes in metabolism, cell cycle, cell development and cell death, is tightly regulated by the balance between fusion and fission. In this study, we found that S6 kinase 1 (S6K1) contributes to mitochondrial dynamics, homeostasis and function. Mouse embryo fibroblasts lacking S6K1 (S6K1 KO MEFs) exhibited more fragmented mitochondria and a higher level of Dynamin related protein 1 (Drp1) and active Drp1 (pS616) in both whole cell extracts and mitochondria' fraction. In addition, there was no evidence for autophagy and mitophagy induction in S6K1 depleted cells. Glycolysis and mitochondrial respiratory activity was higher in S6K1-KO MEFs, whereas OxPhos ATP production was not altered. However, inhibition of Drp1 by Mdivi1 (Drp1 inhibitor) resulted in higher OxPhos ATP production and lower mitochondrial membrane potential. Taken together the depletion of S6K1 increased Drpl-mediated fission, leading to the enhancement of glycolysis. The fission form of mitochondria resulted in lower yield for OxPhos ATP production as well as in higher mitochondrial membrane potential. Thus, these results have suggested a potential role of S6K1 in energy metabolism by modulating mitochondrial respiratory capacity and mitochondrial morphology.

GSK3B induces autophagy by phosphorylating ULK1

Unc-51-like autophagy activating kinase 1 (ULK1), a mammalian homolog of the yeast kinase Atg1, has an essential role in autophagy induction. In nutrient and growth factor signaling, ULK1 activity is regulated by various posttranslational modifications, including phosphorylation, acetylation, and ubiquitination. We previously identified glycogen synthase kinase 3 beta (GSK3B) as an upstream regulator of insulin withdrawal-induced autophagy in adult hippocampal neural stem cells. Here, we report that following insulin withdrawal, GSK3B directly interacted with and activated ULK1 via phosphorylation of S405 and S415 within the GABARAP-interacting region. Phosphorylation of these residues facilitated the interaction of ULK1 with MAP1LC3B and GABARAPL1, while phosphorylation-defective mutants of ULK1 failed to do so and could not induce autophagy flux. Furthermore, high phosphorylation levels of ULK1 at S405 and S415 were observed in human pancreatic cancer cell lines, all of which are known to exhibit high levels of autophagy. Our results reveal the importance of GSK3B-mediated phosphorylation for ULK1 regulation and autophagy induction and potentially for tumorigenesis. © 2021, The Author(s).1

Combined electrodialysis and photo-electro-chlorination for energy efficient control of brine water

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Effects of Wavelength and pH on Generation of Radical Species and Radical Speciation in UV/Chlorine Processes

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Effects of Electrochemical Self-doping Conditions on Photoelectrochemical Activities of TiO2 Nanotube Array

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Electrochemically self-doped Nb2O5 nanotube array for photoelectrochemical degradation of aqueous organic compounds

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