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

Detection of COVID-19 epidemic outbreak using machine learning

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic

Deubiquitination Reactions on the Proteasome for Proteasome Versatility

The 26S proteasome, a master player in proteolysis, is the most complex and meticulously contextured protease in eukaryotic cells. While capable of hosting thousands of discrete substrates due to the selective recognition of ubiquitin tags, this protease complex is also dynamically checked through diverse regulatory mechanisms. The proteasome’s versatility ensures precise control over active proteolysis, yet prevents runaway or futile degradation of many essential cellular proteins. Among the multi-layered processes regulating the proteasome’s proteolysis, deubiquitination reactions are prominent because they not only recycle ubiquitins, but also impose a critical checkpoint for substrate degradation on the proteasome. Of note, three distinct classes of deubiquitinating enzymes—USP14, RPN11, and UCH37—are associated with the 19S subunits of the human proteasome. Recent biochemical and structural studies suggest that these enzymes exert dynamic influence over proteasome output with limited redundancy, and at times act in opposition. Such distinct activities occur spatially on the proteasome, temporally through substrate processing, and differentially for ubiquitin topology. Therefore, deubiquitinating enzymes on the proteasome may fine-tune the degradation depending on various cellular contexts and for dynamic proteolysis outcomes. Given that the proteasome is among the most important drug targets, the biology of proteasome-associated deubiquitination should be further elucidated for its potential targeting in human diseases. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.1

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

Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs

Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat–interval–texture convolutional neural network (BIT-CNN) model, we aimed to learn the comprehensible characteristics of beat shape and inter-beat patterns over time for arrhythmia classification. The proposed approach allows feature embedding vectors to provide interpretable time-morphology patterns focused at each step of the learning process. In addition, this method reduces the number of model parameters needed to be trained and aids visual interpretation, while maintaining similar performance to other CNN-based approaches to arrhythmia classification. For experiments, we used the PhysioNet/CinC Challenge 2017 dataset and achieved an overall F1_NAO of 81.75% and F1_NAOP of 76.87%, which are comparable to those of the state-of-the-art methods for variable-length ECGs

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R⁻R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6⁻17% better accuracy for ECG and 4⁻14% for PPG in drowsy/awake classification