Exosome and virus infection

Exosomes are messengers of intercellular communication in monolayer vesicles derived from cells. It affects the pathophysiological process of the body in various diseases, such as tumors, inflammation, and infection. It has been confirmed that exosomes are similar to viruses in biogenesis, and exosome cargo is widely involved in many viruses’ replication, transmission, and infection. Simultaneously, virus-associated exosomes can promote immune escape and activate the antiviral immune response of the body, which bidirectionally modulates the immune response. This review focuses on the role of exosomes in HIV, HBV, HCV, and SARS-CoV-2 infection and explores the prospects of exosome development. These insights may be translated into therapeutic measures for viral infections and reduce the disease burden

PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment

Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.Comment: DAC 2023 accepeted publication, short version was published on AAAI 2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferenc

PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems. Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration. However, they either achieve a low reduction ratio and suffer from high latency due to limited computation and communication saving, or are power-hungry as existing works mainly focus on general computing platforms such as CPUs and GPUs. In this work, as the first attempt, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration, by integrating hardware latency of the cryptographic building block into the DNN loss function to achieve high energy efficiency, accuracy, and security guarantee. Instead of heuristically checking the model sensitivity after a DNN is well-trained (through deleting or dropping some non-polynomial operators), our key design principle is to em enforce exactly what is assumed in the DNN design -- training a DNN that is both hardware efficient and secure, while escaping the local minima and saddle points and maintaining high accuracy. More specifically, we propose a straight through polynomial activation initialization method for cryptographic hardware friendly trainable polynomial activation function to replace the expensive 2P-ReLU operator. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) platform

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 times ReLU budget reduction.Comment: ICCV 2023 accepeted publicatio

Survey of Tyrosine Kinase Signaling Reveals ROS Kinase Fusions in Human Cholangiocarcinoma

Cholangiocarcinoma, also known as bile duct cancer, is the second most common primary hepatic carcinoma with a median survival of less than 2 years. The molecular mechanisms underlying the development of this disease are not clear. To survey activated tyrosine kinases signaling in cholangiocarcinoma, we employed immunoaffinity profiling coupled to mass spectrometry and identified DDR1, EPHA2, EGFR, and ROS tyrosine kinases, along with over 1,000 tyrosine phosphorylation sites from about 750 different proteins in primary cholangiocarcinoma patients. Furthermore, we confirmed the presence of ROS kinase fusions in 8.7% (2 out of 23) of cholangiocarcinoma patients. Expression of the ROS fusions in 3T3 cells confers transforming ability both in vitro and in vivo, and is responsive to its kinase inhibitor. Our data demonstrate that ROS kinase is a promising candidate for a therapeutic target and for a diagnostic molecular marker in cholangiocarcinoma. The identification of ROS tyrosine kinase fusions in cholangiocarcinoma, along with the presence of other ROS kinase fusions in lung cancer and glioblastoma, suggests that a more broadly based screen for activated ROS kinase in cancer is warranted

Adaptabilities of three mainstream short-term wind power forecasting methods

Variability and intermittency of wind is the main challenge for making a reliable wind power forecasting (WPF). Meteorological and topological complexities make it even harder to fit any forecasting algorithm to one particular case. This paper presents the comparison of three short term WPF models based on three wind farms in China with different terrains and climates. The sensitivity effects of training samples on forecasting performance are investigated in terms of sample size, sample quality, and sample time scale. Then, their adaptabilities and modeling efficiency are also discussed under different seasonal and topographic conditions. Results show that (1) radial basis function (RBF) and support vector machine (SVM) generally have higher prediction accuracy than that of genetic algorithm back propagation (GA-BP), but different models show advantages in different seasons and terrains. (2) WPF taking a month as the training time interval can increase the accuracy of short-term WPF. (3) The change of sample number for the GA-BP and RBF is less sensitive than that of the SVM. (4) GA-BP forecasting accuracy is equally sensitive to all size of training samples. RBF and SVM have different sensibility to different size of training samples. This study can quantitatively provide reference for choosing the appropriate WPF model and further optimization for specific engineering cases, based on better understanding of algorithm theory and its adaptability. In this way, WPF users can select the suitable algorithm for different terrains and climates to achieve reliable prediction for market clearing, efficient pricing, dispatching, etc.</p