Search

Your search keyword '"Mu, Xin"' showing total 950 results
Start Over Author "Mu, Xin" Search Limiters Full Text
950 results on '"Mu, Xin"'

1. Multi-Designated Detector Watermarking for Language Models

Author

Huang, Zhengan, Zeng, Gongxian, Mu, Xin, Wang, Yu, and Yu, Yue

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

In this paper, we initiate the study of \emph{multi-designated detector watermarking (MDDW)} for large language models (LLMs). This technique allows model providers to generate watermarked outputs from LLMs with two key properties: (i) only specific, possibly multiple, designated detectors can identify the watermarks, and (ii) there is no perceptible degradation in the output quality for ordinary users. We formalize the security definitions for MDDW and present a framework for constructing MDDW for any LLM using multi-designated verifier signatures (MDVS). Recognizing the significant economic value of LLM outputs, we introduce claimability as an optional security feature for MDDW, enabling model providers to assert ownership of LLM outputs within designated-detector settings. To support claimable MDDW, we propose a generic transformation converting any MDVS to a claimable MDVS. Our implementation of the MDDW scheme highlights its advanced functionalities and flexibility over existing methods, with satisfactory performance metrics.

Published
2024

2. HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks

Author

Han, Xiaoxu, Mu, Xin, and Zhong, Jinghui

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence
Abstract

Planning the movement of the sink to maximize the lifetime in wireless sensor networks is an essential problem of great research challenge and practical value. Many existing mobile sink techniques based on mathematical programming or heuristics have demonstrated the feasibility of the task. Nevertheless, the huge computation consumption or the over-reliance on human knowledge can result in relatively low performance. In order to balance the need for high-quality solutions with the goal of minimizing inference time, we propose a new framework combining heterogeneous graph neural network with deep reinforcement learning to automatically construct the movement path of the sink. Modeling the wireless sensor networks as heterogeneous graphs, we utilize the graph neural network to learn representations of sites and sensors by aggregating features of neighbor nodes and extracting hierarchical graph features. Meanwhile, the multi-head attention mechanism is leveraged to allow the sites to attend to information from sensor nodes, which highly improves the expressive capacity of the learning model. Based on the node representations, a greedy policy is learned to append the next best site in the solution incrementally. We design ten types of static and dynamic maps to simulate different wireless sensor networks in the real world, and extensive experiments are conducted to evaluate and analyze our approach. The empirical results show that our approach consistently outperforms the existing methods on all types of maps., Comment: Preprint. Under review

Published
2024

3. SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models

Author

Luo, Jinglong, Zhang, Yehong, Zhang, Zhuo, Zhang, Jiaqi, Mu, Xin, Wang, Hui, Yu, Yue, and Xu, Zenglin

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Cryptography and Security
Abstract

With the growing use of large language models hosted on cloud platforms to offer inference services, privacy concerns are escalating, especially concerning sensitive data like investment plans and bank account details. Secure Multi-Party Computing (SMPC) emerges as a promising solution to protect the privacy of inference data and model parameters. However, the application of SMPC in Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often leads to considerable slowdowns or declines in performance. This is largely due to the multitude of nonlinear operations in the Transformer architecture, which are not well-suited to SMPC and difficult to circumvent or optimize effectively. To address this concern, we introduce an advanced optimization framework called SecFormer, to achieve fast and accurate PPI for Transformer models. By implementing model design optimization, we successfully eliminate the high-cost exponential and maximum operations in PPI without sacrificing model performance. Additionally, we have developed a suite of efficient SMPC protocols that utilize segmented polynomials, Fourier series and Goldschmidt's method to handle other complex nonlinear functions within PPI, such as GeLU, LayerNorm, and Softmax. Our extensive experiments reveal that SecFormer outperforms MPCFormer in performance, showing improvements of $5.6\%$ and $24.2\%$ for BERT$_{\text{BASE}}$ and BERT$_{\text{LARGE}}$, respectively. In terms of efficiency, SecFormer is 3.56 and 3.58 times faster than Puma for BERT$_{\text{BASE}}$ and BERT$_{\text{LARGE}}$, demonstrating its effectiveness and speed., Comment: Accepted by ACL 2024

Published
2024

4. EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection

Author

Mu, Xin, Wang, Yu, Huang, Zhengan, Lai, Junzuo, Zhang, Yehong, Wang, Hui, and Yu, Yue

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learning (ML) models, being highly valuable digital assets, have gained significant attention for IP protection. This paper introduces a practical encryption-based framework called \textit{EncryIP}, which seamlessly integrates a public-key encryption scheme into the model learning process. This approach enables the protected model to generate randomized and confused labels, ensuring that only individuals with accurate secret keys, signifying authorized users, can decrypt and reveal authentic labels. Importantly, the proposed framework not only facilitates the protected model to multiple authorized users without requiring repetitive training of the original ML model with IP protection methods but also maintains the model's performance without compromising its accuracy. Compared to existing methods like watermark-based, trigger-based, and passport-based approaches, \textit{EncryIP} demonstrates superior effectiveness in both training protected models and efficiently detecting the unauthorized spread of ML models.

Published
2023

5. Migrant Resettlement by Evolutionary Multi-objective Optimization

Author

Liu, Dan-Xuan, Gu, Yu-Ran, Qian, Chao, Mu, Xin, and Tang, Ke

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly in recent years, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem. We implement MR-EMO using three MOEAs, the popular NSGA-II, MOEA/D as well as the theoretically grounded GSEMO. To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO-SR, using matrix-swap mutation and repair mechanism, which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO.

Published
2023

6. Model Provenance via Model DNA

Author

Mu, Xin, Wang, Yu, Zhang, Yehong, Zhang, Jiaqi, Wang, Hui, Xiang, Yang, and Yu, Yue

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is used). This paper focuses on a novel problem within this field, namely Model Provenance (MP), which concerns the relationship between a target model and its pre-training model and aims to determine whether a source model serves as the provenance for a target model. This is an important problem that has significant implications for ensuring the security and intellectual property of machine learning models but has not received much attention in the literature. To fill in this gap, we introduce a novel concept of Model DNA which represents the unique characteristics of a machine learning model. We utilize a data-driven and model-driven representation learning method to encode the model's training data and input-output information as a compact and comprehensive representation (i.e., DNA) of the model. Using this model DNA, we develop an efficient framework for model provenance identification, which enables us to identify whether a source model is a pre-training model of a target model. We conduct evaluations on both computer vision and natural language processing tasks using various models, datasets, and scenarios to demonstrate the effectiveness of our approach in accurately identifying model provenance.

Published
2023

7. Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging.

Author

Ellison, Jacob, Kim, Kisoo, Li, Yi, Mu, Xin, Glenn, Orit, Ozhinsky, Eugene, Peyvandi, Shabnam, and Xu, Duan

Subjects
Thermometry, brain, fetal, magnetic, resonance
Abstract

BACKGROUND: T2-weighted Single Shot Fast Spin Echo (SSFSE) scans at 3 Tesla (3T) are increasingly used to image fetal pathology due to their excellent tissue contrast resolution and signal-to-noise ratio (SNR). Temperature changes that may occur in response to radio frequency (RF) pulses used for these sequences at 3T have not been studied in human fetal brains. To evaluate the safety of T2-weighted SSFSE for fetal brains at 3T, magnetic resonance (MR) thermometry was used to measure relative temperature changes in a typical clinical fetal brain MR exam. METHODS: Relative temperature was estimated using sets of gradient recalled echo (GRE) images acquired before and after T2-weighted SSFSE images which lasted 27.47±8.19 minutes. Thirty-one fetuses with cardiac abnormalities, and 20 healthy controls were included in this study. Fetal brain temperature was estimated by proton resonance frequency (PRF) thermometry and compared to the estimated temperature in the gluteal muscle of the mother. Seven scans with excessive motion were excluded. Local outlier factor (LOF) was performed to remove 12 additional scans with spurious phase measurements due to motion degradation and potential field drift. Linear regression was performed to determine if temperature changes are dependent on the rate of energy deposition during the scan. RESULTS: For the 32 participants used in the analysis, 17 with cardiac abnormalities and 15 healthy controls, the average relative fetal temperate change was 0.19±0.73 ℃ higher than the mother, with no correlation between relative temperature change and the rate of images acquired during the scans (regression coefficient =-0.05, R-squared =0.05, P=0.22, F-statistic =1.60). The difference in the relative temperature changes between the fetal brain and mothers gluteal tissue in the healthy controls was on average 0.08 ℃ lower and found not to be statistically different (P=0.76) to the group with cardiac abnormalities. CONCLUSIONS: Our results indicate that the estimated relative temperature changes of the fetal brain compared to the mothers gluteal tissue from RF pulses during the course of the T2-weighted SSFSE fetal MR exam are minimal. The differences in acquired phase between these regions through the exam were found not to be statistically different. These findings support that fetal brain imaging at 3T is within FDA limits and safe.

Published
2023

8. Epidemiological features and temporal trends of the co-infection between HIV and tuberculosis, 1990–2021: findings from the Global Burden of Disease Study 2021

Author

Shun-Xian Zhang, Ji-Chun Wang, Jian Yang, Shan Lv, Lei Duan, Yan Lu, Li-Guang Tian, Mu-Xin Chen, Qin Liu, Fan-Na Wei, Xin-Yu Feng, Guo-Bing Yang, Yong-Jun Li, Yu Wang, Xiao-Jie Hu, Ming Yang, Zhen-Hui Lu, Shao-Yan Zhang, Shi-Zhu Li, and Jin-Xin Zheng

Subjects
Co-infection, Tuberculosis, HIV/AIDS, Epidemiology, Global burden of disease 2021, Infectious and parasitic diseases, RC109-216, Public aspects of medicine, RA1-1270
Abstract

Abstract Background The co-infection of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) and tuberculosis (TB) poses a significant clinical challenge and is a major global public health issue. This study aims to elucidate the disease burden of HIV-TB co-infection in global, regions and countries, providing critical information for policy decisions to curb the HIV-TB epidemic. Methods The ecological time-series study used data from the Global Burden of Disease (GBD) Study 2021. The data encompass the numbers of incidence, prevalence, mortality, and disability-adjusted life year (DALY), as well as age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and DALY rate for HIV-infected drug-susceptible tuberculosis (HIV-DS-TB), HIV-infected multidrug-resistant tuberculosis (HIV-MDR-TB), and HIV-infected extensively drug-resistant tuberculosis (HIV-XDR-TB) from 1990 to 2021. from 1990 to 2021. The estimated annual percentage change (EAPC) of rates, with 95% confidence intervals (CIs), was calculated. Results In 2021, the global ASIR for HIV-DS-TB was 11.59 per 100,000 population (95% UI: 0.37–13.05 per 100,000 population), 0.55 per 100,000 population (95% UI: 0.38–0.81 per 100,000 population), for HIV-MDR-TB, and 0.02 per 100,000 population (95% UI: 0.01–0.03 per 100,000 population) for HIV-XDR-TB. The EAPC for the ASIR of HIV-MDR-TB and HIV-XDR-TB from 1990 to 2021 were 4.71 (95% CI: 1.92–7.59) and 13.63 (95% CI: 9.44–18.01), respectively. The global ASMR for HIV-DS-TB was 2.22 per 100,000 population (95% UI: 1.73–2.74 per 100,000 population), 0.21 per 100,000 population (95% UI: 0.09–0.39 per 100,000 population) for HIV-MDR-TB, and 0.01 per 100,000 population (95% UI: 0.00–0.03 per 100,000 population) for HIV-XDR-TB in 2021. The EAPC for the ASMR of HIV-MDR-TB and HIV-XDR-TB from 1990 to 2021 were 4.78 (95% CI: 1.32–8.32) and 10.00 (95% CI: 6.09–14.05), respectively. Conclusions The findings indicate that enhancing diagnostic and treatment strategies, strengthening healthcare infrastructure, increasing access to quality medical care, and improving public health education are essential to combat HIV-TB co-infection. Graphical Abstract

Published
2024
Full Text
View/download PDF

9. Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection

Author

Jin, Xiao, Mu, Xin-Yue, and Xu, Jing

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

As the saying goes, "seeing is believing". However, with the development of digital face editing tools, we can no longer trust what we can see. Although face forgery detection has made promising progress, most current methods are designed manually by human experts, which is labor-consuming. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task. Second, we propose a novel performance estimation metric, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we connect the cells in a cascaded pyramid way for final forgery classification. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios.

Published
2023

14. Implantable Photonic Neural Probes with 3D-Printed Microfluidics and Applications to Uncaging

Author

Mu, Xin, Chen, Fu-Der, Dang, Ka My, Brunk, Michael G. K., Li, Jianfeng, Wahn, Hannes, Stalmashonak, Andrei, Ding, Peisheng, Luo, Xianshu, Chua, Hongyao, Lo, Guo-Qiang, Poon, Joyce K. S., and Sacher, Wesley D.

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Advances in chip-scale photonic-electronic integration are enabling a new generation of foundry-manufacturable implantable silicon neural probes incorporating nanophotonic waveguides and microelectrodes for optogenetic stimulation and electrophysiological recording in neuroscience research. Further extending neural probe functionalities with integrated microfluidics is a direct approach to achieve neurochemical injection and sampling capabilities. In this work, we use two-photon polymerization 3D printing to integrate microfluidic channels onto photonic neural probes, which include silicon nitride nanophotonic waveguides and grating emitters. The customizability of 3D printing enables a unique geometry of microfluidics that conforms to the shape of each neural probe, enabling integration of microfluidics with a variety of existing neural probes while avoiding the complexities of monolithic microfluidics integration. We demonstrate the photonic and fluidic functionalities of the neural probes via fluorescein injection in agarose gel and photoloysis of caged fluorescein in solution and in flxed brain tissue.

Published
2023
Full Text
View/download PDF

15. A Generative Approach for Script Event Prediction via Contrastive Fine-tuning

Author

Zhu, Fangqi, Gao, Jun, Yu, Changlong, Wang, Wei, Xu, Chen, Mu, Xin, Yang, Min, and Xu, Ruifeng

Subjects
Computer Science - Computation and Language
Abstract

Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge~(e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze~(MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at https://github.com/zhufq00/mcnc.

Published
2022

16. Data Provenance via Differential Auditing

Author

Mu, Xin, Pang, Ming, and Zhu, Feida

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Machine Learning
Abstract

Auditing Data Provenance (ADP), i.e., auditing if a certain piece of data has been used to train a machine learning model, is an important problem in data provenance. The feasibility of the task has been demonstrated by existing auditing techniques, e.g., shadow auditing methods, under certain conditions such as the availability of label information and the knowledge of training protocols for the target model. Unfortunately, both of these conditions are often unavailable in real applications. In this paper, we introduce Data Provenance via Differential Auditing (DPDA), a practical framework for auditing data provenance with a different approach based on statistically significant differentials, i.e., after carefully designed transformation, perturbed input data from the target model's training set would result in much more drastic changes in the output than those from the model's non-training set. This framework allows auditors to distinguish training data from non-training ones without the need of training any shadow models with the help of labeled output data. Furthermore, we propose two effective auditing function implementations, an additive one and a multiplicative one. We report evaluations on real-world data sets demonstrating the effectiveness of our proposed auditing technique.

Published
2022

22. Impact factors of Blastocystis hominis infection in persons living with human immunodeficiency virus: a large-scale, multi-center observational study from China

Author

Zhang, Shun-Xian, Wang, Ji-Chun, Li, Zhong-Wei, Zheng, Jin-Xin, Zhou, Wen-Ting, Yang, Guo-Bing, Yu, Ying-Fang, Wu, Xiu-Ping, Lv, Shan, Liu, Qin, Chen, Mu-Xin, Lu, Yan, Dou, Zhi-Hui, Zhang, Da-Wei, Lv, Wen-Wen, Wang, Lei, Lu, Zhen-Hui, Yang, Ming, Zheng, Pei-Yong, Chen, Yue-Lai, Tian, Li-Guang, and Zhou, Xiao-Nong

Published
2023
Full Text
View/download PDF

24. Dielectric and mechanical properties of Ti[O.sub.2]/polyimide composites with low dielectric constant

Author

Zhao, Guiqing, Mu, Xin, Ma, Dandan, Wang, Shuhong, Pan, Jiaqi, Cui, Jiwen, and Qi, Meili

Subjects
Titanium dioxide -- Mechanical properties -- Electric properties, Polyimides -- Mechanical properties -- Electric properties, Polymeric composites -- Mechanical properties -- Electric properties, Engineering and manufacturing industries, Science and technology
Abstract

With the advent of 5G era, the high millimeter-wave frequency band, poor signal penetration and large loss make 5G communication more dependent on low dielectric constant materials. Therefore, the research and development of low dielectric polymer materials has become a hot spot in recent years. With its good chemical corrosion resistance, thermal stability, high dispersion and high mobility, titanium dioxide (Ti[O.sub.2]) as a high function fine inorganic material is doped in polymer systems to improve the electrical properties of polymer composites. In recent years, a large number of studies have used spherical Ti[O.sub.2] nanoparticles doped on the polymer matrix, however, there is almost no research on doping nanolinear Ti[O.sub.2] nanoparticles on the polymer matrix. Here, Ti[O.sub.2] nanowires were prepared by hydrothermal method, and the prepared Ti[O.sub.2] nanowires were added to polyimide (PI). Ti[O.sub.2]/PI monolayer and three-layer composite films with good dielectric and mechanical properties were prepared by in-situ polymerization. Fourier transform infrared spectoscopy, scanning electron microscopy and X-ray diffraction were used to characterize the structure and test the properties of the composites. During the initiation process, the thermal initiation reaction of the composite membrane was completed. The composite film with good mechanical properties and excellent dielectric properties were obtained by adding titanium oxide nanowires, which can meet the requirements of its use in the field of microelectronic devices. KEYWORDS dielectric properties, hydrothermal method, polyimide, Ti[O.sub.2], 1 | INTRODUCTION Polyimide (PI) is a class of high-performance polymer material characterized by the imide ring structure, with excellent thermal stability, chemical corrosion resistance, mechanical properties, and good dielectric [...]

Published
2023
Full Text
View/download PDF

25. Numerical study of regional adaptability of regenerative indirect evaporative cooler

Author

XU Peng and MU Xin

Subjects
evaporative cooling, heat regenerative cycle, plate heat exchanger, relative humidity, latent heat, Materials of engineering and construction. Mechanics of materials, TA401-492, Environmental engineering, TA170-171
Abstract

In order to optimize the heat transfer characteristics of regenerative evaporative coolers under different climatic conditions and provide reference data for numerical simulation research. The dynamic coupling of heat and humidity parameters of the air flow in the regenerative evaporative cooler is the fundamental factor affecting the efficiency of evaporative heat transfer, and mastering its law can guide the structure optimization of the heat exchanger. In this paper, a simulation model is constructed based on the structure of regenerative evaporative cooler, and the variation of heat and humidity parameters in the airflow passage is studied by numerical simulation method with the application background of representative cities at home and abroad under different climate conditions. It is found that the relative humidity increases rapidly in the wet channel, and the length of 40 cm wet channel can meet the full evaporation demand under most working conditions. The evaporation cooling effect is greatly affected by the inlet air parameters, and the saturation degree of ambient air has a significant influence on the cooling effect. Keeping the latent heat exchange process in the high efficiency evaporation section and the sensible heat exchange process in the high temperature difference section of the channel can effectively improve the heat exchange efficiency of the evaporative cooler and shorten the length of the heat exchanger.

Published
2023
Full Text
View/download PDF

26. Impact factors of Blastocystis hominis infection in persons living with human immunodeficiency virus: a large-scale, multi-center observational study from China

Author

Shun-Xian Zhang, Ji-Chun Wang, Zhong-Wei Li, Jin-Xin Zheng, Wen-Ting Zhou, Guo-Bing Yang, Ying-Fang Yu, Xiu-Ping Wu, Shan Lv, Qin Liu, Mu-Xin Chen, Yan Lu, Zhi-Hui Dou, Da-Wei Zhang, Wen-Wen Lv, Lei Wang, Zhen-Hui Lu, Ming Yang, Pei-Yong Zheng, Yue-Lai Chen, Li-Guang Tian, and Xiao-Nong Zhou

Subjects
Blastocystis hominis, Human immunodeficiency virus, Viral load, Non-linear association, Restricted cubic spline, Propensity score, Infectious and parasitic diseases, RC109-216, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Blastocystis hominis (Bh) is zoonotic parasitic pathogen with a high prevalent globally, causing opportunistic infections and diarrhea disease. Human immunodeficiency virus (HIV) infection disrupts the immune system by depleting CD4+ T lymphocyte (CD4+ T) cell counts, thereby increasing Bh infection risk among persons living with HIV (PLWH). However, the precise association between Bh infection risk and HIV-related biological markers and treatment processes remains poorly understood. Hence, the purpose of the study was to explore the association between Bh infection risk and CD4+ T cell counts, HIV viral load (VL), and duration of interruption in antiviral therapy among PLWH. Methods A large-scale multi-center cross-sectional study was conducted in China from June 2020 to December 2022. The genetic presence of Bh in fecal samples was detected by real-time fluorescence quantitative polymerase chain reaction, the CD4+ T cell counts in venous blood was measured using flowcytometry, and the HIV VL in serum was quantified using fluorescence-based instruments. Restricted cubic spline (RCS) was applied to assess the non-linear association between Bh infection risk and CD4+ T cell counts, HIV VL, and duration of interruption in highly active antiretroviral therapy (HARRT). Results A total of 1245 PLWH were enrolled in the study, the average age of PLWH was 43 years [interquartile range (IQR): 33, 52], with 452 (36.3%) being female, 50.4% (n = 628) had no immunosuppression (CD4+ T cell counts > 500 cells/μl), and 78.1% (n = 972) achieved full virological suppression (HIV VL

Published
2023
Full Text
View/download PDF

29. Autism risk gene POGZ promotes chromatin accessibility and expression of clustered synaptic genes

Author

Markenscoff-Papadimitriou, Eirene, Binyameen, Fadya, Whalen, Sean, Price, James, Lim, Kenneth, Ypsilanti, Athena R, Catta-Preta, Rinaldo, Pai, Emily Ling-Lin, Mu, Xin, Xu, Duan, Pollard, Katherine S, Nord, Alex S, State, Matthew W, and Rubenstein, John L

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Autism, Brain Disorders, Human Genome, Intellectual and Developmental Disabilities (IDD), Mental Health, Biotechnology, Pediatric, Neurosciences, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Animals, Autistic Disorder, Binding Sites, Brain, Cell Cycle Proteins, Chromatin Assembly and Disassembly, DNA Transposable Elements, DNA-Binding Proteins, Enhancer Elements, Genetic, Euchromatin, Female, Gene Expression Regulation, Developmental, Genetic Predisposition to Disease, Homeodomain Proteins, Male, Mice, Inbred C57BL, Mice, Knockout, Nerve Tissue Proteins, Neurogenesis, Promoter Regions, Genetic, Synapses, Transposases, Mice, Medical Physiology, Biological sciences
Abstract

Deleterious genetic variants in POGZ, which encodes the chromatin regulator Pogo Transposable Element with ZNF Domain protein, are strongly associated with autism spectrum disorder (ASD). Although it is a high-confidence ASD risk gene, the neurodevelopmental functions of POGZ remain unclear. Here we reveal the genomic binding of POGZ in the developing forebrain at euchromatic loci and gene regulatory elements (REs). We profile chromatin accessibility and gene expression in Pogz-/- mice and show that POGZ promotes the active chromatin state and transcription of clustered synaptic genes. We further demonstrate that POGZ forms a nuclear complex and co-occupies loci with ADNP, another high-confidence ASD risk gene, and provide evidence that POGZ regulates other neurodevelopmental disorder risk genes as well. Our results reveal a neurodevelopmental function of an ASD risk gene and identify molecular targets that may elucidate its function in ASD.

Published
2021

33. Following up the afterglow: strategy for X-ray observation triggered by gravitational wave events

Author

Tong, Hui, Liu, Mu-Xin, Hu, Yi-Ming, Chan, Man Leong, Hendry, Martin, Liu, Zhu, and Sun, Hui

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The multi-messenger observation of compact binary coalescence promises great scientific treasure. However, a synthetic observation from both gravitational wave and electromagnetic channels remains challenging. Relying on the day-to-week long macronova emission, GW170817 remains the only event with successful electromagnetic followup. In this manuscript, we explore the possibility of using the early stage X-ray afterglow to search for the electromagnetic counterpart of gravitational wave events. Two algorithms, the sequential observation and the local optimization are considered and applied to three simulated events. We consider the proposed Einstein probe as a candidate X-ray telescope. Benefiting from the large field of view and high sensitivity, we find that the sequential observation algorithm not only is easy to implement, but also promises a good chance of actual detection.

Published
2020

39. Unsupervised User Identity Linkage via Factoid Embedding

Author

Xie, Wei, Mu, Xin, Lee, Roy Ka-Wei, Zhu, Feida, and Lim, Ee-Peng

Subjects
Computer Science - Social and Information Networks
Abstract

User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Most existing UIL solutions adopt a supervised or semi-supervised approach which generally suffer from scarcity of labeled data. In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach. It is designed to cope with different profile attributes, content types and network links of different OSNs. The key idea is that each piece of information about a user identity describes the real identity owner, and thus distinguishes the owner from other users. We represent such a piece of information by a factoid and model it as a triplet consisting of user identity, predicate, and an object or another user identity. By embedding these factoids, we learn the user identity latent representations and link two user identities from different OSNs if they are close to each other in the user embedding space. Our Factoid Embedding algorithm is designed such that as we learn the embedding space, each embedded factoid is "translated" into a motion in the user embedding space to bring similar user identities closer, and different user identities further apart. Extensive experiments are conducted to evaluate Factoid Embedding on two real-world OSNs data sets. The experiment results show that Factoid Embedding outperforms the state-of-the-art methods even without training data., Comment: Preprint for 2018 IEEE International Conference on Data Mining (ICDM)

Published
2019

42. Study on Self-Healing Growth and Friction Properties of Graphene on Nickel-Based Surface

Author

LV Tong-fa, CAO Yang, HE Yong-chun, LV Chao, LU Ji-chao, YU Shi-qi, WU Tao, MU Xin, QIANG Hao, YE Xiao-hui

Subjects
graphene, self-healing growth, friction performance, nickel substrate, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract

To solve the problem of insufficient durability of graphene film with nano-scale thickness in substrate protection， the self-healing process referring to the re-growth of graphene film after damage on a nickel-based surface was studied by heat treatment method. The characterization of microstructure and morphology showed that the graphene could grow again and repair the damaged surface under the conditions of a heat treatment temperature of 700 ℃and a holding time of 5 min. The results of friction test showed that the friction coefficient of the regrown graphene film was 0.05， which was 50%lower than that of the original nickel surface. Meantime， the mechanism of graphene regrowth was explored， and the driving force of graphene regrowth was described clearly. Generally， this research achievements provided experimental basis and theoretical explanation for graphene self-healing field.

Published
2023
Full Text
View/download PDF

43. Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models

Author

Jia Rui, Jin-Xin Zheng, Jin Chen, Hongjie Wei, Shanshan Yu, Zeyu Zhao, Xin-Yi Wang, Mu-Xin Chen, Shang Xia, Ying Zhou, Tianmu Chen, and Xiao-Nong Zhou

Subjects
SARS-CoV-2, Omicron variant, Biological invasive theory, Dynamic models, Optimal combination of interventions, Spatial spillover risk, Infectious and parasitic diseases, RC109-216, Public aspects of medicine, RA1-1270
Abstract

Abstract Background There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. Methods Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. Results Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81–303.84) and the actual number of the spillovers (0.94–74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. Conclusions Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus. Graphical Abstract

Published
2022
Full Text
View/download PDF

46. Hardcoded vulnerability detection approach for IoT device firmware

Author

Chao MU, Xin WANG, Ming YANG, Heng ZHANG, Zhenya CHEN, Xiaoming WU

Subjects
big data, iot security, hard coding, vulnerability detection, Electronic computers. Computer science, QA75.5-76.95
Abstract

With the popularization of IoT devices, more and more valuable data is generated.Analyzing and mining big data based on IoT devices has become a hot topic in the academic and industrial circles in recent years.However, due to the lack of necessary detection and protection methods, many IoT devices have serious information security risks.In particular, device hard-coded information is closely related to system encryption and decryption, identity authentication and other functions, which can provide confidentiality protection for core data.Once this information is exploited by malicious attackers, serious consequences such as sensitive information leakage, backdoor attacks, and unauthorized logins will occur.In response to this problem, a multi-type character recognition and positioning scheme was designed and a hard-coded vulnerability detection method in executable files was proposed based on the study of the characteristics of hard-coded vulnerabilities in IoT devices.The proposed method extracted the firmware of IoT devices and filtered all executable files as the source to be analyzed.Then, a solution to identify and locate three types of hard-coded characters was provided.Further, the reachability of the function, where the hard-coded character was located, was analyzed according to the function call relationship.Meanwhile, the instruction heterogeneity was mitigated by an intermediate representation (IR) model.The character and parameter hard-coded values was obtained through a data flow analysis approach.A symbolic execution method was devised to determine the trigger conditions of the hard-coded vulnerabilities, and then the vulnerability detection result was output.On the one hand, the proposed method introduced the method of symbolic execution based on the use of the intermediate representation model, which eliminated the dependency of instruction architecture and reduces the false positive rate of vulnerabilities; On the other hand, this method can integrate characters, files, and cryptographic implementation to realize the different characteristics of three types of hard-coded characters, which increased the coverage of vulnerability detection and improves the versatility of the detection method.The experimental results show that the proposed method can effectively detect three types of hard-coded vulnerabilities of characters, files and cryptographic implementation in various IoT devices, and has good detection accuracy, which can provide certain guidance for the deployment of subsequent security protection technologies.

Published
2022
Full Text
View/download PDF

47. Discovery of non-squalene triterpenes

Author

Tao, Hui, Lauterbach, Lukas, Bian, Guangkai, Chen, Rong, Hou, Anwei, Mori, Takahiro, Cheng, Shu, Hu, Ben, Lu, Li, Mu, Xin, Li, Min, Adachi, Naruhiko, Kawasaki, Masato, Moriya, Toshio, Senda, Toshiya, Wang, Xinghuan, Deng, Zixin, Abe, Ikuro, Dickschat, Jeroen S., and Liu, Tiangang

Published
2022
Full Text
View/download PDF

48. Sterically Unprotected Nucleophilic Boron Cluster Reagents

Author

Mu, Xin, Axtell, Jonathan C, Bernier, Nicholas A, Kirlikovali, Kent O, Jung, Dahee, Umanzor, Alexander, Qian, Kevin, Chen, Xiangyang, Bay, Katherine L, Kirollos, Monica, Rheingold, Arnold L, Houk, KN, and Spokoyny, Alexander M

Subjects
Inorganic Chemistry, Organic Chemistry, Chemical Sciences, Nucleophilic boron, alkyl bromides, alkyl pseudo halides, boron-heteroatom bonds, borylation, closo-hexaborate cluster, cluster deconstruction, main group electrophiles, stereoinversion, Macromolecular and Materials Chemistry, Chemical sciences
Abstract

A cornerstone of modern synthetic chemistry rests on the ability to manipulate the reactivity of a carbon center by rendering it either electrophilic or nucleophilic. However, accessing a similar reactivity spectrum with boron-based reagents has been significantly more challenging. While classical nucleophilic carbon-based reagents normally do not require steric protection, readily accessible, unprotected boron-based nucleophiles have not yet been realized. Herein, we demonstrate that the bench stable closo-hexaborate cluster anion can engage in a nucleophilic substitution reaction with a wide array of organic and main group electrophiles. The resulting molecules containing B‒C bonds can be further converted to tricoordinate boron species widely used in organic synthesis.

Published
2019

50. The correlation between pharmacological activity and contents of eight constituents of Glycyrrhiza uralensis Fisch

Author

Rui He, Ting-ting Ma, Mu-xin Gong, Kai-li Xie, Zhi-min Wang, and Jing Li

Subjects
Glycyrrhiza uralensis Fisch., Anti-inflammatory, Antioxidant, Immunoregulatory, Partial least squares regression, Quality evaluation, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Licorice (Glycyrrhiza uralensis Fisch. (GUF), Leguminosae) has been extensively applied in traditional Chinese medicine (TCM) to treat diseases, exactly, in almost half of Chinese herbal prescription. However, the relationship between chemical contents and efficacy has not been established, which could evaluate GUF quality. To create a simple and effective quality-evaluation method, 33 batches of GUF from different habitats in China were collected. The correlation between eight constituents (liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, glycyrrhizic acid, licochalcone A, glabridin and glycyrrhetinic acid) and pharmacological activities (anti-inflammatory, antioxidant and immunoregulatory) was analyzed per the partial least squares regression method. Results showed that eight constituents correlated significantly with the pharmacological activity. The correlation equation modes between pharmacological activity and contents of eight constituents were constructed and verified to be reliable. In GUF extract, the main constituents liquiritin, isoliquiritin and glycyrrhizic acid exhibited positive influence on anti-inflammatory and antioxidant effect with different potent, while the metabolites liquiritigenin and isoliquiritigenin exhibited positive effect on the immunoregulatory activity and glycyrrhetinic acid exhibited positive effect on all the tested activities. Thus, our chemical-efficacy correlation method is reliable and feasible to predict the pharmacological activity based on its eight constituents. It could be powerful in quality control of GUF and provides a useful way for quality evaluation of other medicinal herbs.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results