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1. Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV

Author

An, Haonan, Fang, Zhengru, Zhang, Yuang, Hu, Senkang, Chen, Xianhao, Xu, Guowen, and Fang, Yuguang

Subjects
Computer Science - Artificial Intelligence
Abstract

Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as blind spots and obstructions, CAVs employ vehicle-to-vehicle (V2V) communications to aggregate sensory data from surrounding vehicles. However, cooperative perception is often constrained by the limitations of achievable network throughput and channel quality. In this paper, we propose a channel-aware throughput maximization approach to facilitate CAV data fusion, leveraging a self-supervised autoencoder for adaptive data compression. We formulate the problem as a mixed integer programming (MIP) model, which we decompose into two sub-problems to derive optimal data rate and compression ratio solutions under given link conditions. An autoencoder is then trained to minimize bitrate with the determined compression ratio, and a fine-tuning strategy is employed to further reduce spectrum resource consumption. Experimental evaluation on the OpenCOOD platform demonstrates the effectiveness of our proposed algorithm, showing more than 20.19\% improvement in network throughput and a 9.38\% increase in average precision (AP@IoU) compared to state-of-the-art methods, with an optimal latency of 19.99 ms.

Published
2024

2. OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud

Author

Yuan, Shuai, Li, Hongwei, Qian, Xinyuan, Jiang, Wenbo, and Xu, Guowen

Subjects
Computer Science - Cryptography and Security
Abstract

The expansive storage capacity and robust computational power of cloud servers have led to the widespread outsourcing of machine learning inference services to the cloud. While this practice offers significant operational benefits, it also poses substantial privacy risks, including the exposure of proprietary models and sensitive user data. In this paper, we introduce OnePath, a framework designed for secure and efficient decision tree inference in cloud environments. Unlike existing schemes that require traversing all internal nodes of a decision tree, our protocol securely identifies and processes only the nodes on the prediction path, maintaining data privacy under ciphertext throughout the inference process. This selective traversal enhances both security and efficiency. To further optimize privacy and performance, OnePath employs lightweight cryptographic techniques, such as functional encryption, during the online phase of secure inference. Notably, our protocol allows both providers and clients to perform secure inference without the need to remain online continuously, a critical advantage for real-world applications. We substantiate the security of our framework with formal proofs, demonstrating that OnePath robustly protects the privacy of decision tree classifiers and user data. Experimental results highlight the efficiency of our approach, with our scheme processing query data in mere microseconds on the tested dataset. Through OnePath, we provide a practical solution that balances the needs for security and efficiency in cloud-based decision tree inference, making it a promising option for a variety of applications.

Published
2024

3. ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition

Author

Yuan, Shuai, Li, Hongwei, Han, Xingshuo, Xu, Guowen, Jiang, Wenbo, Ni, Tao, Zhao, Qingchuan, and Fang, Yuguang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Physical adversarial patches have emerged as a key adversarial attack to cause misclassification of traffic sign recognition (TSR) systems in the real world. However, existing adversarial patches have poor stealthiness and attack all vehicles indiscriminately once deployed. In this paper, we introduce an invisible and triggered physical adversarial patch (ITPatch) with a novel attack vector, i.e., fluorescent ink, to advance the state-of-the-art. It applies carefully designed fluorescent perturbations to a target sign, an attacker can later trigger a fluorescent effect using invisible ultraviolet light, causing the TSR system to misclassify the sign and potentially resulting in traffic accidents. We conducted a comprehensive evaluation to investigate the effectiveness of ITPatch, which shows a success rate of 98.31% in low-light conditions. Furthermore, our attack successfully bypasses five popular defenses and achieves a success rate of 96.72%.

Published
2024

4. Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks

Author

Cao, Hangcheng, Yuan, Longzhi, Xu, Guowen, He, Ziyang, Fang, Zhengru, and Fang, Yuguang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society
Abstract

Traffic sign recognition systems play a crucial role in assisting drivers to make informed decisions while driving. However, due to the heavy reliance on deep learning technologies, particularly for future connected and autonomous driving, these systems are susceptible to adversarial attacks that pose significant safety risks to both personal and public transportation. Notably, researchers recently identified a new attack vector to deceive sign recognition systems: projecting well-designed adversarial light patches onto traffic signs. In comparison with traditional adversarial stickers or graffiti, these emerging light patches exhibit heightened aggression due to their ease of implementation and outstanding stealthiness. To effectively counter this security threat, we propose a universal image inpainting mechanism, namely, SafeSign. It relies on attention-enabled multi-view image fusion to repair traffic signs contaminated by adversarial light patches, thereby ensuring the accurate sign recognition. Here, we initially explore the fundamental impact of malicious light patches on the local and global feature spaces of authentic traffic signs. Then, we design a binary mask-based U-Net image generation pipeline outputting diverse contaminated sign patterns, to provide our image inpainting model with needed training data. Following this, we develop an attention mechanism-enabled neural network to jointly utilize the complementary information from multi-view images to repair contaminated signs. Finally, extensive experiments are conducted to evaluate SafeSign's effectiveness in resisting potential light patch-based attacks, bringing an average accuracy improvement of 54.8% in three widely-used sign recognition models

Published
2024

5. General Geometry-aware Weakly Supervised 3D Object Detection

Author

Zhang, Guowen, Fan, Junsong, Chen, Liyi, Zhang, Zhaoxiang, Lei, Zhen, and Zhang, Lei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

3D object detection is an indispensable component for scene understanding. However, the annotation of large-scale 3D datasets requires significant human effort. To tackle this problem, many methods adopt weakly supervised 3D object detection that estimates 3D boxes by leveraging 2D boxes and scene/class-specific priors. However, these approaches generally depend on sophisticated manual priors, which is hard to generalize to novel categories and scenes. In this paper, we are motivated to propose a general approach, which can be easily adapted to new scenes and/or classes. A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. In specific, we propose three general components: prior injection module to obtain general object geometric priors from LLM model, 2D space projection constraint to minimize the discrepancy between the boundaries of projected 3D boxes and their corresponding 2D boxes on the image plane, and 3D space geometry constraint to build a Point-to-Box alignment loss to further refine the pose of estimated 3D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation. The source code is available at https://github.com/gwenzhang/GGA., Comment: Accepted to ECCV24

Published
2024

6. Backdoor Attacks against Image-to-Image Networks

Author

Jiang, Wenbo, Li, Hongwei, He, Jiaming, Zhang, Rui, Xu, Guowen, Zhang, Tianwei, and Lu, Rongxing

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkable performance, the backdoor vulnerability of I2I networks has not been explored. To fill this research gap, we conduct a comprehensive investigation on the susceptibility of I2I networks to backdoor attacks. Specifically, we propose a novel backdoor attack technique, where the compromised I2I network behaves normally on clean input images, yet outputs a predefined image of the adversary for malicious input images containing the trigger. To achieve this I2I backdoor attack, we propose a targeted universal adversarial perturbation (UAP) generation algorithm for I2I networks, where the generated UAP is used as the backdoor trigger. Additionally, in the backdoor training process that contains the main task and the backdoor task, multi-task learning (MTL) with dynamic weighting methods is employed to accelerate convergence rates. In addition to attacking I2I tasks, we extend our I2I backdoor to attack downstream tasks, including image classification and object detection. Extensive experiments demonstrate the effectiveness of the I2I backdoor on state-of-the-art I2I network architectures, as well as the robustness against different mainstream backdoor defenses.

Published
2024

7. Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection

Author

Zhang, Guowen, Fan, Lue, He, Chenhang, Lei, Zhen, Zhang, Zhaoxiang, and Zhang, Lei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. The linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency., Comment: 10 pages, 4 figures

Published
2024

8. DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering

Author

Hei, Zijian, Liu, Weiling, Ou, Wenjie, Qiao, Juyi, Jiao, Junming, Song, Guowen, Tian, Ting, and Lin, Yi

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

Retrieval-Augmented Generation (RAG) has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA). RAG expands the query context by incorporating external knowledge bases to enhance the response accuracy. However, it would be inefficient to access LLMs multiple times for each query and unreliable to retrieve all the relevant documents by a single query. We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. Additionally, a compact classifier is applied to two different selection strategies to determine the contribution of the retrieved documents to answering the query and retrieve the relatively relevant documents. Meanwhile, DR-RAG call the LLMs only once, which significantly improves the efficiency of the experiment. The experimental results on multi-hop QA datasets show that DR-RAG can significantly improve the accuracy of the answers and achieve new progress in QA systems.

Published
2024

9. Thermalization dynamics in photonic lattices of different geometries

Author

Yang, Guowen, Bongiovanni, Domenico, Song, Daohong, Morandotti, Roberto, Chen, Zhigang, and Efremidis, Nikolaos K.

Subjects
Physics - Optics
Abstract

The statistical mechanical behavior of weakly nonlinear multimoded optical settings is attracting increased interest during the last few years. The main purpose of this work is to numerically investigate the main factors that affect the thermalization process in photonic lattices. In particular, we find that lattices with identically selected properties (such as temperature, coupling coefficient, lattice size, and excitation conditions) can exhibit very different thermalization dynamics and thus thermalization distances. Our investigation is focused on two different two-dimensional lattices: the honeycomb lattice and the triangular lattice. Our numerical results show that, independently of the excitation conditions, the honeycomb lattice always thermalizes faster than the triangular lattice. We mainly explain this behavior to the quasilinear spectrum that promotes wave-mixing in the honeycomb lattice in comparison to the power-like spectrum of the triangular lattice. In addition, we investigate the combined effects of temperature as well as the sign and magnitude of the nonlinearity. Switching either the sign of the Kerr nonlinear coefficient or the sign of the temperature can lead to significant differences in the thermalization dynamics, a phenomenon that can be physically explained in terms of wave instabilities. Larger absolute values of the temperature |T| result in more uniform distributions for the power occupation numbers and faster thermalization speeds. Finally, as expected, increasing the magnitude of the nonlinearity results in accelerated thermalization. Our findings provide valuable insights into optical thermalization in discrete systems where experimental realization may bring about new possibilities for light manipulation and applications., Comment: 11 pages, 4 figures

Published
2024

10. OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework

Author

Zhou, Wei, Huang, Hong, Zhang, Guowen, Shi, Ruize, Yin, Kehan, Lin, Yuanyuan, and Liu, Bang

Subjects
Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques.

Published
2024

11. CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning

Author

Wu, Haolin, Chen, Jing, Du, Ruiying, Wu, Cong, He, Kun, Shang, Xingcan, Ren, Hao, and Xu, Guowen

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The increasing prevalence of audio deepfakes poses significant security threats, necessitating robust detection methods. While existing detection systems exhibit promise, their robustness against malicious audio manipulations remains underexplored. To bridge the gap, we undertake the first comprehensive study of the susceptibility of the most widely adopted audio deepfake detectors to manipulation attacks. Surprisingly, even manipulations like volume control can significantly bypass detection without affecting human perception. To address this, we propose CLAD (Contrastive Learning-based Audio deepfake Detector) to enhance the robustness against manipulation attacks. The key idea is to incorporate contrastive learning to minimize the variations introduced by manipulations, therefore enhancing detection robustness. Additionally, we incorporate a length loss, aiming to improve the detection accuracy by clustering real audios more closely in the feature space. We comprehensively evaluated the most widely adopted audio deepfake detection models and our proposed CLAD against various manipulation attacks. The detection models exhibited vulnerabilities, with FAR rising to 36.69%, 31.23%, and 51.28% under volume control, fading, and noise injection, respectively. CLAD enhanced robustness, reducing the FAR to 0.81% under noise injection and consistently maintaining an FAR below 1.63% across all tests. Our source code and documentation are available in the artifact repository (https://github.com/CLAD23/CLAD)., Comment: Submitted to IEEE TDSC

Published
2024

12. Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks

Author

Cao, Hangcheng, Huang, Wenbin, Xu, Guowen, Chen, Xianhao, He, Ziyang, Hu, Jingyang, Jiang, Hongbo, and Fang, Yuguang

Subjects
Computer Science - Cryptography and Security
Abstract

Deep learning technologies are pivotal in enhancing the performance of WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, and regrettably, there is lacking serious attention to this security issue within the WiFi sensing community. In this paper, we elaborate such an attack, called WiIntruder, distinguishing itself with universality, robustness, and stealthiness, which serves as a catalyst to assess the security of existing WiFi-based sensing systems. This attack encompasses the following salient features: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, leading to a universally effective perturbation attack applicable to common applications; (2) Addressing perturbation signal distortion caused by device synchronization and wireless propagation when critical parameters are optimized through a heuristic particle swarm-driven perturbation generation algorithm; and (3) Enhancing attack pattern diversity and stealthiness through random switching of perturbation surrogates generated by a generative adversarial network. Extensive experimental results confirm the practical threats of perturbation attacks to common WiFi-based services, including user authentication and respiratory monitoring.

Published
2024

13. Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping

Author

Zhang, Lei, Bai, Kaixin, Huang, Guowen, Bing, Zhenshan, Chen, Zhaopeng, Knoll, Alois, and Zhang, Jianwei

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

The deep learning models has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping. However, the contact information-guided grasping in cluttered environments remains largely underexplored. To address this gap, we have developed a method for generating multi-fingered hand grasp samples in cluttered settings through contact semantic map. We introduce a contact semantic conditional variational autoencoder network (CoSe-CVAE) for creating comprehensive contact semantic map from object point cloud. We utilize grasp detection method to estimate hand grasp poses from the contact semantic map. Finally, an unified grasp evaluation model is designed to assess grasp quality and collision probability, substantially improving the reliability of identifying optimal grasps in cluttered scenarios. Our grasp generation method has demonstrated remarkable success, outperforming state-of-the-art methods by at least 4.65% with 81.0% average grasping success rate in real-world single-object environment and 75.3% grasping success rate in cluttered scenes. We also proposed the multi-modal multi-fingered grasping dataset generation method. Our multi-fingered hand grasping dataset outperforms previous datasets in scene diversity, modality diversity. The dataset, code and supplementary materials can be found at https://sites.google.com/view/ffh-cluttered-grasping., Comment: 8 pages

Published
2024

21. SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism

Author

Zhang, Yuang, An, Haonan, Fang, Zhengru, Xu, Guowen, Zhou, Yuan, Chen, Xianhao, and Fang, Yuguang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, autonomous driving has garnered significant attention due to its potential for improving road safety through collaborative perception among connected and autonomous vehicles (CAVs). However, time-varying channel variations in vehicular transmission environments demand dynamic allocation of communication resources. Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception. In this paper, we introduce SmartCooper, an adaptive collaborative perception framework that incorporates communication optimization and a judger mechanism to facilitate CAV data fusion. Our approach begins with optimizing the connectivity of vehicles while considering communication constraints. We then train a learnable encoder to dynamically adjust the compression ratio based on the channel state information (CSI). Subsequently, we devise a judger mechanism to filter the detrimental image data reconstructed by adaptive decoders. We evaluate the effectiveness of our proposed algorithm on the OpenCOOD platform. Our results demonstrate a substantial reduction in communication costs by 23.10\% compared to the non-judger scheme. Additionally, we achieve a significant improvement on the average precision of Intersection over Union (AP@IoU) by 7.15\% compared with state-of-the-art schemes.

Published
2024

33. Dietary supplementation with citrus peel extract in transition period improves rumen microbial composition and ameliorates energy metabolism and lactation performance of dairy cows

Author

Lingxue Ju, Qi Shao, Zhiyuan Fang, Erminio Trevisi, Meng Chen, Yuxiang Song, Wenwen Gao, Lin Lei, Xinwei Li, Guowen Liu, and Xiliang Du

Subjects
Adipose tissue, Citrus peel extract, Energy metabolism, Liver, Rumen microbiota, Animal culture, SF1-1100, Veterinary medicine, SF600-1100
Abstract

Abstract Background During the transition period, excessive negative energy balance (NEB) lead to metabolic disorders and reduced milk yield. Rumen microbes are responsible for resolving plant material and producing volatile fatty acids (VFA), which are the primary energy source for cows. In this study, we aimed to investigate the effect of citrus peel extract (CPE) supplementation on rumen microbiota composition, energy metabolism and milk performance of peripartum dairy cows. Methods Dairy cows were fed either a basal diet (CON group) or the same basal diet supplemented with CPE via intragastric administration (4 g/d, CPE group) for 6 weeks (3 weeks before and 3 weeks after calving; n = 15 per group). Samples of serum, milk, rumen fluid, adipose tissue, and liver were collected to assess the effects of CPE on rumen microbiota composition, rumen fermentation parameters, milk performance, and energy metabolic status of dairy cows. Results CPE supplementation led to an increase in milk yield, milk protein and lactose contents, and serum glucose levels, while reduced serum concentrations of non-esterified fatty acid, β-hydroxybutyric acid, insulin, aspartate aminotransferase, alanine aminotransferase, and haptoglobin during the first month of lactation. CPE supplementation also increased the content of ruminal VFA. Compared to the CON group, the abundance of Prevotellaceae, Methanobacteriaceae, Bacteroidales_RF16_group, and Selenomonadaceae was found increased, while the abundance of Oscillospiraceae, F082, Ruminococcaceae, Christensenellaceae, Muribaculaceae UCG-011, Saccharimonadaceae, Hungateiclostridiaceae, and Spirochaetaceae in the CPE group was found decreased. In adipose tissue, CPE supplementation decreased lipolysis, and inflammatory response, while increased insulin sensitivity. In the liver, CPE supplementation decreased lipid accumulation, increased insulin sensitivity, and upregulated expression of genes involved in gluconeogenesis. Conclusions Our findings suggest that CPE supplementation during the peripartum period altered rumen microbiota composition and increased ruminal VFA contents, which further improved NEB and lactation performance, alleviated lipolysis and inflammatory response in adipose tissue, reduced lipid accumulation and promoted gluconeogenesis in liver. Thus, CPE might contribute to improve energy metabolism and consequently lactation performance of dairy cows during the transition period.

Published
2024
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34. MYO6 contributes to tumor progression and enzalutamide resistance in castration-resistant prostate cancer by activating the focal adhesion signaling pathway

Author

Shengfeng Zheng, Zhe Hong, Yao Tan, Yue Wang, Junhong Li, Zihao Zhang, Tao Feng, Zongyuan Hong, Guowen Lin, and Dingwei Ye

Subjects
Myosin VI, Enzalutamide resistance, Androgen receptor, Castration-resistant prostate cancer, Y15, Medicine, Cytology, QH573-671
Abstract

Abstract Background Enzalutamide (Enz) resistance is a poor prognostic factor for patients with castration-resistant prostate cancer (CRPC), which often involves aberrant expression of the androgen receptor (AR). Myosin VI (MYO6), one member of the myosin family, plays an important role in regulating cell survival and is highly expressed in prostate cancer (PCa). However, whether MYO6 is involved in Enz resistance in CRPC and its mechanism remain unclear. Methods Multiple open-access databases were utilized to examine the relationship between MYO6 expression and PCa progression, and to screen differentially expressed genes (DEGs) and potential signaling pathways associated with the MYO6-regulated Enz resistance. Both in vitro and in vivo tumorigenesis assays were employed to examine the impact of MYO6 on the growth and Enz resistance of PCa cells. Human PCa tissues and related clinical biochemical data were utilized to identify the role of MYO6 in promoting PCa progression and Enz resistance. The molecular mechanisms underlying the regulation of gene expression, PCa progression, and Enz resistance in CRPC by MYO6 were investigated. Results MYO6 expression increases in patients with PCa and is positively correlated with AR expression in PCa cell lines and tissues. Overexpression of AR increases MYO6 expression to promote PCa cell proliferation, migration and invasion, and to inhibit PCa cell apoptosis; whereas knockdown of MYO6 expression reverses these outcomes and enhances Enz function in suppressing the proliferation of the Enz- sensitive and resistant PCa cells both in vitro and in vivo. Mechanistically, AR binds directly to the promoter region (residues − 503 to − 283 base pairs) of MYO6 gene and promotes its transcription. Furthermore, MYO6 activates focal adhesion kinase (FAK) phosphorylation at tyrosine-397 through integrin beta 8 (ITGB8) modulation to promote PCa progression and Enz resistance. Notably, inhibition of FAK activity by Y15, an inhibitor of FAK, can resensitize CRPC cells to Enz treatment in cell lines and mouse xenograft models. Conclusions MYO6 has pro-tumor and Enz-resistant effects in CRPC, suggesting that targeting MYO6 may be beneficial for ENZ-resistant CRPC therapy through the AR/MYO6/FAK signaling pathway.

Published
2024
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35. Therapeutic potential of compound extract from Dracocephalum Rupestre Hance and Berberidis Radix against Salmonella-induced lamb diarrhea

Author

Dan Ban, Zhuoga GeSang, Yueyuan Fan, Guowen Fu, Hongjiao Yang, and Langyuzhen Si

Subjects
Lamb diarrhea, Dracocephalum Rupestre Hance, Berberidis Radix, Gut microbiota, Short-chain fatty acids, Antibiotic alternatives, Medicine, Science
Abstract

Abstract Lamb diarrhea is primarily induced by bacterial infections, causing great economic and health challenges. Traditional antibiotic treatments raise concerns over drug resistance and environmental contamination. We explored the therapeutic potential of a compound extract from Dracocephalum rupestre Hance and Berberidis Radix against Salmonella-induced diarrhea in lamb. Twenty-five five-week-old Kunming mice (20 ± 5 g) were used. A controlled laboratory experiment, combing histological examinations, serum cytokine level analysis, gut microbiota composition analysis, and short-chain fatty acid quantification were conducted. Results demonstrated significant reparative effects on intestinal mucosal damage of the compound. Compound treatment notably reduced serum levels of inflammatory cytokines (IL-6, IL-8, sigA, and TNF-α), indicating an anti-inflammatory effect. Gene expression analysis of mucosal repair markers (PCNA, TGF, and EGFR) confirmed the positive impacts on intestinal recovery processes after treatment. Microbiota analysis revealed concentration-dependent alterations in gut microbial composition, with a notable increase in beneficial bacterial genera such as Muribaculum and Prevotella, suggesting the role of the compound in promoting gut health. Additionally, short-chain fatty acid analysis indicated an increase in beneficial acids, which are critical for the gut and overall health. This investigation highlights the potential therapeutic benefits of Dracocephalum rupestre Hance combining Berberidis Radix in lamb with Salmonella-induced diarrhea.

Published
2024
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36. Expression of hepatic genes involved in bile acid metabolism in dairy cows with fatty liver

Author

Xiliang Du, Mingchao Liu, Erminio Trevisi, Lingxue Ju, Yuting Yang, Wenwen Gao, Yuxiang Song, Lin Lei, Majigsuren Zolzaya, Xinwei Li, Zhiyuan Fang, and Guowen Liu

Subjects
bile acid, CYP7A1, FXR, hepatocyte, Dairy processing. Dairy products, SF250.5-275, Dairying, SF221-250
Abstract

ABSTRACT: Bile acids are cholesterol-derived molecules that are primarily produced in the liver. In nonruminants with fatty liver, overproduction of bile acids is associated with liver injury. During the transition period, fatty liver is a metabolic disorder that can affect up to 50% of high-producing dairy cows. The purpose of this study was to provide a comprehensive evaluation of hepatic bile acid metabolism in dairy cows with fatty liver by assessing the expression changes of genes involved in bile acid synthesis, export, and uptake. The serum activities of aspartate aminotransferase, alanine aminotransferase, and glutamate dehydrogenase and the concentration of total bile acids were all greater, whereas the serum concentration of total cholesterol was lower in cows with fatty liver than in healthy cows. The content of total bile acids was higher, but total cholesterol was slightly lower in liver tissues from fatty liver cows than from healthy cows. The hepatic mRNA abundance of cholesterol 7a-hydroxylase (CYP7A1); hydroxy-delta-5-steroid dehydrogenase, 3 β- and steroid delta-isomerase 7 (HSD3B7); and sterol 12α-hydroxylase (CYP8B1), enzymes involved in the classic pathway of bile acid synthesis, was higher in fatty liver cows than in healthy cows. Compared with healthy cows, the hepatic mRNA abundance of alternative bile acid synthesis pathway-related genes sterol 27-hydroxylase (CYP27A1) and oxysterol 7α-hydroxylase (CYP7B1) did not differ in cows with fatty liver. The protein and mRNA abundances of bile acid transporter bile salt efflux pump (BSEP) were lower in the liver of dairy cow with fatty liver. Compared with healthy cows, the hepatic mRNA abundance of bile acid transporters solute carrier family 51 subunit α (SLC51A) and ATP binding cassette subfamily C member 1 (ABCC1) and 3 (ABCC3) was greater in cows with fatty liver, whereas the solute carrier family 51 subunit β (SLC51B) did not differ. The expression of genes involved in bile acid uptake, including solute carrier family 10 member 1 (NTCP), solute carrier organic anion transporter family member 1A2 (SLCO1A2) and 2B1 (SLCO2B1) was upregulated in dairy cows with fatty liver. Furthermore, the hepatic protein and mRNA abundance of bile acid metabolism regulators farnesoid X receptor (FXR) and small heterodimer partner (SHP) were lower in cows with fatty liver than in healthy cows. Overall, these data suggest that inhibition of the FXR signaling pathway may lead to increased bile acid synthesis and uptake and decreased secretion of bile acids from hepatocytes to the bile, which elevates hepatic bile acid content in dairy cows with fatty liver. Because the hepatotoxicity of bile acids has been demonstrated on nonruminant hepatocytes, it is likely that liver injury is induced by increased hepatic bile acid content in dairy cows with fatty liver.

Published
2024
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37. Targeting PHB2-mediated mitophagy alleviates nonesterified fatty acid-induced mitochondrial dysfunction in bovine mammary epithelial cells

Author

Guojin Li, Liguang Cao, Kai Liu, Yifei Dong, Zifeng Yang, Jianchun Luo, Wenrui Gao, Lin Lei, Yuxiang Song, Xiliang Du, Xinwei Li, Wenwen Gao, and Guowen Liu

Subjects
clinical ketosis, prohibitin 2, mitophagy, mitochondrial dysfunction, Dairy processing. Dairy products, SF250.5-275, Dairying, SF221-250
Abstract

ABSTRACT: Mitochondrial dysfunction has been reported to occur in the mammary gland of dairy cows suffering from ketosis. Prohibitin 2 (PHB2) plays a crucial role in regulating mitophagy, which clears impaired mitochondria to maintain normal mitochondrial function. Therefore, the current study aimed to investigate how PHB2 mediates mitophagy, thereby influencing mitochondrial function in the immortalized bovine mammary epithelial cell line (MAC-T cells). First, mammary gland tissue and blood samples were collected from healthy cows (n = 15, BHB 3.0 mM). Compared with healthy cows, cows with clinical ketosis exhibited lower DMI, milk production, milk protein, milk lactose, and serum glucose. In contrast, milk fat, serum nonesterified fatty acids (NEFA) and BHB were greater in cows with clinical ketosis. The protein abundance of PHB2, peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α), mitofusin 2 (MFN2) in whole cell lysates (WCL), as well as PHB2, sequestosome-1 (SQSTM1, also called p62), microtubule-associated protein 1 light chain 3-II (MAP1LC3-II, also called LC3-II), and ubiquitinated proteins in mitochondrial fraction were significantly lower in cows with clinical ketosis. The ATP content of mammary gland tissue in cows with clinical ketosis was lower than that of healthy cows. Second, MAC-T were cultured and treated with NEFA (0, 0.3, 0.6, 1.2 mM). The MAC-T treated with 1.2 mM NEFA displayed decreased protein abundance of PHB2, PGC-1α, and MFN2 in WCL, as well as protein abundance of PHB2, p62, LC3-II, and ubiquitinated proteins in mitochondrial fraction. The content of ATP and JC-1 aggregates in 1.2 mM NEFA group were lower than in the 0 mM NEFA group. Additionally, 1.2 mM NEFA disrupted the fusion between mitochondria and lysosomes. The MAC-T were then pretreated with 100 nM rapamycin, followed by treatment with or without NEFA. Rapamycin alleviated impaired mitophagy and mitochondria dysfunction induced by 1.2 mM NEFA. Third, MAC-T were transfected with small interfering RNA to silence PHB2 or a plasmid for overexpression of PHB2, followed by treatment with or without NEFA. The silencing of PHB2 aggravated 1.2 mM NEFA-induced impaired mitophagy and mitochondrial dysfunction, whereas the overexpression of PHB2 alleviated these effects. Overall, this study provides evidence that PHB2, in regulation of mitophagy, is a mechanism for bovine mammary epithelial cells to counteract NEFA-induced mitochondrial dysfunction.

Published
2024
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38. Flexible, thermal processable, self-healing, and fully bio-based starch plastics by constructing dynamic imine network

Author

Xiaoqian Zhang, Haishan Zhang, Guowen Zhou, Zhiping Su, and Xiaohui Wang

Subjects
Bioplastic, Covalent adaptable networks, Schiff base chemistry, Thermal processability, Self-healing, Renewable energy sources, TJ807-830, Ecology, QH540-549.5
Abstract

The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources. Starch is desirable for fabricating bioplastic due to its abundance and renewable nature. However, limitations such as brittleness, hydrophilicity, and thermal properties restrict its widespread application. To overcome these issues, covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions. This strategy endowed starch plastic with excellent thermal processability, as evidenced by a low glass transition temperature (Tg = 20.15 °C). Through introducing Priamine with long carbon chains, the starch plastic demonstrated superior flexibility (elongation at break = 45.2%) and waterproof capability (water contact angle = 109.2°). Besides, it possessed a good thermal stability and self-adaptability, as well as solvent resistance and chemical degradability. This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.

Published
2024
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39. Optimizing Service Efficacy in 5G HetNets: An Adaptive Acceleration PSO Approach

Author

Guowen Li, Wenbo Hu, Yang Zhao, and Xudong Lu

Subjects
service efficacy, 5g hetnets, macro base station (mbs), femto base station (fbs), particle swarm optimization (pso), Technology, Engineering (General). Civil engineering (General), TA1-2040
Abstract

The dense deployment of Femto Base Stations (FBS) assisting Macro Base Stations (MBS) in a Heterogeneous Network (HetNet) resolves the coverage issue of 5G signal transmission. However, the imprudent layout of FBSs results in extensive energy consumption and increased signal interference among base stations. Regulating the transmission power of each base station in the HetNets through the main controller or MBS is essential to maximize the power efficiency of the entire HetNets while adhering to the constraints of basic signal throughput and fairness. To address this challenge, this paper proposes an Adaptive Acceleration Particle Swarm Optimization (AA-PSO) algorithm. This algorithm dynamically determines the inertia weight based on each particle’s optimal position and the global optimal position, and introduces the concept of time-varying parameters to control the learning rate, thus managing the search range and convergence speed of the particle swarm. The results demonstrate that the AA-PSO algorithm can efficiently determine the optimal transmission power of each base station in the HetNets, reduce interference between MBS and FBSs, as well as among FBSs, and ultimately improve the service efficacy of the entire network.

Published
2024
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40. Differential proteins from EVs identification based on tandem mass tags analysis and effect of Treg-derived EVs on T-lymphocytes in COPD patients

Author

Xuefang Tao, Zhisong Xu, Hai Tian, Jingfeng He, Guowen Wang, and Xuexia Tao

Subjects
Chronic obstructive pulmonary disease, T-lymphocyte subsets, Tregs, EVs, Tandem mass tags analysis, Proteomics analysis, Diseases of the respiratory system, RC705-779
Abstract

Abstract Background Chronic obstructive pulmonary disease (COPD) is a widespread respiratory disease. This study examines extracellular vesicles (EVs) and proteins contained in EVs in COPD. Methods Blood samples were collected from 40 COPD patients and 10 health controls. Cytokines including IFN-γ, TNF-α, IL-1β, IL-6, IL-8, and IL-17, were measured by ELISA. Small EVs samples were extracted from plasma and identified by transmission electron microscope (TEM), nanoparticle tracking analysis (NTA), and Western blot. Protein components contained in EVs were analyzed by Tandem Mass Tags (TMT) to identify differential proteins. Treg-derived EV was extracted and added to isolated CD8+, Treg, and Th17 subsets to assess its effect on T-lymphocytes. Results ELISA revealed higher levels of all cytokines and flow cytometry suggested a higher proportion of Treg and Th17 cells in COPD patients. After identification, TMT analysis identified 207 unique protein components, including five potential COPD biomarkers: BTRC, TRIM28, CD209, NCOA3, and SSR3. Flow cytometry revealed that Treg-derived EVs inhibited differentiation into CD8+, CD4+, and Th17 cells. Conclusion The study shows that cytokines, T-lymphocyte subsets differences in COPD and Treg-derived EVs influence T-lymphocyte differentiation. Identified biomarkers may assist in understanding COPD pathogenesis, prognosis, and therapy. The study contributes to COPD biomarker research.

Published
2024
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41. Associations between different insulin resistance indices and the risk of all-cause mortality in peritoneal dialysis patients

Author

Guowen Zhao, Sijia Shang, Na Tian, Xiaojiang Zhan, Fenfen Peng, Xiaoyang Wang, Yueqiang Wen, Qingdong Xu, Xiaoran Feng, Xingming Tang, Xianfeng Wu, Qian Zhou, Yuanyuan Yang, Xing Zhang, and Ning Su

Subjects
Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background Insulin resistance (IR) is prevalent in individuals undergoing peritoneal dialysis (PD) and is related to increased susceptibility to coronary artery disease and initial peritonitis. In recent investigations, correlations have been found between indices of IR and the incidence of all-cause mortality in various populations. However, such correlations have not been detected among individuals undergoing PD. Hence, the present study’s aim was to explore the connections between IR indices and the incidence of all-cause mortality in PD patients. Methods Peritoneal dialysis patients (n = 1736) were recruited from multiple PD centres between January 2010 and December 2021. Cox proportional hazards and restricted cubic spline regression models were used to evaluate the connections between the triglyceride–glucose (TyG) index, triglyceride–glucose/body mass index (TyG–BMI), and triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and the occurrence of all-cause mortality. All three IR indices were integrated into the same model to assess the predictive stability. Furthermore, a forest plot was employed to display the findings of the subgroup analysis of PD patients. Results Overall, 378 mortality events were recorded during a median follow-up time of 2098 days. Among PD patients, a higher TyG index, TyG–BMI, and TG/HDL-C ratio were identified as independent risk factors for all-cause mortality according to Cox proportional hazards analyses (hazard ratio (HR) 1.588, 95% confidence interval (CI) 1.261–2.000; HR 1.428, 95% CI 1.067–1.910; HR 1.431, 95% CI 1.105–1.853, respectively). In a model integrating the three IR indices, the TyG index showed the highest predictive stability. According to the forest plot for the TyG index, no significant interactions were observed among the subgroups. Conclusion Significant associations were found between the TyG index, TyG–BMI, and TG/HDL-C ratio and the incidence of all-cause mortality among PD patients. The TyG index may be the most stable of the three surrogate IR markers. Finally, a correlation was identified between IR and the risk of all-cause mortality in patients undergoing PD.

Published
2024
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42. Genome-wide investigation of the PIF gene family in alfalfa (Medicago sativa L.) expression profiles during development and stress

Author

Qianning Liu, Baiji Wang, Wen Xu, Yuying Yuan, Jinqiu Yu, and Guowen Cui

Subjects
Medicago sativa, PIF transcription factors, Genome-wide, Abiotic stress, Genetics, QH426-470
Abstract

Abstract Background Phytochrome-interacting factors (PIFs) plays an important role in plants as hubs for intracellular signaling regulation. The PIF gene family has been identified and characterized in many plants, but alfalfa (Medicago sativa L.), an important perennial high-quality legume forage, has not been reported on the PIF gene family. Results In this study, we presented the identification and characterization of five MsPIF genes in alfalfa (Medicago sativa L.). Phylogenetic analysis indicated that PIFs from alfalfa and other four plant species could be divided into three groups supported by similar motif analysis. The collinearity analysis of the MsPIF gene family showed the presence of two gene pairs, and the collinearity analysis with AtPIFs showed three gene pairs, indicating that the evolutionary process of this family is relatively conservative. Analysis of cis-acting elements in promoter regions of MsPIF genes indicated that various elements were related to light, abiotic stress, and plant hormone responsiveness. Gene expression analyses demonstrated that MsPIFs were primarily expressed in the leaves and were induced by various abiotic stresses. Conclusion This study conducted genome-wide identification, evolution, synteny analysis, and expression analysis of the PIFs in alfalfa. Our study lays a foundation for the study of the biological functions of the PIF gene family and provides a useful reference for improving abiotic stress resistance in alfalfa.

Published
2024
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43. Clinical characteristics and risk factors for bacterial co-infections in COVID-19 patients: A retrospective study

Author

Yichuan Gan, Guowen Zhang, Hengbiao Sun, and Xiaoming Lyu

Subjects
COVID-19, Clinical characteristics, Risk factor, Bacterial infection, Multidrug-resistant, Microbiology, QR1-502
Abstract

Objective: This study aimed to analyse the bacterial composition, distribution, drug sensitivity, and clinical characteristics of patients with coronavirus disease 2019 (COVID-19) who develop bacterial co-infections. Methods: We conducted a retrospective study of 184 patients with COVID-19 admitted between December 2022 and January 2023. Data on gender, age, length of hospital stay, pneumonia classification, underlying diseases, invasive surgery, hormone therapy, inflammation indicators, and other relevant information were collected. Samples of sputum, bronchoscopy sputum, alveolar lavage fluid, middle urine, puncture fluid, wound secretions, and blood were collected for pathogen isolation, identification, and drug sensitivity testing. Results: The majority of patients with COVID-19 with bacterial co-infection were elderly and had underlying diseases. Invasive surgery and hormone therapy were identified as risk factors for co-infections. Laboratory analysis showed reduced lymphocyte counts and elevated levels of C-reactive protein and procalcitonin. The most common pathogens in co-infections were Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. The detection rate of drug-resistant strains, including methicillin-resistant S. aureus, carbapenem-resistant K. pneumoniae, carbapenem-resistant A. baumannii, carbapenem-resistant P. aeruginosa, and carbapenem-resistant E. coli, increased with the severity of pneumonia. Conclusion: Respiratory tract infections were the most common site of bacterial co-infection in patients with COVID-19. Severe cases were more susceptible to multidrug-resistant pathogens, leading to a higher mortality rate. Timely control and prevention of co-infection are crucial for improving the prognosis of patients with COVID-19.

Published
2024
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