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Whole-arm tactile feedback is crucial for robots to ensure safe physical interaction with their surroundings. This paper introduces CushSense, a fabric-based soft and stretchable tactile-sensing skin designed for physical human-robot interaction (pHRI) tasks such as robotic caregiving. Using stretchable fabric and hyper-elastic polymer, CushSense identifies contacts by monitoring capacitive changes due to skin deformation. CushSense is cost-effective ($\sim$US\$7 per taxel) and easy to fabricate. We detail the sensor design and fabrication process and perform characterization, highlighting its high sensing accuracy (relative error of 0.58%) and durability (0.054% accuracy drop after 1000 interactions). We also present a user study underscoring its perceived safety and comfort for the assistive task of limb manipulation. We open source all sensor-related resources on https://emprise.cs.cornell.edu/cushsense., Comment: 8 pages, 8 figures, ICRA2024

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, video-text consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Furthermore, our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models. Predicting the source generative model can make the AIGC video features more discriminative, which enhances the quality assessment performance. The proposed method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video, demonstrating its effectiveness. Code will be available at https://github.com/Coobiw/TriVQA., Comment: 9 pages, 3 figures, 3 tables. Accepted by CVPR2024 Workshop (3rd place winner of NTIRE2024 Quality Assessment for AI-Generated Content - Track 2 Video)

Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields. However, challenges like evaluating models and bias amplification remain.

Xuefeng Li,1,2 Hui Jiang,1 Xiaoyu Liang1 1School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China; 2School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of ChinaCorrespondence: Hui Jiang, School of Engineering Science, University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, People’s Republic of China, Email huijiang@ucas.ac.cnPurpose: Based on the Chinese context, this study uses severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) outbreaks as examples to identify the risk factors that lead to the major emerging infectious diseases outbreak, and put forward risk governance strategies to improve China’s biosecurity risk prevention and control capabilities.Material and Methods: This study combines grounded theory and WSR methodology, and utilizes the NVivo 12.0 qualitative analysis software to identify the risk factors that led to the major emerging infectious diseases outbreak. The research data was sourced from 168 publicly available official documents, which are highly authoritative and reliable.Results: This study identified 10 categories of Wuli risk factors, 6 categories of logical Shili risk factors, and 8 categories of human Renli risk factors that contributed to the outbreak of major emerging infectious diseases. These risk factors were distributed across the early stages of the outbreak, and have different mechanisms of action at the macro and micro levels.Conclusion: This study identified the risk factors that lead to the outbreak of major emerging infectious disease, and discovered the mechanism of the outbreak at the macro and micro levels. At the macro level, Wuli risk factors are the forefront antecedents that lead to the outbreak of the crisis, Renli factors are the intermediate regulatory factors, and Shili risk factors are the back-end posterior factors. A

Enhancing the domain generalization performance of Face Anti-Spoofing (FAS) techniques has emerged as a research focus. Existing methods are dedicated to extracting domain-invariant features from various training domains. Despite the promising performance, the extracted features inevitably contain residual style feature bias (e.g., illumination, capture device), resulting in inferior generalization performance. In this paper, we propose an alternative and effective solution, the Textually Guided Domain Generalization (TeG-DG) framework, which can effectively leverage text information for cross-domain alignment. Our core insight is that text, as a more abstract and universal form of expression, can capture the commonalities and essential characteristics across various attacks, bridging the gap between different image domains. Contrary to existing vision-language models, the proposed framework is elaborately designed to enhance the domain generalization ability of the FAS task. Concretely, we first design a Hierarchical Attention Fusion (HAF) module to enable adaptive aggregation of visual features at different levels; Then, a Textual-Enhanced Visual Discriminator (TEVD) is proposed for not only better alignment between the two modalities but also to regularize the classifier with unbiased text features. TeG-DG significantly outperforms previous approaches, especially in situations with extremely limited source domain data (~14% and ~12% improvements on HTER and AUC respectively), showcasing impressive few-shot performance.

Due to their great environmental hazards, the widely used legacy perfluoroalkyl acids (PFAAs) are gradually restricted, and novel alternatives are being developed and applied. For efficient control of emerging environmental risks in agricultural production, we systematically studied the source apportionment in field soils and bioaccumulation characteristics in multiple crops of 12 PFAAs and five novel alternatives in an industrial-intensive region of China, followed by human exposure estimation and health risk assessment. Compared with perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), shorter-chained PFAAs and novel alternatives have become the dominant components in local soils and crops, indicating their wide application. A positive matrix factorization (PMF) model coupled with multivariate analysis identified fluoropolymer manufacturing and textile treatment as the principal sources. The bioaccumulation factors (BAFs) of individual PFAAs and alternatives in crops decreased with increasing carbon chain lengths. As a novel alternative of PFOA, hexafluoropropylene oxide dimer acid (GenX) exhibited much higher BAFs; for the alternative of PFOS, 6:2 chlorinated polyfluorinated ether sulfonic acid (6:2 Cl-PFESA) showed lower BAFs. The bioaccumulation capacities of PFAAs and alternatives were also associated with soil organic matter and crop species. Through crop consumption, short-chained PFAAs and novel alternatives might pose emerging human health threats.

BackgroundPrevious experimental studies showed that limiting methionine in the diet of animals or in cell culture media suppresses mammary cancer cell proliferation or metastasis. However, no previous study has investigated the associations of changes in methionine intake with survival among breast cancer survivors. We aimed to examine the association between changes in dietary intake of methionine, folate/folic acid, and vitamin B12 from before to after diagnosis of breast cancer, and mortality among breast cancer survivors.MethodsWe included 1553 postmenopausal women from the Women's Health Initiative who were diagnosed with invasive breast cancer and completed a food frequency questionnaire both before and after breast cancer diagnosis. Multivariable Cox proportional hazards regression models were used to estimate adjusted hazard ratios (HRs) and 95% confidence (CIs) of all-cause and breast cancer mortality associated with changes in methionine intake and changes in folate/folic acid and vitamin B12 intake.ResultsRelative to pre-diagnosis, 28% of women decreased methionine intake by ≥20%, 30% of women increased methionine intake by ≥20%, and 42% of women had a relatively stable methionine intake (±19.9%) following breast cancer diagnosis. During a mean 16.1 years of follow up, there were 772 deaths in total, including 195 deaths from breast cancer. Compared to women with relatively stable methionine intake, women with decreased methionine intake had lower risks of all-cause (HR 0.78, 95% CI 0.62-0.97) and breast cancer mortality (HR 0.58, 95% CI 0.37-0.91) in fully adjusted models. In contrast, increased methionine intake or changes in folate/folic acid or vitamin B12 intake were not associated with all-cause or breast cancer mortality.ConclusionsAmong breast cancer survivors, decreased methionine intake after breast cancer diagnosis was associated with lower risk of all-cause and breast cancer mortality.

Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models. However, due to the large and unnecessary increase in the margin along adversarial directions, adversarial training causes heavy cross-over between natural examples and adversarial examples, which is not conducive to balancing the trade-off between robustness and natural accuracy. In this paper, we propose a novel adversarial training scheme to achieve a better trade-off between robustness and natural accuracy. It aims to learn a moderate-inclusive decision boundary, which means that the margins of natural examples under the decision boundary are moderate. We call this scheme Moderate-Margin Adversarial Training (MMAT), which generates finer-grained adversarial examples to mitigate the cross-over problem. We also take advantage of logits from a teacher model that has been well-trained to guide the learning of our model. Finally, MMAT achieves high natural accuracy and robustness under both black-box and white-box attacks. On SVHN, for example, state-of-the-art robustness and natural accuracy are achieved.

Due to their great environmental hazards, the widely used legacy perfluoroalkyl acids (PFAAs) are gradually restricted, and novel alternatives are being developed and applied. For efficient control of emerging environmental risks in agricultural production, we systematically studied the source apportionment in field soils and bioaccumulation characteristics in multiple crops of 12 PFAAs and five novel alternatives in an industrial-intensive region of China, followed by human exposure estimation and health risk assessment. Compared with perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), shorter-chained PFAAs and novel alternatives have become the dominant components in local soils and crops, indicating their wide application. A positive matrix factorization (PMF) model coupled with multivariate analysis identified fluoropolymer manufacturing and textile treatment as the principal sources. The bioaccumulation factors (BAFs) of individual PFAAs and alternatives in crops decreased with increasing carbon chain lengths. As a novel alternative of PFOA, hexafluoropropylene oxide dimer acid (GenX) exhibited much higher BAFs; for the alternative of PFOS, 6:2 chlorinated polyfluorinated ether sulfonic acid (6:2 Cl-PFESA) showed lower BAFs. The bioaccumulation capacities of PFAAs and alternatives were also associated with soil organic matter and crop species. Through crop consumption, short-chained PFAAs and novel alternatives might pose emerging human health threats.

The Distributed Denial-of-Service (DDoS) attack is known as one of the most destructive attacks on the Internet. With the advent of new computing paradigms, such as Cloud and Mobile computing, and the emergence of pervasive technology, such as the Internet of Things, on one hand, these revolutionized technologies enable the availability of services and applications to everyone. On the other hand, these techniques also benefit attackers to exploit the vulnerabilities and deploy attacks in more efficient ways. Latest network security reports have shown that distributed Denial of Service (DDoS) attacks have been growing dramatically in volume, frequency, sophistication and impact, making it one of the most challenging threats in the Internet. An unfortunate state of affairs is that the remediation strategies have fallen behind attackers. The severe impact caused by recent DDoS attacks strongly indicates the need for an effective DDoS defense system. We study the current existing solution space, and summarize three fundamental requirements for an effective DDoS defense system: 1) an accurate detection with minimal false alarms; 2) an effective inline inspection and instant mitigation, and 3) a dynamic, distributed and collaborative defense infrastructure. This thesis aims at providing such a defense system that fulfills all the requirements. In this thesis, we explore and address the problem from three directions: 1) we strive to understand the existing detection strategies and provide a survey of an empirical analysis of machine learning based detection techniques; 2) we develop a novel hybrid detection model which ensembles a deep learning model for a practical flow by flow detection and a classic machine learning model that is aware of the network status, and 3) we present the design and implementation of an intelligent, distributed and collaborative DDoS defense system that effectively mitigate the impact of DDoS attacks. The performance evaluation results show that

The Distributed Denial-of-Service (DDoS) attack is known as one of the most destructive attacks on the Internet. With the advent of new computing paradigms, such as Cloud and Mobile computing, and the emergence of pervasive technology, such as the Internet of Things, on one hand, these revolutionized technologies enable the availability of services and applications to everyone. On the other hand, these techniques also benefit attackers to exploit the vulnerabilities and deploy attacks in more efficient ways. Latest network security reports have shown that distributed Denial of Service (DDoS) attacks have been growing dramatically in volume, frequency, sophistication and impact, making it one of the most challenging threats in the Internet. An unfortunate state of affairs is that the remediation strategies have fallen behind attackers. The severe impact caused by recent DDoS attacks strongly indicates the need for an effective DDoS defense system. We study the current existing solution space, and summarize three fundamental requirements for an effective DDoS defense system: 1) an accurate detection with minimal false alarms; 2) an effective inline inspection and instant mitigation, and 3) a dynamic, distributed and collaborative defense infrastructure. This thesis aims at providing such a defense system that fulfills all the requirements. In this thesis, we explore and address the problem from three directions: 1) we strive to understand the existing detection strategies and provide a survey of an empirical analysis of machine learning based detection techniques; 2) we develop a novel hybrid detection model which ensembles a deep learning model for a practical flow by flow detection and a classic machine learning model that is aware of the network status, and 3) we present the design and implementation of an intelligent, distributed and collaborative DDoS defense system that effectively mitigate the impact of DDoS attacks. The performance evaluation results show that

Drought stress is a global problem, and the lack of water is a key factor that leads to agricultural shortages. MicroRNAs play a crucial role in the plant drought stress response; however, the microRNAs and their targets involved in drought response have not been well elucidated. In the present study, we used Illumina platform (https://www.illumina.com/) and combined data from miRNA, RNA, and degradome sequencing to explore the drought- and organ-specific miRNAs in orchardgrass (Dactylis glomerata L.) leaf and root. We aimed to find potential miRNA–mRNA regulation patterns responding to drought conditions. In total, 519 (486 conserved and 33 novel) miRNAs were identified, of which, 41 miRNAs had significant differential expression among the comparisons (p < 0.05). We also identified 55,366 unigenes by RNA-Seq, where 12,535 unigenes were differently expressed. Finally, our degradome analysis revealed that 5950 transcripts were targeted by 487 miRNAs. A correlation analysis identified that miRNA ata-miR164c-3p and its target heat shock protein family A (HSP70) member 5 gene comp59407_c0 (BIPE3) may be essential in organ-specific plant drought stress response and/or adaptation in orchardgrass. Additionally, Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses found that “antigen processing and presentation” was the most enriched downregulated pathway in adaptation to drought conditions. Taken together, we explored the genes and miRNAs that may be involved in drought adaptation of orchardgrass and identified how they may be regulated. These results serve as a valuable genetic resource for future studies focusing on how plants adapted to drought conditions.

Jianwei Duan,1,* Chao Liu,1,* Xiaoyu Liang,1 Xuanling Li,1 Youlu Chen,1 Zuoguan Chen,2 Xiaoli Wang,1 Deling Kong,1,3 Yongjun Li,2 Jing Yang1 1Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China; 2Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China; 3Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin, China *These authors contributed equally to this work Background: To address the issue of delivery of proteins, a six-arm copolymer, six-arm poly(ε-caprolactone)–poly(ethylene glycol) (6S-PCL-PEG), was synthesized by a simple two-step reaction. Thereafter, the application of 6S-PCL-PEG as a protein carrier was evaluated. Materials and methods: A six-arm copolymer, six-arm poly(ε-caprolactone) (6S-PCL), was synthesized by ring-opening polymerization, with stannous octoate as a catalyst and inositol as an initiator. Then, poly(ethylene glycol) (PEG) was linked with 6S-PCL by oxalyl chloride to obtain 6S-PCL-PEG. Hydrogen-1 nuclear magnetic resonance spectrum, Fourier-transform infrared spectroscopy, and gel-permeation chromatography were conducted to identify the structure of 6S-PCL-PEG. The biocompatibility of the 6S-PCL-PEG was evaluated by a cell counting kit-8 assay. Polymeric nanoparticles (NPs) were prepared by a water-in-oil-in-water double emulsion (W1/O/W2) solvent evaporation method. The size distribution and zeta potential of NPs were determined by dynamic light scattering. Transmission electron microscopy was used to observe the morphology of NPs. Drug-loading capacity, encapsulation efficiency, and the release behavior of ovalbumin (OVA)-loading NPs were tested by the bicinchoninic acid assay kit. The stability and activity of OVA released from NPs were detected

Jianwei Duan,1,* Chao Liu,1,* Xiaoyu Liang,1 Xuanling Li,1 Youlu Chen,1 Zuoguan Chen,2 Xiaoli Wang,1 Deling Kong,1,3 Yongjun Li,2 Jing Yang1 1Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China; 2Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China; 3Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin, China *These authors contributed equally to this work Background: To address the issue of delivery of proteins, a six-arm copolymer, six-arm poly(ε-caprolactone)–poly(ethylene glycol) (6S-PCL-PEG), was synthesized by a simple two-step reaction. Thereafter, the application of 6S-PCL-PEG as a protein carrier was evaluated. Materials and methods: A six-arm copolymer, six-arm poly(ε-caprolactone) (6S-PCL), was synthesized by ring-opening polymerization, with stannous octoate as a catalyst and inositol as an initiator. Then, poly(ethylene glycol) (PEG) was linked with 6S-PCL by oxalyl chloride to obtain 6S-PCL-PEG. Hydrogen-1 nuclear magnetic resonance spectrum, Fourier-transform infrared spectroscopy, and gel-permeation chromatography were conducted to identify the structure of 6S-PCL-PEG. The biocompatibility of the 6S-PCL-PEG was evaluated by a cell counting kit-8 assay. Polymeric nanoparticles (NPs) were prepared by a water-in-oil-in-water double emulsion (W1/O/W2) solvent evaporation method. The size distribution and zeta potential of NPs were determined by dynamic light scattering. Transmission electron microscopy was used to observe the morphology of NPs. Drug-loading capacity, encapsulation efficiency, and the release behavior of ovalbumin (OVA)-loading NPs were tested by the bicinchoninic acid assay kit. The stability and activity of OVA released from NPs were detected

Drought stress is a global problem, and the lack of water is a key factor that leads to agricultural shortages. MicroRNAs play a crucial role in the plant drought stress response; however, the microRNAs and their targets involved in drought response have not been well elucidated. In the present study, we used Illumina platform (https://www.illumina.com/) and combined data from miRNA, RNA, and degradome sequencing to explore the drought- and organ-specific miRNAs in orchardgrass (Dactylis glomerata L.) leaf and root. We aimed to find potential miRNA–mRNA regulation patterns responding to drought conditions. In total, 519 (486 conserved and 33 novel) miRNAs were identified, of which, 41 miRNAs had significant differential expression among the comparisons (p < 0.05). We also identified 55,366 unigenes by RNA-Seq, where 12,535 unigenes were differently expressed. Finally, our degradome analysis revealed that 5950 transcripts were targeted by 487 miRNAs. A correlation analysis identified that miRNA ata-miR164c-3p and its target heat shock protein family A (HSP70) member 5 gene comp59407_c0 (BIPE3) may be essential in organ-specific plant drought stress response and/or adaptation in orchardgrass. Additionally, Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses found that “antigen processing and presentation” was the most enriched downregulated pathway in adaptation to drought conditions. Taken together, we explored the genes and miRNAs that may be involved in drought adaptation of orchardgrass and identified how they may be regulated. These results serve as a valuable genetic resource for future studies focusing on how plants adapted to drought conditions.

Xiaowei Zhu,* Chao Liu,* Jianwei Duan, Xiaoyu Liang, Xuanling Li, Hongfan Sun, Deling Kong, Jing Yang Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, People’s Republic of China *These authors contributed equally to this work Purpose: Synthesis of star-shaped block copolymer with oxalyl chloride and preparation of micelles to assess the prospect for drug-carrier applications.Materials and methods: Three-arm star block copolymers of poly(lactic-co-glycolic acid) (3S-PLGA)–polyethylene glycol (PEG) were synthesized by ring-opening polymerization, then PEG as the hydrophilic block was linked to the terminal hydroxyl of 3S-PLGA with oxalyl chloride. Fourier-transform infrared (FT-IR) spectroscopy, gel-permeation chromatography (GPC), hydrogen nuclear magnetic resonance (1H-NMR) spectra, and differential scanning calorimetry were employed to identify the structure and properties of 3S-PLGA-PEG. Rapamycin (RPM)-loaded micelles were prepared by solvent evaporation, and pyrene was used as the fluorescence probe to detect the critical micelle concentration of the copolymer. The particle size, distribution, and ζ-potential of the micelles were determined by dynamic light scattering, and the morphology of the RPM-loaded micelles was analyzed by transmission electron microscopy. High-performance liquid chromatography was conducted to analyze encapsulation efficiency and drug-loading capacity, as well as the release behavior of RPM-loaded micelles. The biocompatibility of material and the cytostatic effect of RPM-loaded micelles were investigated by Cell Counting Kit 8 assay.Results: FT-IR, GPC, and 1H-NMR suggested that 3S-PLGA-PEG was successfully synthesized. The RPM-loaded micelles prepared with the 3S-PLGA-PEG possessed good properties. The micelles had good average diameter and encapsulation efficiency. For in vitro release

Xiaowei Zhu,* Chao Liu,* Jianwei Duan, Xiaoyu Liang, Xuanling Li, Hongfan Sun, Deling Kong, Jing Yang Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, People’s Republic of China *These authors contributed equally to this work Purpose: Synthesis of star-shaped block copolymer with oxalyl chloride and preparation of micelles to assess the prospect for drug-carrier applications.Materials and methods: Three-arm star block copolymers of poly(lactic-co-glycolic acid) (3S-PLGA)–polyethylene glycol (PEG) were synthesized by ring-opening polymerization, then PEG as the hydrophilic block was linked to the terminal hydroxyl of 3S-PLGA with oxalyl chloride. Fourier-transform infrared (FT-IR) spectroscopy, gel-permeation chromatography (GPC), hydrogen nuclear magnetic resonance (1H-NMR) spectra, and differential scanning calorimetry were employed to identify the structure and properties of 3S-PLGA-PEG. Rapamycin (RPM)-loaded micelles were prepared by solvent evaporation, and pyrene was used as the fluorescence probe to detect the critical micelle concentration of the copolymer. The particle size, distribution, and ζ-potential of the micelles were determined by dynamic light scattering, and the morphology of the RPM-loaded micelles was analyzed by transmission electron microscopy. High-performance liquid chromatography was conducted to analyze encapsulation efficiency and drug-loading capacity, as well as the release behavior of RPM-loaded micelles. The biocompatibility of material and the cytostatic effect of RPM-loaded micelles were investigated by Cell Counting Kit 8 assay.Results: FT-IR, GPC, and 1H-NMR suggested that 3S-PLGA-PEG was successfully synthesized. The RPM-loaded micelles prepared with the 3S-PLGA-PEG possessed good properties. The micelles had good average diameter and encapsulation efficiency. For in vitro release