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Sachula,* Zhenjie Yang,* Tianyuan Yu, Jinping Chen, Runlong Zhang, Yingqi Zhang, Jiayue Liu, Hanyu Zhang, Jiawei Sun School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tianyuan Yu, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, People’s Republic of China, Tel +8618210220868, Email yutianyuan@sina.comPurpose: This study aimed to investigate changes in metabolomic expression in the spinal dorsal horn (SDH) and thalamus during a Tuina session, aiming to elucidate the mechanism of immediate analgesia.Methods: The rats were randomly divided into three groups: the Sham group, the Model group, and the Tuina group. A minor chronic constriction injury (minor CCI) model was established in both the Model group and the Tuina group. The therapeutic effect of Tuina was determined using the mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) tests. Differential metabolites of the SDH and thalamus were detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Bioinformatic analysis was performed using CV, PCA, Venn, and KEGG.Results: The therapeutic effect of MWT and TWL after instant Tuina intervention was significant. The therapeutic effect of Tuina instant was significantly better compared to the Model group. In the Veen analysis, it was found that Tuina instantly regulates 10 differential metabolites in the SDH and 5 differential metabolites in the thalamus. In the KEGG enrichment analysis, we found that differential metabolites were enriched in 43 pathways in the thalamus and 70 pathways in the SDH.Conclusion: Tuina therapy may have analgesic effects by metabolizing neurotransmitters such as 2-Picolinic Acid, 5-Hydroxy-Tryptophan Glutathione Betaine-aldehyde-chloride Leucine Lysine Methionine Sarcosine Succinic Acid Histidine Acetylcho

Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-consuming and costly process of annotating graph labels manually prompts the growth of self-supervised learning (SSL) techniques. As a dominant approach of SSL, Contrastive learning (CL) learns discriminative representations by differentiating between positive and negative samples. However, when applied to graph data, it overemphasizes global patterns while neglecting local structures. To tackle the above issue, we propose \underline{Local}-aware \underline{G}raph \underline{C}ontrastive \underline{L}earning (\textbf{\methnametrim}), a self-supervised learning framework that supplementarily captures local graph information with masking-based modeling compared with vanilla contrastive learning. Extensive experiments validate the superiority of \methname against state-of-the-art methods, demonstrating its promise as a comprehensive graph representation learner.

The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conventional dense future endpoints with a small set of fixed prior motion intention points. However, the fixed prior intention points make the MTR multi-modal prediction distribution over-scattered and infeasible in many scenarios. In this paper, we propose the ControlMTR framework to tackle the aforementioned issues by generating scene-compliant intention points and additionally predicting driving control commands, which are then converted into trajectories by a simple kinematic model with soft constraints. These control-generated trajectories will guide the directly predicted trajectories by an auxiliary loss function. Together with our proposed scene-compliant intention points, they can effectively restrict the prediction distribution within the road boundaries and suppress infeasible off-road predictions while enhancing prediction performance. Remarkably, without resorting to additional model ensemble techniques, our method surpasses the baseline MTR model across all performance metrics, achieving notable improvements of 5.22% in SoftmAP and a 4.15% reduction in MissRate. Our approach notably results in a 41.85% reduction in the cross-boundary rate of the MTR, effectively ensuring that the prediction distribution is confined within the drivable area.

Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interactions. Diffusion models have recently risen to prominence, and have proven particularly effective in pedestrian motion prediction tasks. However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models. In response to these impediments, we propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents with enhanced resistance to noise. The core idea of our model is to learn a coarse-grained prior distribution of trajectory, which can skip a large number of denoise steps. This advancement not only boosts sampling efficiency but also maintains the fidelity of prediction accuracy. Our method meets the rigorous real-time operational standards essential for autonomous vehicles, enabling prompt trajectory generation that is vital for secure and efficient navigation. Through extensive experiments, our method speeds up the inference time to 136ms compared to standard diffusion model, and achieves significant improvement in multi-agent motion prediction on the Argoverse 1 motion forecasting dataset., Comment: 7 pages, 4 figures

Contains fulltext : 295057.pdf (Publisher’s version ) (Open Access)

The oblique-incidence reflectivity difference (OI-RD) microscope is a label-free detection system for microarrays that has many successful applications in high throughput drug screening. The increase and optimization of the detection speed of the OI-RD microscope will enable it to be a potential ultra-high throughput screening tool. This work presents a series of optimization methods that can significantly reduce the time to scan an OI-RD image. The wait time for the lock-in amplifier was decreased by the proper selection of the time constant and development of a new electronic amplifier. In addition, the time for the software to acquire data and for translation stage movement was also minimized. As a result, the detection speed of the OI-RD microscope is 10 times faster than before, making the OI-RD microscope suitable for ultra-high throughput screening applications.

Solar energy is regarded as the most attractive energy source for producing clean water and green energy, as well as for photocatalytic pollutants removal. MXene, a 2D photothermal material, has opened up new opportunities in solar energy harvesting due to its unique properties. However, it has limitations, such as strong oxidation and degradation tendencies, that affect its stability. To address these challenges, herein we fabricated a highly stable MXene-PVA-TiO2@PVDF Janus membrane and tested it in an advanced photothermal-catalytic membrane distillation (PMD) system. The modified MXene-PVA-TiO2@PVDF membrane demonstrated superior photothermal desalination flux (1.23 kg/m2h with 99.9% salt rejection) and efficient photodegradation capacity (methylene blue removal rate > 95%) under 1 sun irradiation. The superior photothermal desalination efficiency of the prepared membrane is attributed to the wide-spectrum optical absorption and exceptional light-to-heat conversion characteristics of MXene, while a substantial enhancement in the photodegradation performance is ascribed to the formation of the MXene-TiO2 2D/3D hierarchical structure acting as a direct Z-scheme photocatalyst. The chemical and mechanical strength was significantly enhanced due to the robust 2D network structure of MXene-PVA. Overall, this study demonstrates the feasibility of synergetic photothermal-catalytic membrane distillation and the promising prospects of MXene materials in this field.

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out test sets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques. The paper highlights the state-of-the-art technologies embedded in each of these critical components, providing a profound understanding of the advancements in RAG systems. Furthermore, this paper introduces up-to-date evaluation framework and benchmark. At the end, this article delineates the challenges currently faced and points out prospective avenues for research and development., Comment: Ongoing Work

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics., Comment: 15 pages, 6 figures

Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the computational demands of conventional iterative phase retrieval algorithms have limited their real-time imaging potential. We demonstrate a learning-based MCF phase imaging method, that significantly reduced the phase reconstruction time to 5.5 ms, enabling video-rate imaging at 181 fps. Moreover, we introduce an innovative optical system that automatically generated the first open-source dataset tailored for MCF phase imaging, comprising 50,176 paired speckle and phase images. Our trained deep neural network (DNN) demonstrates robust phase reconstruction performance in experiments with a mean fidelity of up to 99.8\%. Such an efficient fiber phase imaging approach can broaden the applications of QPI in hard-to-reach areas., Comment: 5 pages. 5 figures

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes a value alignment benchmark named Flames, which encompasses both common harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony. Accordingly, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting 17 mainstream LLMs, we obtain model responses and rigorously annotate them for detailed evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. We also develop a lightweight specified scorer capable of scoring LLMs across multiple dimensions to efficiently evaluate new models on the benchmark. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. Our benchmark is publicly available at https://github.com/AIFlames/Flames., Comment: Accepted to the NAACL 2024

Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch., Comment: 8 pages, 5 figures, 2 tables

The robustness of SLAM (Simultaneous Localization and Mapping) algorithms under challenging environmental conditions is critical for the success of autonomous driving. However, the real-world impact of such conditions remains largely unexplored due to the difficulty of altering environmental parameters in a controlled manner. To address this, we introduce CARLA-Loc, a synthetic dataset designed for challenging and dynamic environments, created using the CARLA simulator. Our dataset integrates a variety of sensors, including cameras, event cameras, LiDAR, radar, and IMU, etc. with tuned parameters and modifications to ensure the realism of the generated data. CARLA-Loc comprises 7 maps and 42 sequences, each varying in dynamics and weather conditions. Additionally, a pipeline script is provided that allows users to generate custom sequences conveniently. We evaluated 5 visual-based and 4 LiDAR-based SLAM algorithms across different sequences, analyzing how various challenging environmental factors influence localization accuracy. Our findings demonstrate the utility of the CARLA-Loc dataset in validating the efficacy of SLAM algorithms under diverse conditions.

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like fiber-optic cell manipulation. The random and discrete distribution of the fiber cores in an MCF induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel deep neural network—CoreNet, providing accurate tailored CGHs generation for MCFs at a near video rate. The CoreNet is trained by unsupervised learning and speeds up the computation time by two magnitudes with high fidelity light field generation compared to the previously reported CGH algorithms for MCFs. Real-time generated tailored CGHs are on-the-fly loaded to the phase-only spatial light modulator (SLM) for near video-rate complex light fields generation through the MCF microendoscope. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.

Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In this paper, we propose a planning-centric prediction neural network, which takes surrounding agents' historical states and map context information as input, and outputs the joint multi-modal prediction trajectories for surrounding agents, as well as a sequence of control commands for the ego vehicle by imitation learning. An agent-agent interaction module along the time axis is proposed in our network architecture to better comprehend the relationship among all the other intelligent agents on the road. To incorporate the map's topological information, a Dynamic Graph Convolutional Neural Network (DGCNN) is employed to process the road network topology. Besides, the whole architecture can serve as a backbone for the Differentiable Integrated motion Prediction with Planning (DIPP) method by providing accurate prediction results and initial planning commands. Experiments are conducted on real-world datasets to demonstrate the improvements made by our proposed method in both planning and prediction accuracy compared to the previous state-of-the-art methods., Comment: 8 pages, 5 figures

In this paper we propose and analyze finite element discontinuous Galerkin methods for the one- and two-dimensional stochastic Maxwell equations with multiplicative noise. The discrete energy law of the semi-discrete DG methods were studied. Optimal error estimate of the semi-discrete method is obtained for the one-dimensional case, and the two-dimensional case on both rectangular meshes and triangular meshes under certain mesh assumptions. Strong Taylor 2.0 scheme is used as the temporal discretization. Both one- and two-dimensional numerical results are presented to validate the theoretical analysis results.

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Information (MI) between different augmented views of the same graph (positive pairs). However, the GCL is limited by dimensional collapse, i.e., embedding vectors only occupy a low-dimensional subspace. In this paper, we show that the smoothing effect of the graph pooling and the implicit regularization of the graph convolution are two causes of the dimensional collapse in GCL. To mitigate the above issue, we propose a non-maximum removal graph contrastive learning approach (nmrGCL), which removes "prominent'' dimensions (i.e., contribute most in similarity measurement) for positive pair in the pre-text task. Comprehensive experiments on various benchmark datasets are conducted to demonstrate the effectiveness of nmrGCL, and the results show that our model outperforms the state-of-the-art methods. Source code will be made publicly available.

This study presents a layer-by-layer interfacial polymerization approach to enhance the chlorine resistance and water permeability of thin-film composite (TFC) polyamide (PA) forward osmosis membranes. The PA film was prepared by self-polymerization using 3,5-Dihydroxybenzoic acid (DHBA) and trimesoyl chloride (TMC) with addition of 4-amino-benzene sulfonamide (4-ABSA), which is a sulfonamide monomer, on a polysulfone (PSF) support layer. The cross-linking structure and surface characterisation of the DHBA-ABSA membrane was studied systematically. FTIR results showed the successful grafting of the sulfonamide group on the membrane surface. Compared with the MPD-TMC membrane, the water flux of three modified membranes—DHBA, ABSA, and DHBA with ABSA—improved by 27.6%, 44.0%, and 67.6%, respectively, and reverse salt flux decreased by 9.9%, 12.3%, and 16.2%, respectively. Furthermore, the chlorine-stability test using 200 ppm NaClO indicated stable long-term performance under different pH values. The stable sulfonamide structure of ABSA with N-H group effectively prevented chlorine from directly attacking the active layer and improve the chlorine-stability of the membrane. In addition, the abundant hydrophilic groups on ABSA and DHBA monomers formed a hydration layer with water molecules on the membrane surface through hydrogen bonding, which enhanced the permeability of the TFC-PA membranes. The findings of this study demonstrate the DHBA-ABSA membrane's wider application potential in water and wastewater treatment processes. © 2022 Elsevier B.V.

The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like fiber-optic cell manipulation. The random and discrete distribution of the fiber cores in an MCF induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel deep neural network—CoreNet, providing accurate tailored CGHs generation for MCFs at a near video rate. The CoreNet is trained by unsupervised learning and speeds up the computation time by two magnitudes with high fidelity light field generation compared to the previously reported CGH algorithms for MCFs. Real-time generated tailored CGHs are on-the-fly loaded to the phase-only spatial light modulator (SLM) for near video-rate complex light fields generation through the MCF microendoscope. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

Hang Zhou,1,2,* Jiawei Sun,1,* Jiaqi Wu,1 Hong Wei,1 Xianli Zhou1 1In-Patient Ultrasound Department, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150081, People’s Republic of China; 2Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, 310058, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xianli ZhouIn-Patient Ultrasound Department, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150081, People’s Republic of ChinaTel/Fax +86-0451-86605362Email hrbzhouxl@163.comBackground: The specific microenvironment of solid tumors, which is characterized by hypoxia, overexpression of glutathione (GSH), and high accumulation of anti-inflammatory tumor-associated macrophages (TAMs), limits the efficiency of sonodynamic therapy (SDT).Method and Results: Herein, a multifunctional nanoplatform was engineered to modulate the tumor microenvironment for highly efficient SDT. In this system, sonosensitizers and catalase were encapsulated in disulfide-bridged mesoporous organosilicon nanoparticles with high loading, which protected the activity of catalase and ensure the stability of sonosensitizers and enzyme. Subsequently, hyaluronic acid was grafted onto the nanoplatform to reeducate TAMs and induce the secretion of exogenous hydrogen peroxide. Due to the good protection of enzyme, the catalase within the nanoplatform efficiently produced the mount of O2 through decomposing the hydrogen peroxide in tumor tissues, which remarkably alleviated tumor hypoxia. Furthermore, degradation of the nanoparticles was observed in response to GSH, which effectively decreased the intracellular GSH level, further favoring SDT-triggered anticancer effect.Conclusion: Based on the multiple adjustments to tumor microenvironment, our nanoplatform displayed extraordinary sonodynamic therapeutic effect with low systemic toxicity.Ke

Achieving strong coupling between emitters and cavity photons holds an important position in the light-matter interaction due to its applications such as polariton lasing, all-optical switches, and quantum information processing. However, room-temperature polaritonic devices with subwavelength dimensions based on strong light-matter coupling are difficult to realize using traditional emitter-cavity coupled systems. In recent years, coupled systems constructed from plasmonic nanostructures and transition metal dichalcogenides (TMDs) have shown their potential in achieving room-temperature strong coupling and robustness in the nanofabrication processes. This minireview presents the recent progress in strong plasmon-exciton coupling in such plasmonic-TMD hybrid structures. Differing from a broader scope of strong coupling, we focus on the plasmon-exciton coupling between excitons in TMDs and plasmons in single nanoparticles, nanoparticle-over-mirrors, and plasmonic arrays. In addition, we discuss the future perspectives on the strong plasmon-exciton coupling at few-excitons level and the nonlinear response of these hybrid structures in the strong coupling regime.

Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations that are hard to be executed in parallel, which heavily reduces the training speed. To address this issue, in this article, we propose a methodology to alleviate the BN's cost by using only a few sampled or generated data for mean and variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation in sampling while the efficiency expects more regular execution patterns. To this end, we design two categories of approach: sampling or creating a few uncorrelated data for statistics' estimation with certain strategy constraints. The former includes 'batch sampling (BS)' that randomly selects a few samples from each batch and 'feature sampling (FS)' that randomly selects a small patch from each feature map of all samples, and the latter is 'virtual data set normalization (VDN)' that generates a few synthetic random samples to directly create uncorrelated data for statistics' estimation. Accordingly, multiway strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The proposed methods are comprehensively evaluated on various DNN models, where the loss of model accuracy and the convergence rate are negligible. Without the support of any specialized libraries, 1.98\times BN layer acceleration and 23.2% overall training speedup can be practically achieved on modern GPUs. Furthermore, our methods demonstrate powerful performance when solving the well-known 'micro-BN' problem in the case of a tiny batch size. This article provides a promising solution for the efficient training of high-perfo

Achieving strong coupling between emitters and cavity photons holds an important position in the light-matter interaction due to its applications such as polariton lasing, all-optical switches, and quantum information processing. However, room-temperature polaritonic devices with subwavelength dimensions based on strong light-matter coupling are difficult to realize using traditional emitter-cavity coupled systems. In recent years, coupled systems constructed from plasmonic nanostructures and transition metal dichalcogenides (TMDs) have shown their potential in achieving room-temperature strong coupling and robustness in the nanofabrication processes. This minireview presents the recent progress in strong plasmon-exciton coupling in such plasmonic-TMD hybrid structures. Differing from a broader scope of strong coupling, we focus on the plasmon-exciton coupling between excitons in TMDs and plasmons in single nanoparticles, nanoparticle-over-mirrors, and plasmonic arrays. In addition, we discuss the future perspectives on the strong plasmon-exciton coupling at few-excitons level and the nonlinear response of these hybrid structures in the strong coupling regime.

Hang Zhou,1,2,* Jiawei Sun,1,* Jiaqi Wu,1 Hong Wei,1 Xianli Zhou1 1In-Patient Ultrasound Department, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150081, People’s Republic of China; 2Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, 310058, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xianli ZhouIn-Patient Ultrasound Department, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150081, People’s Republic of ChinaTel/Fax +86-0451-86605362Email hrbzhouxl@163.comBackground: The specific microenvironment of solid tumors, which is characterized by hypoxia, overexpression of glutathione (GSH), and high accumulation of anti-inflammatory tumor-associated macrophages (TAMs), limits the efficiency of sonodynamic therapy (SDT).Method and Results: Herein, a multifunctional nanoplatform was engineered to modulate the tumor microenvironment for highly efficient SDT. In this system, sonosensitizers and catalase were encapsulated in disulfide-bridged mesoporous organosilicon nanoparticles with high loading, which protected the activity of catalase and ensure the stability of sonosensitizers and enzyme. Subsequently, hyaluronic acid was grafted onto the nanoplatform to reeducate TAMs and induce the secretion of exogenous hydrogen peroxide. Due to the good protection of enzyme, the catalase within the nanoplatform efficiently produced the mount of O2 through decomposing the hydrogen peroxide in tumor tissues, which remarkably alleviated tumor hypoxia. Furthermore, degradation of the nanoparticles was observed in response to GSH, which effectively decreased the intracellular GSH level, further favoring SDT-triggered anticancer effect.Conclusion: Based on the multiple adjustments to tumor microenvironment, our nanoplatform displayed extraordinary sonodynamic therapeutic effect with low systemic toxicity.Ke

The generation of tailored light with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like in vivo optogenetic stimulation and fiber-optic cell manipulation. The random and discrete distribution of the fiber cores induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel phase encoder deep neural network (CoreNet), which can generate accurate tailored CGHs for MCFs at a near video-rate. Simulations show that CoreNet can speed up the computation time by two magnitudes and increase the fidelity of the generated light field compared to the conventional CGH techniques. For the first time, real-time generated tailored CGHs are on-the-fly loaded to the phase-only SLM for dynamic light fields generation through the MCF microendoscope in experiments. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.

One- and multi-dimensional stochastic Maxwell equations with additive noise are considered in this paper. It is known that such system can be written in the multi-symplectic structure, and the stochastic energy increases linearly in time. High order discontinuous Galerkin methods are designed for the stochastic Maxwell equations with additive noise, and we show that the proposed methods satisfy the discrete form of the stochastic energy linear growth property and preserve the multi-symplectic structure on the discrete level. Optimal error estimate of the semi-discrete DG method is also analyzed. The fully discrete methods are obtained by coupling with symplectic temporal discretizations. One- and two-dimensional numerical results are provided to demonstrate the performance of the proposed methods, and optimal error estimates and linear growth of the discrete energy can be observed for all cases.

Boussinesq type equations have been widely studied to model the surface water wave. In this paper, we consider the abcd Boussinesq system which is a family of Boussinesq type equations including many well-known models such as the classical Boussinesq system, BBM-BBM system, Bona-Smith system etc. We propose local discontinuous Galerkin (LDG) methods, with carefully chosen numerical fluxes, to numerically solve this abcd Boussinesq system. The main focus of this paper is to rigorously establish a priori error estimate of the proposed LDG methods for a wide range of the parameters a, b, c, d. Numerical experiments are shown to test the convergence rates, and to demonstrate that the proposed methods can simulate the head-on collision of traveling wave and finite time blow-up behavior well.

The hybrid combination of two-dimensional (2D) transition metal dichalcogenides (TMDs) and plasmonic materials open up novel means of (ultrafast) optoelectronic applications and manipulation of nanoscale light–matter interaction. However, control of the plasmonic excitations by TMDs themselves has not been investigated. Here, we show that the ultrathin 2D WSe2 crystallites permit nanoscale spatially controlled coherent excitation of surface plasmon polaritons (SPPs) on smooth Au films. The resulting complex plasmonic interference patterns are recorded with nanoscale resolution in a photoemission electron microscope. Modeling shows good agreement with experiments and further indicates how SPPs can be tailored with high spatiotemporal precision using the shape of the 2D TMDs with thicknesses down to single molecular layers. We demonstrate the use of WSe2 nanocrystals as 2D optical elements for exploring the ultrafast dynamics of SPPs. Using few-femtosecond laser pulse pairs we excite an SPP at the boundary of a WSe2 crystal and then have a WSe2 monolayer wedge act as a delay line inducing a spatially varying phase difference down to the attosecond time range. The observed effects are a natural yet unexplored consequence of high dielectric functional values of TMDs in the visible range that should be considered when designing metal–TMD hybrid devices. As the 2D TMD crystals are stable in air, can be defect free, can be synthesized in many shapes, and are reliably positioned on metal surfaces, using them to excite and steer SPPs adds an interesting alternative in designing hybrid structures for plasmonic control.

The hybrid combination of two-dimensional (2D) transition metal dichalcogenides (TMDs) and plasmonic materials open up novel means of (ultrafast) optoelectronic applications and manipulation of nanoscale light–matter interaction. However, control of the plasmonic excitations by TMDs themselves has not been investigated. Here, we show that the ultrathin 2D WSe2 crystallites permit nanoscale spatially controlled coherent excitation of surface plasmon polaritons (SPPs) on smooth Au films. The resulting complex plasmonic interference patterns are recorded with nanoscale resolution in a photoemission electron microscope. Modeling shows good agreement with experiments and further indicates how SPPs can be tailored with high spatiotemporal precision using the shape of the 2D TMDs with thicknesses down to single molecular layers. We demonstrate the use of WSe2 nanocrystals as 2D optical elements for exploring the ultrafast dynamics of SPPs. Using few-femtosecond laser pulse pairs we excite an SPP at the boundary of a WSe2 crystal and then have a WSe2 monolayer wedge act as a delay line inducing a spatially varying phase difference down to the attosecond time range. The observed effects are a natural yet unexplored consequence of high dielectric functional values of TMDs in the visible range that should be considered when designing metal–TMD hybrid devices. As the 2D TMD crystals are stable in air, can be defect free, can be synthesized in many shapes, and are reliably positioned on metal surfaces, using them to excite and steer SPPs adds an interesting alternative in designing hybrid structures for plasmonic control.

Liheng Chen,1 Jiawei Sun,2,3 Qiaohong Wang,1 Lingzhen Hu,1 Yi Zhang,1 Huibin Ma,2,3 Xize Jia,4,5 Xuyan Yang1 1Department of Rheumatology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310009, People’s Republic of China; 2School of Information and Electronics Technology, Jiamusi University, Jiamusi, People’s Republic of China; 3Integrated Medical Research School, Jiamusi University, Jiamusi, People’s Republic of China; 4Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, People’s Republic of China; 5Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People’s Republic of ChinaCorrespondence: Xize JiaInstitute of Psychological Sciences, Hangzhou Normal University, Hangzhou, People’s Republic of ChinaEmail jiaxize@hznu.edu.cnXuyan YangDepartment of Rheumatology, Second Affiliated Hospital, College of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, People’s Republic of ChinaEmail hangzhouyxy@zju.edu.cnPurpose: In this study, we seek to investigate dynamic changes of brain activity in non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) patients with inactive disease.Patients and Methods: Thirty-one non-NPSLE patients with inactive disease and 20 matched healthy controls underwent the blood oxygenation level-dependent fMRI examination. Dynamic regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) were used to analyze the brain activity in typical band (0.01– 0.08 Hz), slow-4 (0.027– 0.073 Hz) and slow-5 (0.01– 0.027 Hz). Pearson’s correlation analysis was performed to correlate dynamic regional homogeneity (dReHo) and dynamic fractional amplitude of low-frequency fluctuations (dfALFF) values for clusters of voxels where significant group differences were found with clinical variables in non-NPSLE patients wi

Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures. Moreover, 3D objects can be placed with arbitrary position and orientation in real-world applications, which further requires shape descriptors to be robust to rigid transformations. The shape descriptions used in existing 3D shape retrieval systems fail to meet the above two criteria. In this paper, we introduce a novel deep architecture, RISA-Net, which learns rotation invariant 3D shape descriptors that are capable of encoding fine-grained geometric information and structural information, and thus achieve accurate results on the task of fine-grained 3D object retrieval. RISA-Net extracts a set of compact and detailed geometric features part-wisely and discriminatively estimates the contribution of each semantic part to shape representation. Furthermore, our method is able to learn the importance of geometric and structural information of all the parts when generating the final compact latent feature of a 3D shape for fine-grained retrieval. We also build and publish a new 3D shape dataset with sub-class labels for validating the performance of fine-grained 3D shape retrieval methods. Qualitative and quantitative experiments show that our RISA-Net outperforms state-of-the-art methods on the fine-grained object retrieval task, demonstrating its capability in geometric detail extraction. The code and dataset are available at: https://github.com/IGLICT/RisaNET., Comment: The code and dataset are available at: https://github.com/IGLICT/RisaNET

Control of the structure and function of three-dimensional multicellular tissues depends critically on the spatial and temporal coordination of cellular physical properties, yet the organizational principles that govern these events and their disruption in disease remain poorly understood. Using a multicellular mammary cancer organoid model, we map here the spatial and temporal evolution of positions, motions and physical characteristics of individual cells in three dimensions. Compared with cells in the organoid core, cells at the organoid periphery and the invasive front are found to be systematically softer, larger and more dynamic. These mechanical changes are shown to arise from supracellular fluid flow through gap junctions, the suppression of which delays the transition to an invasive phenotype. These findings highlight the role of spatiotemporal coordination of cellular physical properties in tissue organization and disease progression., National Cancer Institute (Grant 1U01CA202123)

Contains fulltext : 208623.pdf (publisher's version ) (Open Access)

Contains fulltext : 208623.pdf (publisher's version ) (Open Access)

In this paper, we show for the first time that environmentally friendly nanoplatelets synthesized from sugar beetroot waste with surface area and hydroxyl functional groups similar to those of graphene oxide (GO) can be used to significantly enhance the performance of cementitious composites. A comprehensive experimental and numerical simulation study was carried out to examine the performance of the bio waste-derived 2D nanoplatelets (BNP) in cementitious composites. The experimental results revealed that the addition of BNPs decreased the workability of the cement pastes due to their high surface area and dominant hydrophilic functional groups. The experimental results also revealed that the BNP sheets altered the morphology of the hydration phases of the cementitious composites. At 0.20-wt%, the BNP sheets increased the content of the C-S-H gels. At higher concentrations (i.e., 0.40-wt% and 0.60-wt%), however, the BNP sheets increased the content of the calcium hydroxide (Ca(OH)2) products and altered their sizes and morphologies. The flexural results demonstrated that the 0.20-wt% BNPs produced the highest flexural strength and modulus elasticity and they were increased by 75% and 200%, respectively. The numerical simulations were in good agreement with the fracture test results. Both results showed that the 0.20-wt% BNPs optimal concentration significantly enhanced the fracture properties of the cementitious composite and produced mixed mode crack propagation as a failure mode compared to Mode I crack propagation for the plain cementitious composite due to combined crack bridging and crack deflection toughening mechanisms. Because of this, the fracture energy and the fracture toughness were increased by about 88% and 106%, respectively.

In this paper, we show for the first time that environmentally friendly nanoplatelets synthesized from sugar beetroot waste with surface area and hydroxyl functional groups similar to those of graphene oxide (GO) can be used to significantly enhance the performance of cementitious composites. A comprehensive experimental and numerical simulation study was carried out to examine the performance of the bio waste-derived 2D nanoplatelets (BNP) in cementitious composites. The experimental results revealed that the addition of BNPs decreased the workability of the cement pastes due to their high surface area and dominant hydrophilic functional groups. The experimental results also revealed that the BNP sheets altered the morphology of the hydration phases of the cementitious composites. At 0.20-wt%, the BNP sheets increased the content of the C-S-H gels. At higher concentrations (i.e., 0.40-wt% and 0.60-wt%), however, the BNP sheets increased the content of the calcium hydroxide (Ca(OH)2) products and altered their sizes and morphologies. The flexural results demonstrated that the 0.20-wt% BNPs produced the highest flexural strength and modulus elasticity and they were increased by 75% and 200%, respectively. The numerical simulations were in good agreement with the fracture test results. Both results showed that the 0.20-wt% BNPs optimal concentration significantly enhanced the fracture properties of the cementitious composite and produced mixed mode crack propagation as a failure mode compared to Mode I crack propagation for the plain cementitious composite due to combined crack bridging and crack deflection toughening mechanisms. Because of this, the fracture energy and the fracture toughness were increased by about 88% and 106%, respectively.

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration. Specifically, we model it as a statistical sampling problem and identify that by sampling less correlated data, we can largely reduce the requirement of the number of data for statistics estimation in BN, which directly simplifies the reduction operations. Based on this conclusion, we propose two sampling strategies, "Batch Sampling" (randomly select several samples from each batch) and "Feature Sampling" (randomly select a small patch from each feature map of all samples), that take both computational efficiency and sample correlation into consideration. Furthermore, we introduce an extremely simple variant of BN, termed as Virtual Dataset Normalization (VDN), that can normalize the activations well with few synthetical random samples. All the proposed methods are evaluated on various datasets and networks, where an overall training speedup by up to 20% on GPU is practically achieved without the support of any specialized libraries, and the loss on accuracy and convergence rate are negligible. Finally, we extend our work to the "micro-batch normalization" problem and yield comparable performance with existing approaches at the case of tiny batch size.

Jiawei Sun,1 Lei Jiang,2 Yi Lin,3 Ethan Michael Gerhard,4 Xuehua Jiang,1 Li Li,3 Jian Yang,4 Zhongwei Gu3 1West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, 2Department of Pharmaceutics, China Pharmaceutical University, Nanjing, Jiangsu, 3National Engineering Research Center for Biomaterials, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 4Department of Biomedical Engineering Materials Research Institute, The Huck Institutes of The Life Sciences, The Pennsylvania State University, University Park, PA, USA Abstract: Mitochondria serve as both “energy factories” and “suicide weapon stores” of cells. Targeted delivery of cytotoxic drugs to the mitochondria of tumor cells and tumor vascular cells is a promising strategy to improve the efficacy of chemotherapy. Here, multistage tumor-targeting liposomes containing two targeted peptide-modified lipids, cRGD-PEG2000-DSPE and KLA-PEG2000-DSPE, were developed for encapsulation of the anticancer drug paclitaxel (PTX, RGD-KLA/PTX-Lips). Compared with Taxol (free PTX), RGD/PTX-Lips and KLA/PTX-Lips, the half-maximal inhibitory concentration (IC50) value of RGD-KLA/PTX-Lips in vitro was 1.9-, 36.7- and 22.7-fold lower with 4T1 cells, respectively, because of higher levels of cellular uptake. Similar results were also observed with human umbilical vascular endothelial cells (HUVECs). An apoptosis assay showed that the total apoptotic ratio of RGD-KLA/PTX-Lips was the highest because of the mitochondria-targeted drug delivery and the activation of mitochondrial apoptosis pathways, as evidenced by visible mitochondrial localization, decreased mitochondrial membrane potential, release of cytochrome c and increased activities of caspase-9 and caspase-3. The strongest tumor growth inhibition (TGI; 80.6%) and antiangiogenesis effects without systemic toxicity were also observed in RGD-KLA/PTX-Lip-treated 4T1 tumor xenograft BALB/c mice. In conclusio

Jiawei Sun,1 Lei Jiang,2 Yi Lin,3 Ethan Michael Gerhard,4 Xuehua Jiang,1 Li Li,3 Jian Yang,4 Zhongwei Gu3 1West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, 2Department of Pharmaceutics, China Pharmaceutical University, Nanjing, Jiangsu, 3National Engineering Research Center for Biomaterials, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 4Department of Biomedical Engineering Materials Research Institute, The Huck Institutes of The Life Sciences, The Pennsylvania State University, University Park, PA, USA Abstract: Mitochondria serve as both “energy factories” and “suicide weapon stores” of cells. Targeted delivery of cytotoxic drugs to the mitochondria of tumor cells and tumor vascular cells is a promising strategy to improve the efficacy of chemotherapy. Here, multistage tumor-targeting liposomes containing two targeted peptide-modified lipids, cRGD-PEG2000-DSPE and KLA-PEG2000-DSPE, were developed for encapsulation of the anticancer drug paclitaxel (PTX, RGD-KLA/PTX-Lips). Compared with Taxol (free PTX), RGD/PTX-Lips and KLA/PTX-Lips, the half-maximal inhibitory concentration (IC50) value of RGD-KLA/PTX-Lips in vitro was 1.9-, 36.7- and 22.7-fold lower with 4T1 cells, respectively, because of higher levels of cellular uptake. Similar results were also observed with human umbilical vascular endothelial cells (HUVECs). An apoptosis assay showed that the total apoptotic ratio of RGD-KLA/PTX-Lips was the highest because of the mitochondria-targeted drug delivery and the activation of mitochondrial apoptosis pathways, as evidenced by visible mitochondrial localization, decreased mitochondrial membrane potential, release of cytochrome c and increased activities of caspase-9 and caspase-3. The strongest tumor growth inhibition (TGI; 80.6%) and antiangiogenesis effects without systemic toxicity were also observed in RGD-KLA/PTX-Lip-treated 4T1 tumor xenograft BALB/c mice. In conclusio

Jiawei Sun,1,* Jie Bao,2,* Yanan Shi,3 Bin Zhang,4 Lindong Yuan,5 Junhong Li,1 Lihai Zhang,1 Mo Sun,6 Ling Zhang,2 Wuzhuang Sun1 1Department of Respiratory Medicine, First Hospital of Hebei Medical University, 2Department of Respiratory Medicine, Chest Hospital of Hebei Province, 3Maternal and Child Health Care Center of Hebei Province, 4Department of Emergency, First Hospital of Hebei Medical University, Shijiazhuang, 5Department of Respiratory Medicine, People’s Hospital of Liaocheng, Liaocheng, 6Hebei Medical University, Shijiazhuang, People’s Republic of China *These authors contributed equally to this work Background: Proteases may play an important role in the development of chronic obstructive pulmonary disease and emphysema in response to cigarette smoke exposure (CSE). The current study was designed to investigate the expression of matrix metalloproteinase (MMP)-8, MMP-9, MMP-12, tissue inhibitor of MMP (TIMP)-1, and TIMP-4 in rat lung tissues in response to CSE, and assessed the effect of simvastatin in regulating expression of MMPs and TIMPs.Methods: Thirty normal Sprague Dawley (SD) rats were divided into control (n=10), CSE (n=10), and CSE plus simvastatin (n=10) groups. Animals were whole-body exposed to the cigarette smoke in the box for 1 hour each time, twice a day, 5 days a week for 16 weeks. Animals of CSE + simvastatin group were intra-gastrically administered simvastatin at a dose of 5 mg/kg/day followed by CSE. Bronchoalveolar lavage fluid was harvested for inflammatory cell count and lung tissues were stained for morphologic examination. Expression of mRNA and protein level of MMP-8, MMP-9, MMP-12, TIMP-1, and TIMP-4 was assessed by real-time reverse transcription polymerase chain reaction and immunohistochemistry, respectively.Results: CSE resulted in a significant increase of mean linear intercept (MLI: 34.6±2.0 µm) and bronchial wall thickness and diameter (BWT/D, 0.250±0.062) compared to

Hong Wei,1 Hongbin Wang,2 Qiao Ji,1 Jiawei Sun,1 Lin Tao,1 Xianli Zhou1 1Department of Ultrasonography, The Second Affiliated Hospital of Harbin Medical University, 2Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, People’s Republic of China Abstract: Nuclear receptor binding protein 1 (NRBP1) is a highly conserved protein that is ubiquitously expressed across cell types in humans. NRBP1 has been recently identified as an adaptor protein. It has been suggested that it plays important roles in cellular homeostasis and the pathophysiology of cancer. To determine whether NRBP1 is involved in the pathophysiology of breast cancer, we performed a correlation study between the expression level of NRBP1 and the clinicopathological features in 92 breast cancer patients. A strong correlation was detected between NRBP1 expression and advanced histopathology grades, tumor, node, and metastasis stage, tumor diameter, lymph node involvement, as well as the recurrence of breast cancer in 92 tested patients. The tumor tissues from patients also expressed lower NRBP1 than did adjacent healthy tissues. Furthermore, we overexpressed NRBP1 in MCF-7 and MDA-MB-231 breast cancer cell lines and found NRBP1 upregulation-inhibited cell proliferation by using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Blocking the autocrine Wnt signaling pathway by LGK974 could remove the NRBP1-overexpression-induced inhibition in breast cancer cells. The results of this study suggest that NRBP1 plays a tumor-suppressive role in breast cancer pathophysiology, which likely acts through the Wnt/β-catenin signaling pathway. Keywords: nuclear receptor binding protein 1 (NRBP1), breast cancer, clinicopathological features, tumor suppression Wnt/β-catenin pathway, cell proliferation