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Bone health related skeletal disorders are commonly diagnosed by X-ray imaging, but the radiation limits its use. Light excitation and optical imaging through the near-infrared-II window (NIR-II, 1000–1700 nm) can penetrate deep tissues without radiation risk, but the targeting of contrast agent is non-specific. Here, we report that lanthanide-doped nanocrystals can be passively transported by endothelial cells and macrophages from the blood vessels into bone marrow microenvironment. We found that this bone targeting scheme can be effective for longer than two months. We therefore developed an intravital 3D and high-resolution planar imaging instrumentation for bone disease diagnosis. We demonstrated the regular monitoring of 1 mm bone defects for 11 days in NIR-II window, with spatial resolution similar to X-ray imaging result, but more flexible use in prognosis. Moreover, the passive targeting can be used to reveal the early onset inflammation at the joints as the synovitis in the early stage of rheumatoid arthritis. Furthermore, the proposed method is comparable to micro computed tomography (µCT) in recognizing symptoms of osteoarthritis, including the mild hyperostosis in femur which is ~ 100 µm thicker than normal, and the growth of millimeter-scale osteophyte in the knee joint, which further proves the power and universality of our approach.

The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context modeling capacity, which is critical to semantic segmentation. In this paper, we propose a parametric-free patch rotate operation to reorganize the pixels spatially. It first divides the feature map into multiple groups and then rotates the patches within each group. Based on the proposed patch rotate operation, we design a novel segmentation network, named PRSeg, which includes an off-the-shelf backbone and a lightweight Patch Rotate MLP decoder containing multiple Dynamic Patch Rotate Blocks (DPR-Blocks). In each DPR-Block, the fully connected layer is performed following a Patch Rotate Module (PRM) to exchange spatial information between pixels. Specifically, in PRM, the feature map is first split into the reserved part and rotated part along the channel dimension according to the predicted probability of the Dynamic Channel Selection Module (DCSM), and our proposed patch rotate operation is only performed on the rotated part. Extensive experiments on ADE20K, Cityscapes and COCO-Stuff 10K datasets prove the effectiveness of our approach. We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation., Comment: Accepted by IEEE TCSVT

Conventional biomedical imaging modalities, including endoscopy, X-rays, and magnetic resonance, are invasive and cannot provide sufficient spatial and temporal resolutions for regular imaging of gastrointestinal (GI) tract to guide prognosis and therapy of GI diseases. Here we report a non-invasive method for optical imaging of GI tract. It is based on a new type of lanthanide-doped nanocrystal with near-infrared (NIR) excitation at 980 nm and second NIR window (NIR-IIb) (1500~1700 nm) fluorescence emission at around 1530 nm. The rational design and controlled synthesis of nanocrystals with high brightness have led to an absolute quantum yield (QY) up to 48.6%. Further benefitting from the minimized scattering through the NIR-IIb window, we enhanced the spatial resolution by 3 times compared with the other NIR-IIa (1000~1500 nm) contract agents for GI tract imaging. The approach also led to a high temporal resolution of 8 frames per second, so that the moment of mice intestinal peristalsis happened in one minute can be captured. Furthermore, with a light-sheet imaging system, we demonstrated a three-dimensional (3D) imaging of the stereoscopic structure of the GI tract. Moreover, we successfully translate these advances to diagnose inflammatory bowel disease (IBD) in a pre-clinical model of mice colitis.

In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i.e.,} classify each pixel representation to a specific category. However, these methods only focus on learning better pixel representations or classification kernels while ignoring the structural information of objects, which is critical to human decision-making mechanism. In this paper, we present a new paradigm for semantic segmentation, named structure-aware extraction. Specifically, it generates the segmentation results via the interactions between a set of learned structure tokens and the image feature, which aims to progressively extract the structural information of each category from the feature. Extensive experiments show that our StructToken outperforms the state-of-the-art on three widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K., Accept by IEEE TCSVT

Scorzonera austriaca Wild is a traditional herbal medicine; however, little is known with regard to the effect of flavonoids from S. austriaca (FSA) on liver injury induced by Carbon tetrachloride ...

Roughly 268,000 new cases of prostate cancer and 34,000 deaths from prostate cancer are projected by the American Cancer Society to occur in the United States in 2022. Androgen receptor is a key protein in the proliferation and survival of prostate cancer cells and has been revealed to be overexpressed in 30% to 50% of castration-resistant prostate cancer patients. One promising approach to reducing the level of this protein is Proteolysis Targeting Chimeras (PROTACs) that is an emerging drug discovery technology. PROTACs are hetero-bifunctional molecules where one end binds to a protein of interest and the other to an E3 ligase ligand, initiating the Ubiquitin-Proteasome Pathway for protein degradation. Two PROTACs with niclosamide as androgen receptor ligand and VHL-032 as the E3 ligase ligand have been designed and synthesized for suppressing proliferation of androgen receptor-positive prostate cancer cells via degrading androgen receptor. The in vitro antiproliferative assessment suggested that they can selectively suppress PC-3, LNCaP, and 22Rv1 prostate cancer cell proliferation, but cannot inhibit DU145 cell proliferation. However, the mechanism of both compounds in suppressing prostate cancer cell proliferation is not through the AR PROTAC mechanism because they did not degrade AR in our Western Blotting assay up to 1 µM.

The second near-infrared window (1000–1700 nm, NIR-II) emissive lanthanide-doped nanoparticles excited by 808 nm laser are ideal for in vivo bioimaging due to their non-heating excitation. The study of 808 nm excited lanthanide-doped nanoparticles not only provides high-quality NIR-II imaging but also can promote in vivo multiplexed bioimaging under single excitation. Herein, we prepared three sets of typical 808 nm excited lanthanide-doped nanoparticles (NaYF4: Nd3+@NaYF4, NaErF4@NaYF4, and NaYbF4: Er3+/Ce3+@NaYbF4@NaYF4: Yb3+/Nd3+) and then systematically compared their optical properties in the NIR-II window. Furthermore, we prepared water-soluble and biocompatible nanoprobes and achieved multiplexed NIR-II in vivo imaging in mice under single 808 nm excitation. These results highlight the potential of 808 nm excited lanthanide probes for physiological studies and biomedical applications.

Unlike the European Union emission trade system (EU ETS), China’s pilot ETSs implemented diversified policy designs instead of using a uniform framework. Variance ratio test is used to evaluate the...

To search for novel androgen receptor (AR) modulators for the potential treatment of castration-resistant prostate cancer (CRPC), naturally occurring silibinin was sought after as a lead compound because it possesses a moderate potency towards AR-positive prostate cancer cells and its chemical scaffold is dissimilar to all currently marketed AR antagonists. On the basis of the structure–activity relationships that we have explored, this study aims to incorporate carbamoyl groups to the alcoholic hydroxyl groups of silibinin to improve its capability in selectively suppressing AR-positive prostate cancer cell proliferation together with water solubility. To this end, a feasible approach was developed to regioselectively introduce a carbamoyl group to the secondary alcoholic hydroxyl group at C-3 without causing the undesired oxidation at C2–C3, providing an avenue for achieving 3-O-carbamoyl-5,7,20-O-trimethylsilybins. The application of the synthetic method can be extended to the synthesis of 3-O-carbamoyl-3′,4′,5,7-O-tetramethyltaxifolins. The antiproliferative potency of 5,7,20-O-trimethylsilybin and its nine 3-carbamoyl derivatives were assessed in an AR-positive LNCaP prostate cancer cell line and two AR-null prostate cancer cell lines (PC-3 and DU145). Our preliminary bioassay data imply that 5,7,20-O-trimethylsilybin and four 3-O-carbamoyl-5,7,20-O-trimethylsilybins emerge as very promising lead compounds due to the fact that they can selectively suppress AR-positive LNCaP cell proliferation. The IC50 values of these five 5,7,20-O-trimethylsilybins against the LNCaP cells fall into the range of 0.11–0.83 µM, which exhibit up to 660 times greater in vitro antiproliferative potency than silibinin. Our findings suggest that carbamoylated 5,7,20-O-trimethylsilybins could serve as a natural product-based scaffold for new antiandrogens for lethal castration-resistant prostate cancer.

The rotating construction of the bridge is the better choice, the continuous beam rotation intelligent monitoring system was built by the Academy of Railway Sciences and was developed and applied based on the “Smart Construction Site” project of the Huzhou-Hangzhou Railway. Firstly, this article introduces the system. The system is based on Beidou’s high-precision satellite positioning solution system to calculate the attitude of the bridge during the construction of the rotating body. The key stress level and change of the bridge can be obtained through the stress sensor,which can obtain the response trend of the pontic structure, and effectively control the stress damage and dangerous points in the first time. Secondly, it continuously moitors the temperature and humidity of the rotating bridge, wind speed, bridge pier strain, inclination angle, and rotation angle in real time during the entire course of the bridge rotation to ensure the safety of construction. Thirdly, points out the location of the sensor buried in the foundation pit during the bridge construction process. Finally, the principle and composition of the bridge health warning technology scheme based on BIM technology are introduced, and its significance and advantages are summarized. Provides solutions and experience for the long-term monitoring of bridges in rotating construction.

Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from the inexperienced user unfriendliness, high model bias, and low effectiveness-to-efficiency ratio. The three challenges result from the explicitness characteristic (i.e. explicit formulation of the reconstruction model) in these techniques. For these challenges, we are the first to develop an implicit strain reconstruction framework by a deep neural network architecture. However, the classic neural network methods are unsuitable to the strain reconstruction task because they are difficult to impose any direct influence on the intermediate state of the learning process. This may lead the map learned by the neural network to be biased with the desired map. In order to correct the intermediate state of the learning process, our framework proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network. It provides the causal privileged information besides the training examples to help the network learning, while makes these privileged information unavailable at the test stage. This improvement can narrow the search region of the map learned by the network, and thus prompts the network to evolve towards the actual ultrasound elastography process. Moreover, in order to ensure the causality in LUPI, our framework proposes a physically-based data generation strategy to produce the triplets of privileged information, training examples and labels. This data generation process can approximately describes the actual ultrasound elastography process by the numerical simulation based on the tissue biomechanics and ultrasound physics. It thus can build the causal relationship between the privileged information and training examples/labels. It can also address the medical data insufficiency problem. The performance of our framework has been validated on 100 simulation data, 42 phantom data and 4 real clinical data by comparing with the ground truth performed by an ultrasound simulation system and four state-of-the-art methods. The experimental results show that our framework is well agreed (average bias is 0.065 for strain reconstruction) with the ground truth, as well as superior to these state-of-the-art methods. These results can demonstrate the effectiveness of our framework in the strain reconstruction.

In this work, we developed an end-to-end convolutional neural network (CNN) to reconstruct the ultrasound elastography directly from radio frequency (RF) data. The novelty of this network is able to infer the distribution of elastography from real RF data by only using computational simulation as the training data. Moreover, this framework can generate displacement and strain field respectively both from ultrasound RF data directly. We evaluated the performance of this network on 50 simulated RF datasets, 42 phantom datasets, and 4 human datasets. The best results of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in simulated data, phantom data, and human data are 39.5 dB and 69.64 dB, 32.64 dB and 48.76 dB, 23.24 dB and 46.22 dB, respectively. Furthermore, we also compare the performance of our method to the state-of-art ultrasound elastography using normalized cross-correlation (NCC) technique. From this comparison, it shows that that our method can effectively compute the strain field robustly and accurately in the this paper. These results might imply great potential of this deep learning method in ultrasound elastography application.

This study starts with the principles of the variable dielectric-type cylindrical capacitive sensor, conducts the theoretical analysis after dielectric is divided into three phases (solid, liquid and gas), determines the relation between dielectric constant and other influence factors through the least squares fitting method, makes compensation for such factors as temperature, grain variety and air void ratio, builds the mathematical model of grain moisture content(GMC) through the variable dielectric-type cylindrical capacitive sensor, and suggests the capacitance measurement method based on pulse-width modulation. The experiment result shows that the suggested capacitance measurement method can accurately measure micro-(pF-class) capacitance, and the theoretical value of GMC as produced by this mathematical model falls within an allowable error range when compared to actual value, so this model proves to be rational.