Pol2Pol: self-supervised polarimetric image denoising.

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.

Dimer Peptide Ligands of Vascular Endothelial Growth Factor: Optimizing Linker Length for High Affinity and Antiangiogenic Activity.

Macromolecular ligands targeting vascular endothelial growth factor A (VEGF) to inhibit pathological angiogenesis are used in the clinic for the treatment of cancers and ocular diseases. To develop smaller ligands retaining high affinity through an avidity effect, here we design homodimer peptides targeting the two symmetrical binding sites of the VEGF homodimer. A series of 11 dimers were synthesized with flexible poly(ethylene glycol) (PEG) linkers of increasing lengths. The binding mode was determined by size exclusion chromatography, and analytical thermodynamic parameters were measured by isothermal titration calorimetry and compared to the antibody bevacizumab. The effect of linker length was qualitatively correlated to a theoretical model. With the optimal length in PEG25-dimer D6, the binding affinity was improved 40-fold compared to a monomer control, resulting in a single-digit nanomolar Kd value. Finally, we validated the benefit of the dimerization strategy by evaluating the activity of control monomers and selected dimers in cell-based assays with human umbilical vein endothelial cells (HUVECs).

Polarized image super-resolution via a deep convolutional neural network.

Reduced resolution of polarized images makes it difficult to distinguish detailed polarization information and limits the ability to identify small targets and weak signals. A possible way to handle this problem is the polarization super-resolution (SR), which aims to obtain a high-resolution polarized image from a low-resolution one. However, compared with the traditional intensity-mode image SR, the polarization SR is more challenging because more channels and their nonlinear cross-links need to be considered as well as the polarization and intensity information need to be reconstructed simultaneously. This paper analyzes the polarized image degradation and proposes a deep convolutional neural network for polarization SR reconstruction based on two degradation models. The network structure and the well-designed loss function have been verified to effectively balance the restoration of intensity and polarization information, and can realize the SR with a maximum scaling factor of four. Experimental results show that the proposed method outperforms other SR methods in terms of both quantitative evaluation and visual effect evaluation for two degradation models with different scaling factors.

Physics-informed neural network for polarimetric underwater imaging.

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.

Attention-based neural network for polarimetric image denoising.

In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually.

Underwater image restoration via Stokes decomposition.

In this Letter, we present a Stokes imaging-based method to restore objects and enhance image contrast in turbid water. In the system, a light source illuminates the objects with two orthometric polarization states; based on a new Stokes decomposition model, the recorded images are converted to Stokes maps and subsequently restored to a clear image, free of reflections and scattered lights. A mathematical model has been developed to explain the Stokes decomposition and how the undesired reflections and scattered lights are rejected. Imaging experiments have been devised and performed on different objects, e.g., metals and plastics, under different turbidities. The results demonstrate enhanced image quality and capability to distinguish polarization differences. This new, to the best of our knowledge, method can be readily applied to practical underwater object detection and potentially realize clear vision in other scattering media.

Identification of PRMT5 inhibitors with novel scaffold structures through virtual screening and biological evaluations.

Protein arginine methyltransferase 5 (PRMT5), an important member in PRMT family, has been validated as a promising anticancer target. In this study, through the combination of virtual screening and biological experiments, we have identified two PRMT5 inhibitors with novel scaffold structures. Among them, compound Y2431 showed moderate activity with IC50 value of 10.09 µM and displayed good selectivity against other methyltransferases. The molecular docking analysis and molecular dynamics (MD) simulations suggested that the compound occupied the substrate-arginine binding site. Furthermore, Y2431 exhibited anti-proliferative activity to leukemia cells by inducing cell cycle arrest. Overall, the hit compound could provide a novel scaffold for further optimization of small-molecule PRMT5 inhibitors.

Electrospun polycaprolactone nanofibrous membranes loaded with baicalin for antibacterial wound dressing.

Due to the rise in bacterial resistance, the antibacterial extractions from Chinese herbs have been used more frequently for wound care. In this work, baicalin, an extraction from the Chinese herb Scutellaria baicalensis, was utilized as the antibacterial component in the poly(ε-caprolactone)/MXene (PCL/Ti3C2TX) hybrid nanofibrous membranes for wound dressing. The results revealed that the presence of Ti3C2TX aided in the diameter reduction of the electrospun nanofibers. The PCL hybrid membrane containing 3 wt% Ti3C2TX nanoflakes and 5 wt% baicalin exhibited the smallest mean diameter of 210 nm. Meanwhile, the antibacterial tests demonstrated that the PCL ternary hybrid nanofibers containing Ti3C2TX and baicalin exhibited adequate antibacterial activity against the Gram-positive bacterial S. aureus due to the good synergistic effects of Ti3C2TX naoflakes and baicalin. The addition of Ti3C2TX nanoflakes and baicalin could significantly improve the hydrophilicity of the membranes, resulting in the release of baicalin from the nanofibers. In addition, the cytotoxicity of the nanofibers on rat skeletal myoblast L6 cells confirmed their good compatibility with these PCL-based nanofibrous membrances. This work offers a feasible way to prepare antibacterial nanofibrous membranes using Chinese herb extraction for wound dressing applications.

Berberine coated biocomposite hemostatic film based alginate as absorbable biomaterial for wound healing.

In wound treatment, severe bleeding and infection are always primary challenges. Therefore, it is highly desired to develop novel dressing with both hemostatic and antibacterial capability. Herein, a series of biocomposite hemostatic films (BHFs) based alginate/chitosan/collagen-berberine have been prepared and well characterized for further biofunctional study. We have demonstrated that the hemostatic and antibacterial activities were significantly enhanced by calcium/berberine dual-crosslinking system in the film. Through the synergistic effects, BHF-6B exhibited a shorter in vivo clotting and wound healing time than that of commercial dressing in rat tail amputation and full-thickness skin defect models. Additionally, BHF-6B showed excellent bacteriostatic activity with long-term effects. Moreover, hemolysis and cytotoxicity tests in vitro illustrated the prominent biocompatibility of the composite films. Notably, BHF-6B could be degraded quickly and completely in vivo. Overall, the present work indicated that the functionalized BHF-6B has great potential as an absorbable biomaterial for wound treatment.

Unsupervised anomaly detection of MEMS in low illumination based on polarimetric Support Vector Data Description.

Low illuminated images make it challenging to conduct anomaly detection on material surface. Adding polarimetric information helps expand pixel range and recover background structure of network inputs. In this letter, an anomaly detection method in low illumination is proposed which utilizes polarization imaging and patch-wise Support Vector Data Description (SVDD) model. Polarimetric information of Micro Electromechanical System (MEMS) surface is captured by a division-of-focal- plane (DoFP) polarization camera and used to enhance low illuminated images. The enhanced images without defects serve as training sets of model to make it available for anomaly detection. The proposed method can generate heatmaps to locate defects correctly. It reaches 0.996 anomaly scores, which is 22.4% higher than that of low illuminated images and even higher than normal illuminated images.

A Cyclic Peptide Epitope of an Under-Explored VEGF-B Loop 1 Demonstrated In Vivo Anti-Angiogenic and Anti-Tumor Activities.

Pathological angiogenesis is mainly initiated by the binding of abnormal expressed vascular endothelial growth factors (VEGFs) to their receptors (VEGFRs). Blocking the VEGF/VEGFR interaction is a clinically proven treatment in cancer. Our previous work by epitope scan had identified cyclic peptides, mimicking the loop 1 of VEGF-A, VEGF-B and placental growth factor (PlGF), inhibited effectively the VEGF/VEGFR interaction in ELISA. We described here the docking study of these peptides on VEGFR1 to identify their binding sites. The cellular anti-angiogenic activities were examined by inhibition of VEGF-A induced cell proliferation, migration and tube formation in human umbilical vein endothelial cells (HUVECs). The ability of these peptides to inhibit MAPK/ERK1/2 signaling pathway was examined as well. On chick embryo chorioallantoic membrane (CAM) model, a cyclic peptide named B-cL1 with most potent in vitro activity showed important in vivo anti-angiogenic effect. Finally, B-cL1 inhibited VEGF induced human gastric cancer SGC-7901 cells proliferation. It showed anti-tumoral effect on SGC-7901 xenografted BALB/c nude mouse model. The cyclic peptides B-cL1 constitutes an anti-angiogenic peptide drug lead for the design of new and more potent VEGFR antagonists in the treatment of angiogenesis related diseases.

Automatic underwater polarization imaging without background region or any prior.

Previous polarization underwater imaging methods based on the physical scattering model usually require background region included in the image and the prior knowledge, which hinders its practical application. In this paper, we analyze and optimize the physically feasible region and propose an improved method by degenerating intermediate variables, which can realize automatic underwater image recovery without background region or any prior. The proposed method does not need to estimate the intermediate variables in the traditional underwater imaging model and is adaptable to the underwater image with non-uniform illumination, which avoids the poor and unstable image recovery performance caused by inaccurate estimation of intermediate parameters due to the improper identification of the background region. Meanwhile, our method is effective for both images without background region and images in which the background region is hard to be identified. In addition, our method solves the significant variation in recovery results caused by the different selection of background regions and the inconsistency of parameter adjustment. The experimental results of different underwater scenes show that the proposed method can enhance image contrast while preserving image details without introducing considerable noise, and the proposed method is effective for the dense turbid medium.

Frequency interferometric localization microscopy.

We propose a new super-resolution microscopy, named frequency interferometric localization microscopy (FILM). FILM is implemented by incorporating a Michelson interferometer into wide-field fluorescence microscope, which introduces coherence time as a new auxiliary axis to obtain the spectral information of individual fluorophores. After the time-wavelength transformation, the homogeneous linewidth of individual fluorophores can be isolated from the inhomogeneous broadening distribution of the fluorophore ensemble. Thus, the nearby fluorophores with a distinguishable central wavelength can be separated in the frequency domain and localized with accuracy beyond the diffraction limit. The principle of the method, experimental schematics, and reconstruction algorithm are numerically demonstrated. With properly prepared fluorophores, FILM has the potential to reach, in principle, molecular-scale spatial resolution.

Integration time optimization and starting angle autocalibration of full Stokes imagers based on a rotating retarder.

Full Stokes imaging can be performed with a continuously rotating retarder in front of a fixed polarizer and a standard camera (RRFP) or a division of a focal plane polarization camera (RRDOFP). We determine the optimal number and duration of intensity measurements through a cycle of the retarder for these two types of setups as a function of instrument and noise parameters. We show that this number mainly depends on the type of noise that corrupts the measurements. We also show that with these setups, the starting angle of the retarder need not be known precisely and can be autocalibrated, which facilitates synchronization of the rotating retarder with the camera. We investigate the precision and feasibility domain of this autocalibration and show the RRDOFP setup has more attractive properties compared with RRFP setup. These results are important to optimize and facilitate the operation of polarization imagers based on a rotating retarder.

NaYF4:Yb/Tm@SiO2-Dox/Cur-CS/OSA nanoparticles with pH and photon responses.

Stimulus-triggered drug delivery systems (DDSs) based on lanthanide-doped upconversion nanoparticles (UCNPs) have attracted significant attention for treating cancers due to their merits of high drug availability, precisely controlled drug release, and low side-effects. However, such DDSs usually exhibit a single stimulus-response, which may limit the efficiency of cancer treatment. To extend response types in a single DDS, we construct NaYF4:Yb/Tm@SiO2-doxorubicin (Dox)/curcumin (Cur)-chitosan (CS)/2-Octen-1-ylsuccinic anhydride (OSA) nanoparticles with core-shell structures. Our method is based on the exploration of the synergistic effect of UCNPs and multiple drugs. In particular, the NaYF4:Yb/Tm is used to convert near-infrared light to visible light, activating Cur photosensitizers to produce singlet oxygen for photodynamic therapy, while CS/OSA responds to a low pH environment to release cancer drugs, including Dox and Cur for chemotherapy through breaking a free carboxyl group. The results show that the UCNPs with a 40 nm diameter, 23 nm thick mesoporous SiO2, and 19/1 mol% Yb3+/Tm3+concentrations could continuously release Dox and Cur at a pH value of 6.5 within 6 h after the excitation of a 980 nm-wavelength CW laser. Our study provides a promising approach for developing efficient DDSs for cancer treatment.

Berberine-Coated Biomimetic Composite Microspheres for Simultaneously Hemostatic and Antibacterial Performance.

Biomimetic microspheres containing alginate/carboxymethylcellulose/gelatin and coated with 0%, 1%, 3%, and 6% berberine (BACG, BACG-1B, BACG-3B, BACG-6B) were prepared by the oil-in-water emulsion method combined with spray drying. Through a series of physicochemical parameters and determination of hemostatic properties in vitro and in vivo, the results indicated that BACG and BACG-Bs were effective in inducing platelet adhesion/aggregation and promoting the hemostatic potential due to their biomimetic structure and rough surface. In addition, BACG-6B with high berberine proportion presented better hemostatic performance compared with the commercial hemostatic agent compound microporous polysaccharide hemostatic powder (CMPHP). BACG-6B also showed strong antibacterial activity in the in vitro test. The hemolysis test and cytotoxicity evaluation further revealed that the novel composite biomaterials have good hemocompatibility and biocompatibility. Thus, BACG-6B provides a new strategy for developing a due-functional (hemostat/antibacterial) biomedical material, which may have broad and promising applications in the future.

IPLNet: a neural network for intensity-polarization imaging in low light.

Imaging in low light is significant but challenging in many applications. Adding the polarization information into the imaging system compromises the drawbacks of the conventional intensity imaging to some extent. However, generally speaking, the qualities of intensity images and polarization images cannot be compatible due to the characteristic differences in polarimetric operators. In this Letter, we collected, to the best of our knowledge, the first polarimetric imaging dataset in low light and present a specially designed neural network to enhance the image qualities of intensity and polarization simultaneously. Both indoor and outdoor experiments demonstrate the effectiveness and superiority of this neural network-based solution, which may find important applications for object detection and vision in photon-starved environments.

Graphene-based dual-mode modulators.

Mode-division multiplexing (MDM) has attracted broad attention as it could effectively boost up transmission capability by utilizing optical modes as a spatial dimension in optical interconnects. In such a technique, different data channels are usually modulated to the respective carriers over different spatial modes by using individual parallel electro-optic modulators. Each modulated channel is then multiplexed to a multi-mode waveguide. However, the method inevitably suffers from a high cost, large device footprint and high insertion loss. Here, we design intensity and phase dual-mode modulators, enabling simultaneous modulations over two channels via a graphene-on-silicon waveguide. Our method is based on the exploration of co-planar interactions between structured graphene nanoribbons (GNs) and spatial modes in a silicon waveguide. Specifically, the zeroth-order transverse electric (TE0) and first-order transverse electric (TE1) modes are modulated separately and simultaneously by applying independent driving electrodes to different GNs in an identical modulator. Our study is expected to open an avenue to develop high-density MDM photonics integrated circuits for tera-scale optical interconnects.

When is retardance autocalibration of microgrid-based full Stokes imagers possible and useful?

Full Stokes polarimetric images can be obtained from two acquisitions with a microgrid polarization camera equipped with a retarder. When the retardance is imperfectly known, it can be calibrated from the measurements, but this requires three image acquisitions and may cause divergence of estimation variance at a low signal-to-noise ratio. We determine closed-form equations allowing one to decide in which experimental conditions autocalibration is possible and useful, and to quantify the performance gain obtained in practice. These results are validated by real-world experiments.

Learning-based denoising for polarimetric images.

Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising. This method is based on the residual dense network and can significantly suppress the noise in polarimetric images. The experimental results show that the proposed method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.