Search

In this paper, we review both development and applications of the atom-bond electronegativity equalization method fused into molecular mechanics, i.e., ABEEM/MM polarizable force field (FF). We will focus on the applications of the ABEEM/MM in pure water systems, chemical and biomolecular ion-containing systems, small molecules and biomolecules, etc. The results show that the performance of ABEEM/MM is generally better than that of the commonly used nonpolarizable force fields.

Ghosting artifacts caused by moving objects and misalignments are a key challenge in constructing high dynamic range (HDR) images. Current methods first register the input low dynamic range (LDR) images using optical flow before merging them. This process is error-prone, and often causes ghosting in the resulting merged image. We propose a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images. Unlike previous methods that directly stack the LDR images or features for merging, we use dual-attention modules to guide the merging according to the reference image. DAHDRNet thus exploits both spatial attention and feature channel attention to achieve ghost-free merging. The spatial attention modules automatically suppress undesired components caused by misalignments and saturation, and enhance the fine details in the non-reference images. The channel attention modules adaptively rescale channel-wise features by considering the inter-dependencies between channels. The dual-attention approach is applied recurrently to further improve feature representation, and thus alignment. A dilated residual dense block is devised to make full use of the hierarchical features and increase the receptive field when hallucinating missing details. We employ a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images. Although DAHDRNet is not flow-based, it can be applied to flow-based registration to reduce artifacts caused by optical-flow estimation errors. Experiments on different datasets show that the proposed DAHDRNet achieves state-of-the-art quantitative and qualitative results.

Objective Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disease in reproductive women, and the endocrine levels are also affected by diseases. The aim of this study was to determine the effect of thrombospondin-1 (TSP-1) on PCOS rat model. Methods We established the PCOS rat model, the serum hormones including TSP-1 expression were determined and morphological characteristics were investigated to evaluate the model. These above endocrine and morphological features were investigated again to evaluate the effect of TSP-1 treatment. Results In the PCOS model group, the serum hormones change (higher luteinizing hormone, testosterone and estrogen) and decreased TSP-1 expression levels were found compared with the control group. Besides, the morphological characteristics of PCOS were also observed in the model group. After TSP-1 treatment, the higher TSP-1, ANGPT2, PDGFB and PDGFD expression levels, the lower LH and T levels, decreased vessel density as well as VEGFA and ANGPT1 expression levels were found compared with the control group, and the ovary morphological changes were also observed in the TSP-1 experimental group. Conclusions TSP-1 delivery system might be an alternative therapy for PCOS treatment.

The homeostasis of the ocular lens is maintained by a microcirculation system propagated through gap junction channels. It is well established that the intercellular communications of the lens become deteriorative during aging. However, the molecular basis for this change in human lenses has not been well defined. Here, we present evidence to show that over 90% of Cx46 and Cx50 are lost in the fiber cells of normal human lenses aged 50 and above. From transparent to cataractous lenses, while Cx43 was upregulated, both Cx46 and Cx50 were significantly down-regulated in the lens epithelia. During aging of mouse lenses, Cx43 remained unchanged, but both Cx46 and Cx50 were significantly downregulated. Under oxidative stress treatment, mouse lenses develop in vitro cataractogenesis. Associated with this process, Cx43 was significantly upregulated, in contrast, Cx46 and Cx50 were sharply downregulated. Together, our results for the first time reveal that downregulation in Cx46 and Cx50 levels appears to be the major reason for the diminished coupling conductance, and the aging-dependent loss of Cx46 and Cx50 promotes senile cataractogenesis.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds)., Comment: CVPR Workshops 2022. 15 pages, 21 figures, 2 tables

Removing rain from images is of a great importance to various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuristics to handcraft various priors to remove or separate rain from images. Recently, deep learning models are proposed to learn various end-to-end methods to complete this task. However, these methods might fail in obtaining satisfactory results in some real-world scenarios, especially when the captured images suffer from heavy rain that brings not only rain streaks but also a haze-like effect (caused by the accumulation of tiny raindrops). Different from most of the existing deep learning deraining methods that focus on handling rain streaks, we add a new variable to model the haze-like effect in a general model for rain, based on which a deep neural network is designed accordingly. Specifically, in our method, two branches are designed to handle rain streaks and the haze-like effect, respectively. The output of such branch structure is fed to an additional module to further enhance the performance. Three modules are trained jointly, leading to an end-to-end network, which supports a adjustment to the strength of removing the haze-like effect. Extensive experiments on several datasets show that our method outperforms several state-of-the-art methods in both objective assessment and visual quality.

The Chayu area is located at the southeastern margin of the Qinghai-Tibet Plateau. This region was considered to be in the southeastward extension of the Lhasa Block, bounded by Nujiang suture zone in the north and Yarlung Zangbo suture zone in the south. The Demala Group complex, a set of high-grade metamorphic gneisses widely distributed in the Chayu area, is known as the Precambrian metamorphic basement of the Lhasa Block in the area. According to field-based investigations and microstructure analysis, the Demala Group complex is considered to mainly consist of banded biotite plagiogneisses, biotite quartzofeldspathic gneiss, granitic gneiss, amphibolite, mica schist, and quartz schist, with many leucogranite veins. The zircon U-Pb ages of two granitic gneiss samples are 205 ± 1 Ma and 218 ± 1 Ma, respectively, representing the ages of their protoliths. The zircons from two biotite plagiogneisses samples show core-rim structures. The U-Pb ages of the cores are mainly 644 –446 Ma, 1213 –865 Ma, and 1780 –1400 Ma, reflecting the age characteristics of clastic zircons during sedimentation of the original rocks. The U-Pb ages of the rims are from 203 ± 2 Ma to 190 ± 1 Ma, which represent the age of metamorphism. The zircon U-Pb ages of one sample taken from the leucogranite veins that cut through granitic gneiss foliation range from 24 Ma to 22 Ma, interpreted as the age of the anatexis in the Demala Group complex. Biotite and muscovite separates were selected from the granitic gneiss, banded gneiss, and leucogranite veins for 40Ar/39Ar dating. The plateau ages of three muscovite samples are 16.56 ± 0.21 Ma, 16.90 ± 0.21 Ma, and 23.40 ± 0.31 Ma, and the plateau ages of four biotite samples are 16.70 ± 0.24 Ma, 16.14 ± 0.19 Ma, 15.88 ± 0.20 Ma, and 14.39 ± 0.20 Ma. The mica Ar-Ar ages can reveal the exhumation and cooling history of the Demala Group complex. Combined with the previous research results of the Demala Group complex, the authors refer that the Demala Group complex should be a set of metamorphic complex. The complex includes not only Precambrian basement metamorphic rock series, but also Paleozoic sedimentary rock and Mesozoic granitic rock. Based on the deformation characteristics, the authors concluded that two stages of the metamorphism and deformation can be revealed in the Demala Group complex since the Mesozoic, namely Late Triassic-Early Jurassic (203 –190 Ma) and Oligocene –Miocene (24 –14 Ma). The early stage of metamorphism (ranging from 203 –190 Ma) was related to the Late Triassic tectono-magmatism in the area. The anatexis and uplifting-exhumation of the later stage (24 –14 Ma) were related to the shearing of the Jiali strike-slip fault zone. The Miocene structures are response to the large-scale southeastward escape of crustal materials and block rotation in Southeast Tibet after India-Eurasia collision. ©2021 China Geology Editorial Office.

Based on the atom-bond electronegativity equalization method fused into molecular mechanics (ABEEM/MM), two fluctuating charge models of OH--water system were proposed. The difference between these two models is whether there is charge transfer between OH- and its first-shell water molecules. The structures, charge distributions, charge transfer, and binding energies of the OH-(H2O)n (n = 1-8, 10, 15, 23) clusters were studied by these two ABEEM/MM models, the OPLS/AA force field, the OPLS-SMOOTH/AA force field, and the QM methods. The results demonstrate that two ABEEM/MM models can search out all stable structures just as the QM methods, and the structures and charge distributions agree well with those from the QM calculations. The structures, the charge transfer, and the strength of hydrogen bonds in the first hydration shell are closely related to the coordination number of OH-. Molecular dynamics simulations on the aqueous OH- solution are performed at 298 and 278 K using ABEEM/MM-I model. The MD results show that the populations of three-, four-, and five-coordinated OH- are 29.6%, 67.1%, and 3.4% at 298 K, respectively, and those of two-, three-, four-, and five-coordinated OH- are 10.8%, 44.9%, 39.2%, and 4.9% at 278 K, respectively; the average hydrogen bond lengths and the hydrogen bond angle in the first shell increase with the temperature decreasing.

Controlling proliferation and aflatoxin production by Aspergillus flavus is a pressing challenge for global food safety and security. Marine bacterium Staphylococcus saprophyticus strain L-38 showed excellent antifungal activity toward A. flavus in vitro and in vivo. In sealed, non-contact confrontation assays, L-38 completely inhibited conidial germination and mycelial growth of A. flavus through the production of volatile organic compounds (VOCs). Gas chromatography-mass spectrometry identified 3,3-dimethyl-1,2-epoxybutane (3-DE) as the most abundant VOC (32.61% of total peak area, 78% matching). Exposure of A. flavus cultures to synthetic 3-DE similarly demonstrated strong inhibition of growth. Moreover, culture of L-38 in a sealed chamber with maize or peanuts artificially inoculated with A. flavus, at high water activity, resulted in significant inhibition of A. flavus germination and aflatoxin biosynthesis. Scanning electron microscopy of these samples revealed severe damage to conidial cells and hyphae compared to samples not exposed to L-38. L-38 also showed broad and effective antifungal activity toward eight other phytopathogenic fungi including Aspergillus niger, Fusarium verticillioides, Fusarium graminearum, Sclerotinia sclerotiorum, Rhizoctonia solani, Alternaria alternata, Monilinia fructicola, and Botrytis cinerea. This work introduces S. saprophyticus L-38 as a potential biocontrol agent and demonstrates the efficacy of the volatile 3-DE in the control of A. flavus and other destructive plant pathogens for post-harvest food safety.

Endoscopic submucosal dissection (ESD) has been widely used in the treatment of early gastric cancer (EGC). A personalized and effective prediction method for ESD with EGC is urgently needed.To construct a risk prediction model for ulcers after ESD for EGC based on LASSO regression.A total of 196 patients with EGC who received ESD treatment were prospectively selected as the research subjects and followed up for one month. They were divided into an ulcer group and a non-ulcer group according to whether ulcers occurred. The general data, pathology, and endoscopic characteristics of the groups were compared, and the best risk predictor subsets were screened by LASSO regression and tenfold cross-validation. Multivariate logistic regression was applied to analyze the risk factors for ulcers after ESD in patients with EGC. A receiver operating characteristic (ROC) curve was used to estimate the predictive model performance.One month after the operation, no patient was lost to follow-up. The incidence of ulcers was 20.41% (40/196) (ulcer group), and the incidence of no ulcers was 79.59% (156/196) (non-ulcer group). There were statistically significant differences in the course of disease,Clopidogrel medication history, lesion diameter, convergent folds, and mucosal discoloration can predict the occurrence of ulcers after ESD in patients with EGC.

Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks which leads to catastrophic forgetting. To ameliorate this problem, we propose to only activate and select sparse neurons for learning current and past tasks at any stage. More parameters space and model capacity can thus be reserved for the future tasks. This minimizes the interference between parameters for different tasks. To do so, we propose a Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers. Full Experience Replay (FER) provides effective supervision in learning the sparse activations of the neurons in different layers. A loss-aware reservoir-sampling strategy is developed to maintain the memory buffer. The proposed method is agnostic as to the network structures and the task boundaries. Experiments on different datasets show that our approach achieves state-of-the-art performance for mitigating forgetting.

Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years. Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data. This work further explores this direction by proposing to couple SlfSL with SemSL. Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target. Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes. By implementing this idea through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Estimation (CRAE). Specifically, CRAE is featured by adopting a module which predicts the image rotation angle conditioned on the candidate image class. Through experimental evaluation, we show that CRAE achieves superior performance over the other existing ways of combining SlfSL and SemSL. To further boost CRAE, we propose two extensions to strengthen the coupling between SemSL target and SlfSL target in basic CRAE. We show that this leads to an improved CRAE method which can achieve the state-of-the-art SemSL performance.

Aspergillus flavus and the produced aflatoxins cause great hazards to food security and human health across all countries. The control of A. flavus and aflatoxins in grains during storage is of great significance to humans. In the current study, bacteria strain YM6 isolated from sea sediment was demonstrated effective in controlling A. flavus by the production of anti-fungal volatiles. According to morphological characteristics and phylogenetic analysis, strain YM6 was identified as Pseudomonas stutzeri. YM6 can produce abundant volatile compounds which could inhibit mycelial growth and conidial germination of A. flavus. Moreover, it greatly prevented fungal infection and aflatoxin production on maize and peanuts during storage. The inhibition rate was 100%. Scanning electron microscopy further supported that the volatiles could destroy the cell structure of A. flavus and prevent conidia germination on the grain surface. Gas chromatography/mass spectrometry revealed that dimethyl trisulfide (DMTS) with a relative abundance of 13% is the most abundant fraction in the volatiles from strain YM6. The minimal inhibitory concentration of DMTS to A. flavus conidia is 200 µL/L (compound volume/airspace volume). Thus, we concluded that Pseudomonas stutzeri YM6 and the produced DMTS showed great inhibition to A. flavus, which could be considered as effective biocontrol agents in further application.

As the important component of the eastern Tethys tectonic region, the Jinsha River-Ailao Mountain suture zone is often considered to be an ophiolitic melange belt. However, the P-T-t path and chronological framework of the metamorphic evolution in the collisional orogenetic process of this zone are still poorly understood owing to the lack of metamorphism research of symbolic high-pressure rocks. During a regional geological survey on a scale of 1 : 50000 in Gonjo County, Tibet Autonomous Region involved in this paper, (retrograde) eclogites lenses of different scales were found in Jinsha River suture zone, eastern Tibet for the first time. The (retrograde) eclogites can be divided into garnet-albite-chlorite-actinolite schists and eclogites according to retrograde degrees. The mainly mineral components of eclogites include garnet (45%–50%), clinopyroxene (about 25%), and hornblende (5%–10%) primarily, and biotite, quartz, rutile, and muscovite secondarily. According to the data of electron probe micro analysis (EPMA), clinopyroxenes feature high content of Na2O (5.6%–6%) and corresponding jadeite (Jd) molecules of 37%–44%, and they fall within the omphacite region in Quad-Jd-Ae diagram. The temperature and pressure of the metamorphism at peak are P≈2.2–2.34 GPa and T≈622–688 °C respectively as measured with geobarometry Grt-Omp-Phe and geothermometer Grt-Omp. This will provide a new reference for the understanding of Paleo-Tethyan evolution. In this paper, two samples of eclogites were chosen for LA-ICP-MS zircon U-Pb dating and their 206Pb/238U ages obtained are 240 ± 3 Ma and 244 ± 1 Ma respectively. Furthermore, the zircons feature extremely low Th/U ratio (

Endogenous substance such as nucleotide as a drug carrier has been proposed as a novel drug delivery system. The nucleotide guanosine-5′-monophosphate (GMP) is used to transport an anticancer drug pemetrexed disodium heptahydrate (PMX) via specific base pairing. The endogenous nature of GMP helps to avoid biocompatibility issues that are generally accompanied with nanocarriers including cytotoxicity, immunogenicity and blood compatibility. Furthermore, the low-molecular weight of the GMP nucleotide carrier significantly boosts the drug loading capacity compared to traditional liposomes and high-molecular weight carriers. Hydrogen-bonding interaction between the carrier and drug realizes the controlled release of loaded drug, and also facilitates large scale manufacture since no additional chemical synthesis is required. More importantly, in vivo experiments reveal that the base-paired GMP:PMX nanovesicles improve the target specificity and pharmacokinetic properties of PMX, and exhibit remarkably enhanced anticancer abilities compared to standalone PMX without any carriers. We envision that this strategy could be extended to other endogenous substances and drugs bearing functional groups capable of specific interaction, and promote the construction of drug delivery systems with inherent biocompatibility, enhanced drug delivery efficacy, and a simplified preparation method.

Fumonisin B1 (FB1) contamination in cereals and cereal products remains an important aspect of food safety because of its wide distribution and the potential health hazard. However, only a few microorganisms have been reported to effectively degrade FB1. In this present study, a bacterial consortium SAAS79 with highly FB1-degrading activity was isolated from the spent mushroom compost. The combination of antibiotic-driven selection and 16S rDNA sequencing identified the Pseudomonas genus as the key FB1-degrading member. The microbial consortium could degrade more than 90% of 10 μg/mL FB1 after incubation for 24 h at pH of 5–7 and temperature of 28–35 °C. The enzymes from the intracellular space were proved to be responsible for FB1 degradation, which eliminated about 90% of 10 μg/mL FB1 in 3 h. Besides, liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) analysis identified two degradation products of FB1, and their toxicity on the monkey kidney cells (MARC-145) was significantly lower (p

Traditional image quality assessment (IQA) methods do not perform robustly due to the shallow hand-designed features. It has been demonstrated that deep neural network can learn more effective features than ever. In this paper, we describe a new deep neural network to predict the image quality accurately without relying on the reference image. To learn more effective feature representations for non-reference IQA, we propose a two-stream convolution network that includes two subcomponents for image and gradient image. The motivation for this design is using a two-stream scheme to capture different-level information of inputs and easing the difficulty of extracting features from one steam. The gradient stream focuses on extracting structure features in details, and the image stream pays more attention to the information in intensity. In addition, to consider the locally non-uniform distribution of distortion in images, we add a region-based fully convolutional layer for using the information around the center of the input image patch. The final score of the overall image is calculated by averaging of the patch scores. The proposed network performs in an end-to-end manner in both the training and testing phases. The experimental results on a series of benchmark datasets, e.g., LIVE, CISQ, IVC, TID2013, and Waterloo Exploration Database, show that the proposed algorithm outperforms the state-of-the-art methods, which verifies the effectiveness of our network architecture.

Objective: It has been well established that sumoylation acts as an important regulatory mechanism that controls many different cellular processes. We and others have shown that sumoylation plays an indispensable role during mouse eye development. Whether sumoylation is implicated in ocular pathogenesis remains to be further studied. In the present study, we have examined the expression patterns of the de-sumoylation enzymes (SENPs) in the in vitro cataract models induced by glucose oxidase and UVA irradiation. Methods: Four-week-old C57BL/6J mice were used in our experiments. Lenses were carefully dissected out from mouse eyes and cultured in M199 medium for 12 hours. Transparent lenses (without surgical damage) were selected for experimentation. The lenses were exposed to UVA for 60 min or treated with 20 mU/mL glucose oxidase (GO) to induce cataract formation. The mRNA levels were analyzed with qRT-PCR. The protein levels were determined with western blot analysis and quantitated with Image J. Results: GO treatment and UVA irradiation can induce cataract formation in lens cultured in vitro. GO treatment significantly down-regulated the mRNA levels for SENPs from 50% to 85%; on the other hand, expression of seven SENP proteins under GO treatment appeared in 3 situations: upregulation for SENP1, 2 and 6; downregulation for SENP 5 and 8; and unchanged for SENP3 and 7. UVA irradiation upregulates the mRNAs for all seven SENPs; In contrast to the mRNA levels for 7 SENPs, the expression levels for 6 SENPs (SENP1-3, 5-6 and 8) appeared down-regulated from 10% to 50%, and only SENP7 was slightly upregulated. Conclusion: Our results for the first time established the differentiation expression patterns of 7 de-sumoylation enzymes (SENPs) under treatment by GO or UVA, which provide preliminary data to link sumoylation to stress-induced cataractogenesis.

Previous studies have shown that ghrelin, a peptide produced in the stomach, attenuates acute lung injury (ALI) in various animal models, and that some of these effects are associated with inhibition of the nuclear factor κB signaling pathway. This study investigated whether ghrelin exerts beneficial effects on hemorrhagic shock (HS)-induced ALI by modulating nuclear factor κB inhibitor kinase/nuclear factor κB inhibitor/nuclear factor κB (IKK/IκBα/NF-κB) pathway activity. HS was induced in male SD rats by withdrawing blood to a mean arterial pressure (MAP) of 40 mm Hg for 1 h; rats then received ghrelin (10 nmol/kg) or vehicle intravenously and were resuscitated with the shed blood and an equal volume of Ringer lactate solution followed by observation for 2 h. After resuscitation, samples were collected and analyzed for lung histopathology, wet to dry weight ratio (W/D), bronchoalveolar lavage fluid (BALF) protein, neutrophil infiltration, plasma inflammatory cytokines (TNF-α and IL-6), and cytoplasmic phosphorylated IKKβ, IκBα, phosphorylated IκBα and nuclear NF-κB expression. Compared to those in the two sham groups, lung injury, W/D, BALF protein, neutrophil infiltration, plasma TNF-α and IL-6 levels, and IKK/IκBα/NF-κB pathway activation were significantly increased in HS rats. After ghrelin administration, all parameters analyzed were decreased compared to those without ghrelin in HS rats. Moreover, ghrelin alleviated the decreased MAP after resuscitation compared to that in HS rats. Exogenous ghrelin attenuates the inflammatory response and acute lung injury after HS. These beneficial effects appear to be mediated through inhibition of IKK/IκBα/NF-κB signaling.

Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (\ie, the ability to fit a wide range of signals with diverse structures) and adaptability (\ie, being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this study, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements. To maximize the adaptability, we also propose a new sparse signal estimation where the sparse signals, support, noise and signal parameter estimation are unified into a variational optimization problem, which can be effectively solved with an alternative minimization scheme. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime., Comment: 13 pages, submitted to IEEE Transactions on Image Processing

PURPOSE Accumulated evidence have well established that protein sumoylation plays multiple roles in various cellular processes. In the vertebrate eye, we and others have demonstrated that sumoylation displays indispensable roles in regulating eye development. Various ocular cell lines including human embryonic cell line (FHL124), the SV40-large T-transformed human lens epithelial cell line (HLE), the SV40-large T-transformed mouse lens epithelial cell line (αTN4-1), the rabbit lens epithelial cell line (N/N1003A) and the human retina pigment epithelial cell line (ARPE-19) have been extensively used for studying various cellular functions and disease processes including sumoylation functions, and mechanisms for cataract and age-related macular degeneration (AMD). However, the sumoylation enzyme systems have not been well established. METHODS FHL124, HLE, αTN4-1, N/N1003A and ARPE-19 were cultured in Dulbecco's modified eagle medium (DMEM) containing 10% FBS and 1% penicillin & streptomycin. The expression levels of seven SENP mRNAs were analyzed with qRT-PCR, and the expression levels of seven SENP proteins were detected with Western blot analysis. RESULTS Using both qRT-PCR and Western blot analysis, we have obtained the followings: 1). The 3 human ocular cell lines, FHL124, HLE and ARPE-19 express all types of SENP mRNA and proteins. 2). In mouse lens epithelial cell line αTN4-1, and rabbit lens epithelial cells line N/N1003A, however, only the mRNAs for SENP1, 2, 3, 6 and 7 are expressed. At the protein level, SENP8 was absent in both αTN4-1 and N/N1003A cells; 3). Each cell line has different dominant SENP enzymes. For FHL124, SENP3, 5, 7 and 8 proteins are relatively dominant. SENP3, 5 and 6 are the major de-sumoylation enzymes in HLE cells. Different from human lens epithelial cells, FHL124 and HLE, human retina pigment epithelial cells (ARPE-19) have SENP3, 7, and 8 as the dominant forms of de-sumoylation enzymes. For mouse lens epithelial cells, SENP1, 3 and 7 are the major de-sumoylation enzymes. On the other hand, the rabbit lens epithelial cells have SENP1, 2 and 7 as the major isoforms. CONCLUSION Our results for the first time defined the differential expression patterns of the seven types of de-sumoylation enzymes (SENPs) in 5 major ocular cell lines. These results help to establish the basis for the future study of sumoylation functions and the related mechanisms in vertebrate eye.

RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods.

Based on the atom-bond electronegativity equalization method fused into molecular mechanics (ABEEM/MM), two fluctuating charge models of OH

Purpose Protein Sumoylation is one of the most important and prevalent posttranscriptional modification. Increasing evidence have shown that the SENPs (sentrin/SUMOspecific proteases) are critical for steady-state levels of SUMO modification of target proteins, and protein de-sumoylation modulates a great diversity of biological processes including transcription, development, differentiation, neuroprotection, as well as pathogenesis. In the vertebrate eye, we and others have previously shown that sumoylation participated in the differentiation of major ocular tissues including retina and lens. However, the biological significance of seven SENP enzymes: SENP1 to 3 and SENP5 to 8 have not be fully investigated in the ocular tissues. Methods The 5 major ocular cell lines were cultured in Dulbecco's modified Eagle's medium (DMEM) containing fetal bovine serum (FBS) or rabbit serum (RBS) and 1% Penicillin- Streptomycin. The mRNA levels were analysed with qRT-PCR. The protein levels were determined with western blot analysis and quantitated with Image J. Results At the mRNA level, all SENPs were highly expressed in retina, and much reduced expression patterns in cornea, lens epithelium and lens fiber. At the protein level, SENP1 to -3, and SENP6 were highly abundant in cornea, while SENP5, SENP7 and SENP8 were enriched in retina, and these SENPs were relatively less abundant in lens tissues. Conclusion Our results for the first time established the differentiation expression patterns of the 7 de-sumoylation enzymes (SENPs), which provides a basis for further investigation of protein desumoylation functions in vertebrate eye.

Purpose It is now well established that protein sumoylation acts as an important regulatory mechanism modulating functions over three thousand proteins. In the vision system, protein conjugation with SUMO peptides can regulate differentiation of multiple ocular tissues. Such regulation is often explored through analysis of biochemical and physiological changes with various cell lines in vitro. We have recently analyzed the expression levels of both mRNAs and proteins for seven de-sumoylation enzymes (SENPs) in five major ocular cell lines. In continuing the previous study, here we have determined their cellular localization of the seven de-sumoylation enzymes (SENP1, 2, 3, 5, 6, 7 and 8) in the above 5 major ocular cell lines using immunocytochemistry. Methods The 5 major ocular cell lines were cultured in Dulbecco's modified Eagle's medium (DMEM) containing fetal bovine serum (FBS) or rabbit serum (RBS) and 1% Penicillin- Streptomycin. The localization of the 7 major de-sumoylation enzymes (SENPs) in the 5 major ocular cell lines were determined with immunohistochemistry. The images were captured with a Zeiss LSM 880 confocal microscope. Results 1) The SENP1 was localized in both cytoplasm and nucleus of 3 human ocular cell lines, FHL124, HLE and ARPE-19; In N/N1003A and αTN4-1, SENP 1 was more concentrated in the cytoplasm. SENP1 appears in patches; 2) SENP2 was distributed in both cytoplasm and nucleus of all ocular cell lines in patches. In HLE and ARPE-19 cells, SENP2 level was higher in nucleus than in cytoplasm; 3) SENP3 was almost exclusively concentrated in the nuclei in all ocular cells except for N/N1003A cells. In the later cells, a substantial amount of SENP3 was also detected in the cytoplasm although nuclear SENP3 level was higher than the cytoplasmic SENP3 level. SENP3 appeared in obvious patches in the nuclei; 4) SENP5 was dominantly localized in the cytoplasm (cellular organelles) near nuclear membrane or cytoplasmic membrane ; 5) SENP6 was largely concentrated in the nuclei of all cell lines except for αTN4-1 cells. In the later cells, a substantial amount of SENP6 was also detected in the cytoplasm although nuclear SENP6 level was higher than the cytoplasmic SENP6 level. 6) SENP7 has an opposite localization pattern between human and animal cell lines. In human cell lines, a majority of SENP7 was localized in nuclei whereas in mouse and rabbit lens epithelial cells, most SENP7 was distributed in the cytoplasm. SENP8 was found present in human cell lines. The 3 human ocular cell lines had relatively similar distribution pattern. In FHL124 and ARPE-19 cells, SENP8 was detected only in the cytoplasm, but in HLE cells, patches of SENP8 in small amount was also detected in the nuclei. Conclusions Our results for the first time defined the differential distribution patterns of seven desumoylation enzymes (SENPs) in 5 major ocular cell lines. These results help to understand the different functions of various SENPs in maintaining the homeostasis of protein sumoylation patterns during their functioning processes.

Background It is now well established that protein sumoylation is an important mechanism to regulate multiple cellular processes including gene transcription, chromatin structure, cell proliferation and differentiation, as well as pathogenesis. Objective In the vertebrate eye, we and others have previously shown that sumoylation can regulate differentiation of major ocular tissues including retina and lens. However, the expression patterns of the three types of sumoylation enzymes, the activating enzymes SAE1 and UBA2, the conjugating enzyme UBC9, and the ligating enzymes such as RanBP2 and PIAS1 have not been well studied in the ocular tissues. Conclusion In the present study, using QRT-PCR and western blot analysis, we have determined the differentiatial expression patterns of the above three types of enzymes, and the obtained results lay down a foundation for further exploration of sumoylation functions in vertebrate eye.

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed, or learned from examples. Existing learning based methods have shown superior restoration quality but are not practical enough due to their restricted and static model design. They solely focus on learning a prior and require to know the noise level for deconvolution. We address the gap between the optimization-based and learning-based approaches by learning a universal gradient descent optimizer. We propose a Recurrent Gradient Descent Network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme. A hyper-parameter-free update unit shared across steps is used to generate updates from the current estimates, based on a convolutional neural network. By training on diverse examples, the Recurrent Gradient Descent Network learns an implicit image prior and a universal update rule through recursive supervision. The learned optimizer can be repeatedly used to improve the quality of diverse degenerated observations. The proposed method possesses strong interpretability and high generalization. Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

Sumoylation is one of the key regulatory mechanisms in eukaryotes. Our previous studies reveal that sumoylation plays indispensable roles during lens differentiation (Yan et al. 2010. Proc Natl Acad Sci USA. 107:21034–21039; Gong et al. 2014. Proc Natl Acad Sci USA. 111:5574–5579). Whether sumoylation is implicated in cataractogenesis, a disease largely derived from aging, remains elusive. In the present study, we have examined the changing patterns of the sumoylation ligases and de‐sumoylation enzymes (SENPs) and their substrates including Pax6 and other proteins in cataractous lenses of different age groups from 50 to 90 years old. It is found that compared with normal lenses, sumoylation ligases 1 and 3, de‐sumoylation enzymes SENP3/7/8, and p46 Pax6 are clearly increased. In contrast, Ubc9 is significantly decreased. Among different cataract patients from 50s to 70s, male patients express more sumoylation enzymes and p46 Pax6. Ubc9 and SENP6 display age‐dependent increase. The p46 Pax6 displays age‐dependent decrease in normal lens, remains relatively stable in senile cataracts but becomes di‐sumoylated in complicated cataracts. In contrast, sumoylation of p32 Pax6 is observed in senile cataracts and increases its stability. Treatment of rat lenses with oxidative stress increases Pax6 expression without sumoylation but promotes apoptosis. Thus, our results show that the changing patterns in Ubc9, SENP6, and Pax6 levels can act as molecular markers for senile cataract and the di‐sumoylated p46 Pax6 for complicated cataract. Together, our results reveal the presence of molecular signature for both senile and complicated cataracts. Moreover, our study indicates that sumoylation is implicated in control of aging and cataractogenesis., The molecular signatures for both senile cataract and complicated cataract. Upregulation of sumoylation ligases Ubc9 and PIAS1, and de‐sumoylation enzyme SENP6, and SUMO1 conjugation of p32 Pax6 are molecular markers for senile cataract. Di‐sumoylation of p46 Pax6 is the molecular marker for complicated cataract.

According to the fact that 5-nitro-3-trinitromethyl-1H-1,2,4 triazole (NTNMT) is a successful, good explosive, energetic groups such as -CH

Obtaining a high dynamic range (HDR) image from multiple low dynamic range images with different exposures is an important step in various computer vision tasks. One of the ongoing challenges in the field is to generate HDR images without ghosting artifacts. Motivated by an observation that such artifacts are particularly noticeable in the gradient domain, in this paper, we propose an HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain. The proposed method generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain. The context information in a large area helps to reconstruct the contents contaminated by saturation and misalignments. Specifically, an additional gradient stream and the supervision in the gradient domain are applied to incorporate the gradient information in HDR imaging. To use the context information captured from a large area while preserving spatial resolution, we adopt dilated convolutions to extract multi-scale features with rich context information. Moreover, we build a new dataset containing 40 groups of real-world images from diverse scenes with ground truth to validate the proposed model. The samples in the proposed dataset include more challenging moving objects inducing misalignments. Extensive experimental results demonstrate that our proposed model outperforms previous methods on different datasets in terms of both quantitative measure and visual perception quality.

The widespread existence of potentially toxic heavy elements have become one of the serious environmental concerns around the globe. Thus, it is highly important to develop efficient and selective chemical sensors to detect such potentially toxic elements. Herein, we developed a rhodamine-2-amino-5-bromopyrimidine based chemosensor (RBP) for the selective detection of toxic elements using Hg2+ and Cr3+ as model elements in combination with other competitive ions such as Al3+, Ag+, Ca2+, Ba2+, Cd2+, Fe2+, Fe3+, Li+, K+, Mn2+, Mg2+, Cu2+, Na+, Ni2+, Pb2+and Zn2+. The newly developed RBP displayed good visibility, high sensitivity, excellent selectivity, high binding ability and low limit of detection in CH3CN/HEPES buffer (1 mM 3:2 v/v, pH 7.3) as a medium. The binding constants of RBP for Hg2+ and Cr3+ were 1.09 × 107 M−1 and 8.3 × 104 M−1, respectively. As compared to earlier reported studies, the lowest detection limits (LOD), i.e., 1.6 μM for Hg2+ and 4.9 μM for Cr3+ were recorded. RBP also show reversible binding affinity with Hg2+ and Cr3+ in the presence of ethylenediaminetetraacetate (EDTA). The binding mode between metal ions and RBP were further investigated by using density functional theory (DFT) calculations, which support the experimental findings very well. More importantly, RBP can be prepared as a test paper kit to detect the concentration of Hg2+ and Cr3+ ions by changing the color of the paper visible to naked eyes and is potential for the practical infield application. In addition, the results obtained from confocal microscopy revealed that the probe is cell permeable with low cytotoxicity and can be employed as a bio-imaging reagent for intracellular recognition of Hg2+ and Cr3+ ratification in human breast cancer cells MCF-7.

Blind image deblurring aims to recover the sharp image from a blurred observation, which is an ill-posed inverse problem. Proper image priors for the unknown variables (i.e. latent sharp image and blur kernel) are crucial. Abundant previous methods have shown the effectiveness of the sparsity-based priors on both image gradients and the blur kernel. The correlation among the elements of the sparse variables is paid less attention, however. In this paper, we propose to handle the blind image deblurring problem by promoting group sparsity. The proposed group sparsity priors are based on the fact that the nonzero elements of natural image gradients and blur kernels tend to cluster in structured group pattern. Based on the proposed priors, we introduce proper algorithms to iteratively update latent image gradients and blur kernel, respectively. The proposed algorithms preserve the salient structures and smooth the minor components in image gradients and restrict the blur kernel in a domain of dynamic group sparse vector. To illustrate the reliability of the proposed algorithm, we conduct experiments to analyze the properties of the regularizers and the convergence property of the proposed algorithm. Experiments with both quantitative and visual comparison further prove the effectiveness of the proposed method.

Tetra-amido macrocyclic ligand (TAML) activator is a functional analog of peroxidase enzymes, which activates hydrogen peroxide (H2O2) to form high valence iron-oxo complexes that selectively degrade persistent aromatic organic contaminants (ACs) in water. Here, we develop quantitative structure–activity relationship (QSAR) models based on measured pseudo first-order kinetic rate coefficients ( k obs ) of 29 ACs (e.g., phenols and pharmaceuticals) oxidized by TAML/H2O2 at neutral and basic pH values to gain mechanistic insight on the selectivity and pH dependence of TAML/H2O2 systems. These QSAR models infer that electron donating ability ( E HOMO ) is the most important AC characteristic for TAML/H2O2 oxidation, pointing to a rate-limiting single-electron transfer (SET) mechanism. Oxidation rates at pH 7 also depend on AC reactive indices such as f m i n − and qH+, which respectively represent propensity for electrophilic attack and the most positive net atomic charge on hydrogen atoms. At pH 10, TAML/H2O2 is more reactive towards ACs with a lower hydrogen to carbon atoms ratio (#H:C), suggesting the significance of hydrogen atom abstraction. In addition, ln k obs of 14 monosubstituted phenols is negatively correlated with Hammett constants (σ) and exhibits similar sensitivity to substituent effects as horseradish peroxidase. Although accurately predicting degradation rates of specific ACs in complex wastewater matrices could be difficult, these QSAR models are statistically robust and help predict both relative degradability and reaction mechanism for TAML/H2O2-based treatment processes.

Herein, we report a new rhodamine-based multianalyte (Cu2+ and Hg2+) fluorescent sensor RH-FP. The synthesized probe acts as a dual chemosensor for recognition of Cu2+ and Hg2+ in acetonitrile-water (4:1 v/v, pH 7.2 with 10 mM, HEPES) in the presence of other miscellaneous metal cations i.e. Ag+, Al3+, Ba2+, Ca2+, Cd2+, Fe3+, Fe2+, K+, Li+, Mg2+, Mn2+, Cu+2, Pb2+, Ni2+, Na+ and Zn2+. A highly selective and sensitive colorimetric (colorless to pink) response accompanied by a significant increase in absorption and emission maxima (250-folds) was observed by the interaction of RH-FP and Cu2+ or Hg2+. This makes RH-FP a suitable “bare-eye” detector. The Job’s plot and ESI–MS confirm 1:1 binding stoichiometry between sensor RH-FP and metal cations. The calculated binding constant for the complexes (RH-FP-Cu2+ and RH-FP-Hg2+) are 3.37 × 105 mol−1 and 7.6 × 105 mol−1, with a detection limit of 3.9 nm and 2.36 nm respectively. The binding mechanism was confirmed by Fourier transform infra-red spectroscopy (FT-IR) 1H, 13C NMR and density functional theory (DFT) calculations. Moreover, the fluorescent sensor RH-FP displayed potential in-field applications as detection kit and in fluorescent imaging of biological systems to detect Cu2+ or Hg2+.

We calculate the cross-correlation function $\langle (\Delta T/T)(\mathbf{v}\cdot \mathbf{n}/\sigma_{v}) \rangle$ between the kinetic Sunyaev-Zeldovich (kSZ) effect and the reconstructed peculiar velocity field using linear perturbation theory, to constrain the optical depth $\tau$ and peculiar velocity bias of central galaxies with Planck data. We vary the optical depth $\tau$ and the velocity bias function $b_{v}(k)=1+b(k/k_{0})^{n}$, and fit the model to the data, with and without varying the calibration parameter $y_{0}$ that controls the vertical shift of the correlation function. By constructing a likelihood function and constraining $\tau$, $b$ and $n$ parameters, we find that the quadratic power-law model of velocity bias $b_{v}(k)=1+b(k/k_{0})^{2}$ provides the best-fit to the data. The best-fit values are $\tau=(1.18 \pm 0.24) \times 10^{-4}$, $b=-0.84^{+0.16}_{-0.20}$ and $y_{0}=(12.39^{+3.65}_{-3.66})\times 10^{-9}$ ($68\%$ confidence level). The probability of $b>0$ is only $3.12 \times 10^{-8}$ for the parameter $b$, which clearly suggests a detection of scale-dependent velocity bias. The fitting results indicate that the large-scale ($k \leq 0.1\,h\,{\rm Mpc}^{-1}$) velocity bias is unity, while on small scales the bias tends to become negative. The value of $\tau$ is consistent with the stellar mass--halo mass and optical depth relation proposed in the previous literatures, and the negative velocity bias on small scales is consistent with the peak background-split theory. Our method provides a direct tool to study the gaseous and kinematic properties of galaxies., Comment: 13 pages, 13 figures, 3 tables

Current image editing algorithms prevailingly involve specific processing intensities. After the network training phase, the definite mapping is carried on testing images, which leads to under- or over-editing in certain cases. However, for many real problems, having access to diverse intensities of processing of the output is preferable. The hypothesis is that the part of parameters that correspond to the intensity of processing of the network are implicitly embedded in the whole parameter set and can be explicitly extracted and utilized, even with limited supervision. In this paper, we introduce the concept of Intensity of Processing, termed as IP, and propose image editing with Varying Intensities of Processing, termed as VIP. Given the input and corresponding fully-edited output, we would like to not only figure out the particular mapping from input to output, but also be able to obtain multiple continuous intermediate states with in-between intensities of processing. Refer to the network that deals with the original image editing operation as Base Network. By adding an extra module, which we call Condition Encoder, to condition and regulate the Base Network, we make the whole image editing process controllable. Moreover, we develop a triplet loss and construct the sequentiality of generated intermediate states with limited supervision. A significant advantage of our method is that it does not require groundtruth images of the middle states during training, which substantially relaxes the range of applicable problems. Extensive experiments demonstrate the effectiveness and potentiality of the proposed framework; discussion and future work are also provided.

Weakly-supervised semantic segmentation, as a promising solution to alleviate the burden of collecting per-pixel annotations, aims to train a segmentation model from partial weak annotations. Scribble on the object is one of the commonly used weak annotations and has shown to be sufficient for learning a decent segmentation model. Despite being effective, scribble-based weakly-supervised learning methods often lead to imprecise segmentation on object boundaries. This is mainly because the scribble annotations usually locate inside the objects and the dataset lacks annotations close to the semantic boundaries. To alleviate this issue, this paper proposes a simple-but-effective solution, i.e., BoundaryMix, which generates pseudo training image-annotation pairs from the original images to supplement the missing semantic boundaries. Specifically, given a prediction of segmentation, we cut off the regions around the estimated boundaries, which are error-prone and replace them with the contents from another image, which in effect creates new samples with less ambiguity around semantic boundaries. With training on scribbles and the on-the-fly generated pseudo annotations, the network acquires better prediction capability around the boundary region and thus improves the overall segmentation performance. By conducting experiments on PASCAL VOC 2012 dataset and POTSDAM dataset with only scribble annotations, we demonstrate the excellent performance of the proposed method and the almost closed gap between scribble-supervised and fully-supervised image segmentation.

The structural polymorphism of the oxygen-evolving complex is of great significance to photosynthetic water oxidation. Employing density functional theory calculations, we have made further advisement on the interconversion mechanism of O5 transfer in the S2 state, mainly focusing on the potentiality of multi-state reactivity and spin transitions. Then, O5 protonation is proven impossible in S2 for irreversibility of the interconversion, which serves as an auxiliary judgment for the protonation state of O5 in S1. Besides, the structural polymorphism could also be archived by alternative mechanisms involving Mn3 ligand exchange, one of which with Mn3(III) makes sense to substrate water exchange in S2, although being irresponsible for the derivations of the observed EPR signals. During the water exchange, high-spin states would prevail to facilitate electron transfer between the ferromagnetically coupled Mn centers. In addition, water exchange in S1 could account for the closed-cubane structure as the initial form entering S2 at cryogenic temperatures. With regard to water oxidation, the structural flexibility and variability in both S2 and S3 guarantee smooth W2-O5 coupling in S4, according to the substrate assignments from water exchange kinetics. Within this theoretical framework, the new XFEL findings on S1-S3 can be readily rationalized. Finally, an alternative mechanistic scenario for O O bond formation with ·OH radical near O4 is presented, followed by water binding to the pivot Mn4(III) from O4 side during S4-S0. This may diversify the substrate sources combined with the Ca channel in water delivery for the forthcoming S-cycle.

Boron nitride nanoparticles were prepared in a muffle furnace at 1000 °C by using commercial available boron nitride flakes and sodium chloride as raw materials. The structure, functional groups, morphology and specific surface area of the as-prepared samples were characterized by X-ray diffracmeter, fourier-transform infrared spectrometer, transmission electron microscope and accelerated surface area/porosimetry system, respectively. In order to evaluate the properties of the as-prepared products, they were mixed with epoxy resin to fabricate polymer-based composites. Experimental results showed that the average size and specific surface area of the as-prepared nanoparticles were about 20 nm and 895.7 m2/g. Moreover, the as-prepared nanoparticles could improve the tensile strength and elongation at break of the epoxy matrix. Both of them were first increased and then decreased with increasing the usage amount of the as-prepared nanoparticles. When the usage amount of the boron nitride nanoparticles was 0.4 wt%, the tensile strength and elongation at break of the composites reached to 71.4 MPa and 22.1%, which were the maximum values of the composites, respectivley. They were increased by 137 and 146% more than those of pure epoxy resin. In addition, the as-prepared fillers could accelerate the solidification of the matrix.