Investigating the Corrosion Resistance of Different SiC Crystal Types: From Energy Sectors to Advanced Applications.

Silicon carbide, as a third-generation semiconductor material, plays a pivotal role in various advanced technological applications. Its exceptional stability under extreme conditions has garnered a significant amount of attention. These superior characteristics make silicon carbide an ideal candidate material for high-frequency, high-power electronic devices and applications in harsh environments. In particular, corrosion resistance in natural or artificially acidic and alkaline environments limits the practical application of many other materials. In fields such as chemical engineering, energy conversion, and environmental engineering, materials often face severe chemical erosion, necessitating materials with excellent chemical stability as foundational materials, carriers, or reaction media. Silicon carbide exhibits outstanding performance under these conditions, demonstrating significant resistance to corrosive substances such as hydrochloric acid, sulfuric acid, nitric acid, and alkaline substances such as potassium hydroxide and sodium hydroxide. Despite the well-known chemical stability of silicon carbide, the stability conditions of its different types (such as 3C-, 4H-, and 6H-SiC polycrystals) in acidic and alkaline environments, as well as the specific corrosion mechanisms and differences, warrant further investigation. This Review not only delves deeply into the detailed studies related to this topic but also highlights the current applications of different silicon carbide polycrystals in chemical reaction systems, energy conversion equipment, and recycling processes. Through a comprehensive analysis, this Review aims to bridge research gaps, offering a comparative analysis of the advantages and disadvantages between different polymorphs. It provides material scientists, engineers, and developers with a thorough understanding of silicon carbide's behavior in various chemical environments. This work will propel the research and development of silicon carbide materials under extreme conditions, especially in areas where chemical stability is crucial for device performance and durability. It lays a solid foundation for ultra-high-power, high-integration, high-reliability module architectures, supercomputing chips, and highly safe long-life batteries.

Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation.

3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and treatment. Accurate segmentation results are critical for localization and spatial distribution of brain tumors using 3D MRI. However, most existing methods mainly focus on extracting global semantic features from the spatial and depth dimensions of a 3D volume, while ignoring voxel information, inter-layer connections, and detailed features. A 3D brain tumor segmentation network SDV-TUNet (Sparse Dynamic Volume TransUNet) based on an encoder-decoder architecture is proposed to achieve accurate segmentation by effectively combining voxel information, inter-layer feature connections, and intra-axis information. Volumetric data is fed into a 3D network consisting of extended depth modeling for dense prediction by using two modules: sparse dynamic (SD) encoder-decoder module and multi-level edge feature fusion (MEFF) module. The SD encoder-decoder module is utilized to extract global spatial semantic features for brain tumor segmentation, which employs multi-head self-attention and sparse dynamic adaptive fusion in a 3D extended shifted window strategy. In the encoding stage, dynamic perception of regional connections and multi-axis information interactions are realized through local tight correlations and long-range sparse correlations. The MEFF module achieves the fusion of multi-level local edge information in a layer-by-layer incremental manner and connects the fusion to the decoder module through skip connections to enhance the propagation ability of spatial edge information. The proposed method is applied to the BraTS2020 and BraTS2021 benchmarks, and the experimental results show its superior performance compared with state-of-the-art brain tumor segmentation methods. The source codes of the proposed method are available at https://github.com/SunMengw/SDV-TUNet.

Transcriptomic analysis of Vibrio alginolyticus challenged by Rhizoma coptidis reveals mechanisms of virulence genes.

Rhizoma coptidis, a Chinese herbal medicine widely used to treat various bacterial infections, has the potential to develop antibiotic substitutes to overcome the drug resistance of Vibrio alginolyticus. To study the inhibitory effect of R. coptidis on V. alginolyticus, we sequenced the transcriptomes of three groups of samples of wild-type V. alginolyticus (CK) and V. alginolyticus, which were stressed by 5 mg/mL R. coptidis for 2 h (RC_2 h) and 4 h (RC_4 h). CK was compared with RC_2 h and RC_4 h, respectively, and a total of 1565 differentially expressed genes (DEGs) (988 up-regulated and 577 down-regulated) and 1737 DEGs (1152 up-regulated and 585 down-regulated) were identified. Comparing RC_2 h with RC_4 h, 156 DEGs (114 up-regulated and 42 down-regulated) were identified. The ability of biofilm formation and motility of V. alginolyticus altered upon with different concentrations of R. coptidis. Interestingly, relative expression patterns of virulence genes appeared statistically significantly varied, upon different concentrations of R. coptidis extract. DEGs were annotated to the Gene Ontology (GO) database for function enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the results showed that the main enriched pathways, was those related to the virulence of V. alginolyticus. This study provides a new perspective for understanding the complex pathogenic mechanism of V. alginolyticus. R. coptidis could potnetially be used as alternative or complimnetary to antibiotics to treat infections after further research.

Driver distraction detection based on lightweight networks and tiny object detection.

Real-time and efficient driver distraction detection is of great importance for road traffic safety and assisted driving. The design of a real-time lightweight model is crucial for in-vehicle edge devices that have limited computational resources. However, most existing approaches focus on lighter and more efficient architectures, ignoring the cost of losing tiny target detection performance that comes with lightweighting. In this paper, we present MTNet, a lightweight detector for driver distraction detection scenarios. MTNet consists of a multidimensional adaptive feature extraction block, a lightweight feature fusion block and utilizes the IoU-NWD weighted loss function, all while considering the accuracy gain of tiny target detection. In the feature extraction component, a lightweight backbone network is employed in conjunction with four attention mechanisms strategically integrated across the kernel space. This approach enhances the performance limits of the lightweight network. The lightweight feature fusion module is designed to reduce computational complexity and memory access. The interaction of channel information is improved through the use of lightweight arithmetic techniques. Additionally, CFSM module and EPIEM module are employed to minimize redundant feature map computations and strike a better balance between model weights and accuracy. Finally, the IoU-NWD weighted loss function is formulated to enable more effective detection of tiny targets. We assess the performance of the proposed method on the LDDB benchmark. The experimental results demonstrate that our proposed method outperforms multiple advanced detection models.

Drug-target affinity prediction method based on multi-scale information interaction and graph optimization.

Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability. Therefore, transformer and diffusion on drug graphs in DTA prediction (TDGraphDTA) are introduced to predict drug-target interactions using multi-scale information interaction and graph optimization. An interactive module is integrated into feature extraction of drug and target features at different granularity levels. A diffusion model-based graph optimization module is proposed to improve the representation of molecular graph structures and enhance the interpretability of graph representations while obtaining optimal feature representations. In addition, TDGraphDTA improves the accuracy and reliability of predictions by capturing relationships and contextual information between molecular substructures. The performance of the proposed TDGraphDTA in DTA prediction was verified on three publicly available benchmark datasets (Davis, Metz, and KIBA). Compared with state-of-the-art baseline models, it achieved better results in terms of consistency index, R-squared, etc. Furthermore, compared with some existing methods, the proposed TDGraphDTA is demonstrated to have better structure capturing capabilities by visualizing the feature capturing capabilities of the model using Grad-AAM toxicity labels in the ToxCast dataset. The corresponding source codes are available at https://github.com/Lamouryz/TDGraph.

Genetic analysis of Vibrio alginolyticus challenged by Fructus schisandrae reveals the mechanism of virulence genes.

Due to the abusive use of antibiotics, bacterial resistance has become a global problem and poses severe threats to aquaculture. The drug-resistant diseases caused by Vibrio alginolyticus have caused significant economic losses to cultured marine fish. Fructus schisandrae is used to treat inflammatory diseases in China and Japan. There have been no reports of bacterial molecular mechanisms associated with F. schisandrae stress. In this study, the inhibiting effect of F. schisandrae on the growth of V. alginolyticus was detected to understand response mechanisms at the molecular level. The antibacterial tests were analyzed via next-generation deep sequencing technology (RNA sequencing, RNA-seq). Wild V. alginolyticus (CK) was compared with V. alginolyticus, F. schisandrae incubated for 2 h, and V. alginolyticus, F. schisandrae incubated for 4 h. Our results revealed that there were 582 genes (236 upregulated and 346 downregulated) and 1068 genes (376 upregulated and 692 downregulated), respectively. Differentially expressed genes (DEGs) were involved in the following functional categories: metabolic process, single-organism process, catalytic activity, cellular process, binding, membrane, cell part, cell, and localization. FS_2 h was compared with FS_4 h, and 21 genes (14 upregulated and 7 downregulated) were obtained. The RNA-seq results were validated by detecting the expression levels of 13 genes using quantitative real-time polymerase chain reaction (qRT-PCR). The qRT-PCR results matched those of the sequencing, which reinforced the reliability of the RNA-seq. The results revealed the transcriptional response of V. alginolyticus to F. schisandrae, which will provide new ideas for studying V. alginolyticus' complex virulence molecular mechanism and the possibility of developing Schisandra to prevent and treat drug-resistant diseases.

Mechanisms Underlying the Virulence Regulation of Vibrio alginolyticus ND-01 pstS and pstB with a Transcriptomic Analysis.

Vibrio alginolyticus is a common opportunistic pathogen of fish, shrimp, and shellfish, and many diseases it causes can result in severe economic losses in the aquaculture industry. Causing host disease was confirmed by several virulence factors of V. alginolyticus. To date, there have been no reports on the effect of the pstS gene on its virulence regulation of V. alginolyticus. The virulence mechanism of target genes regulating V. alginolyticus is worthy of further study. Previous studies found that Fructus schisandrae (30 mg/mL) inhibited the growth of V. alginolyticus ND-01 (OD600 = 0.5) for 4 h, while the expressions of pstS and pstB were significantly affected by F. schisandrae stress. So, we speculated that pstS and pstB might be the virulence genes of V. alginolyticus, which were stably silenced by RNAi to construct the silencing strains pstS-RNAi and pstB-RNAi, respectively. After the expression of pstS or pstB gene was inhibited, the adhesion capacity and biofilm formation of V. alginolyticus were significantly down-regulated. The chemotaxis and biofilm formation ability of pstS-RNAi was reduced by 33.33% and 68.13% compared with the wild-type strain, respectively. Sequence alignment and homology analysis showed that pstS was highly conserved, which suggested that pstS played a vital role in the secretion system of V. alginolyticus. The pstS-RNAi with the highest silencing efficiency was selected for transcriptome sequencing. The Differentially Expressed Genes (DEGs) and GO terms were mapped to the reference genome of V. alginolyticus, including 1055 up-regulated genes and 1134 down-regulated genes. The functions of the DEGs were analyzed by GO and categorized into different enriched functional groups, such as ribosome synthesis, organelles, biosynthesis, pathogenesis, and secretion. These DEGs were then mapped to the reference KEGG pathways of V. alginolyticus and enriched in commonalities in the metabolic, ribosomal, and bacterial secretion pathways. Therefore, pstS and pstB could regulate the bacterial virulence of V. alginolyticus by affecting its adhesion, biofilm formation ability, and motility. Understanding the relationship between the expressions of pstS and pstB with bacterial virulence could provide new perspectives to prevent bacterial diseases.

The temperature-dependent expression of type II secretion system controls extracellular product secretion and virulence in mesophilic Aeromonas salmonida SRW-OG1.

Aeromonas salmonicida is a typical cold water bacterial pathogen that causes furunculosis in many freshwater and marine fish species worldwide. In our previous study, the pathogenic A. salmonicida (SRW-OG1) was isolated from a warm water fish, Epinephelus coioides was genomics and transcriptomics analyzed. Type II secretion system was found in the genome of A. salmonicida SRW-OG1, while the expressions of tatA, tatB, and tatC were significantly affected by temperature stress. Also, sequence alignment analysis, homology analysis and protein secondary structure function analysis showed that tatA, tatB, and tatC were highly conservative, indicating their biological significance. In this study, by constructing the mutants of tatA, tatB, and tatC, we investigated the mechanisms underlying temperature-dependent virulence regulation in mesophilic A. salmonida SRW-OG1. According to our results, tatA, tatB, and tatC mutants presented a distinct reduction in adhesion, hemolysis, biofilm formation and motility. Compared to wild-type strain, inhibition of the expression of tatA, tatB, and tatC resulted in a decrease in biofilm formation by about 23.66%, 19.63% and 40.13%, and a decrease in adhesion ability by approximately 77.69%, 80.41% and 62.14% compared with that of the wild-type strain. Furthermore, tatA, tatB, and tatC mutants also showed evidently reduced extracellular enzymatic activities, including amylase, protease, lipase, hemolysis and lecithinase. The genes affecting amylase, protease, lipase, hemolysis, and lecithinase of A. salmonicida SRW-OG1 were identified as cyoE, ahhh1, lipA, lipB, pulA, HED66_RS01350, HED66_RS19960, aspA, fabD, and gpsA, which were notably affected by temperature stress and mutant of tatA, tatB, and tatC. All above, tatA, tatB and tatC regulate the virulence of A. salmonicida SRW-OG1 by affecting biofilm formation, adhesion, and enzymatic activity of extracellular products, and are simultaneously engaged in temperature-dependent pathogenicity.

Knockdown of growth factor receptor bound protein 7 suppresses angiogenesis by inhibiting the secretion of vascular endothelial growth factor A in ovarian cancer cells.

Growth factor receptor bound protein 7 (GRB7) plays an important role in regulating the growth and metastasis of ovarian cancer. Angiogenesis is the basis for the growth, invasion, and metastasis of malignant tumors. In the current study, we aimed to determine whether GRB7 plays a role in regulating angiogenesis in ovarian cancer. Immunohistochemistry on tissue microarray showed that GRB7 and platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31) protein expression were positively correlated in ovarian cancer tissues. GRB7 knockdown suppressed vascular endothelial growth factor A (VEGFA) expression and reduced VEGFA secretion. The effects of GRB7-silenced SKOV-3 cells on human umbilical vein endothelial cells (HUVECs) were evaluated using a transwell cell co-culture model, which showed that knockdown of GRB7 in SKOV-3 cells suppressed HUVEC proliferation, migration, invasion, and tube formation. Moreover, knockdown of GRB7 in SKOV-3 cells downregulated the expression of proteins associated with angiogenesis, including vascular endothelial growth factor receptor-2 (VEGFR2), mitogen-activated protein kinase kinase 1 (MAP2K1/MEK1), extracellular signal-regulated kinases 1 and 2 (ERK1/2), notch receptor 1 (NOTCH1), and delta-like canonical Notch ligand 4 (DLL4) in HUVECs. In conclusion, knockdown of GRB7 in ovarian cancer cells is an attractive potential therapeutic target for the suppression of angiogenesis in ovarian cancer. GRB7 may regulate angiogenesis through VEGFA/VEGFR2 signaling and its downstream pathways.

Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary.

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.

A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification.

As a crucial task in surveillance and security, person re-identification (re-ID) aims to identify the targeted pedestrians across multiple images captured by non-overlapping cameras. However, existing person re-ID solutions have two main challenges: the lack of pedestrian identification labels in the captured images, and domain shift issue between different domains. A generative adversarial networks (GAN)-based self-training framework with progressive augmentation (SPA) is proposed to obtain the robust features of the unlabeled data from the target domain, according to the preknowledge of the labeled data from the source domain. Specifically, the proposed framework consists of two stages: the style transfer stage (STrans), and self-training stage (STrain). First, the targeted data is complemented by a camera style transfer algorithm in the STrans stage, in which CycleGAN and Siamese Network are integrated to preserve the unsupervised self-similarity (the similarity of the same image between before and after transformation) and domain dissimilarity (the dissimilarity between a transferred source image and the targeted image). Second, clustering and classification are alternately applied to enhance the model performance progressively in the STrain stage, in which both global and local features of the target-domain images are obtained. Compared with the state-of-the-art methods, the proposed method achieves the competitive accuracy on two existing datasets.

EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID.

Person re-identification (Re-ID) is challenging due to host of factors: the variety of human positions, difficulties in aligning bounding boxes, and complex backgrounds, among other factors. This paper proposes a new framework called EXAM (EXtreme And Moderate feature embeddings) for Re-ID tasks. This is done using discriminative feature learning, requiring attention-based guidance during training. Here "Extreme" refers to salient human features and "Moderate" refers to common human features. In this framework, these types of embeddings are calculated by global max-pooling and average-pooling operations respectively; and then, jointly supervised by multiple triplet and cross-entropy loss functions. The processes of deducing attention from learned embeddings and discriminative feature learning are incorporated, and benefit from each other in this end-to-end framework. From the comparative experiments and ablation studies, it is shown that the proposed EXAM is effective, and its learned feature representation reaches state-of-the-art performance.

Increased uterine NLRP3 inflammasome and leucocyte infiltration in a rat model of preeclampsia.

The disruption of the inflammatory microenvironment in the uterus affects pregnancy outcome. However, the exact quantification and distribution of leukocyte subpopulations in the uterus in preeclampsia (PE) have not been clearly characterized. Inflammasomes promote the release of proinflammatory cytokines interleukin (IL)-ß and IL-18. A higher expression of NLRP3 inflammasome in placentas contributes to excessive inflammation in PE. However, related studies on the uterus are scarce. We aimed to investigate changes in the infiltration of leukocyte subpopulations in decidual and uterine tissues, and explore the role of activation of uterine NLRP3 inflammasomes in PE. Decidual tissues were collected from normotensive pregnant women and preeclamptic women. A PE-like model was established via administration of lipopolysaccharide to normal pregnant rats. Uterine and decidual tissues were collected from all experimental groups. It was found that the number of leukocytes was significantly elevated in decidual and uterine tissues in PE patients compared to normal controls. The leukocytes (predominantly macrophages and NK cells) particularly infiltrated into the decidua and uterine decidua in PE-like rats, and these were sparse in the myometrium. The NLRP3 immunoreactivity in the uterus was extremely little in control rats, its immunoreactivity and caspase-1 immunoreactivity were significantly elevated in the PE-like rats; the mRNA expression results also indicated an upward trend in the activation of NLRP3 inflammasomes. These results support that leucocyte infiltration in the decidua and uterine deciduas, and the activation of NLRP3 inflammasome in the uterus, which participate in the pathogenesis, are responsible for the excessive inflammation at the maternal-fetal interface during PE.

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition.

Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained. Second, the activity level and matching level are exploited in the focus decision mapping of the low-frequency sub-image fusion, with the former calculated by using local window energy and the latter measured by the color difference between color pixels expressed by a quaternion. Third, the fusion results of low-frequency coefficients are incorporated into the fusion of high-frequency sub-images, and a local contrast fusion rule based on the integration of high-frequency and low-frequency regions is proposed. Finally, the fused images are reconstructed employing inverse transform of the QMSVD. Simulation results show that image fusion using this method achieves great overall visual effects, with high resolution images, rich colors, and low information loss.

A survey of influencing factors of missed abortion during the two-child peak period.

This study aimed to investigate the influencing factors of missed abortion during the two-child peak period in China. 220 pregnant women were divided into observation (presence of missed abortion, 100 cases) and control group (no presence of missed abortion, 120 cases). The single factor analysis of clinical data showed that, advanced age, premarital examination, genitalia abnormality, luteal insufficiency, spouse semen abnormality, mycoplasma infection, chlamydia infection, sexually transmitted diseases, perm or dyeing hair in pregnancy, radiation overload, primipara, spontaneous abortion history, smoking, drinking and overly intimate with pets had significant difference between observation and control group (p < .05). The logistic regression analysis results showed that, the advanced age, genital abnormality, luteal insufficiency, spouse sperm abnormality, pregnancy infection, primipara, spontaneous abortion history and bad life habits were the main risk factors of missed abortion. In the intervention for prevention of missed abortion, these factors should be paid more attention.Impact statementWhat is already known on this subject? There are many complex factors affecting the embryonic development and causing the missed abortion.What do the results of this study add? The advanced age, genital abnormality, luteal insufficiency, spouse sperm abnormality, pregnancy infection, primipara, spontaneous abortion history and bad life habits are the main risk factors of missed abortion.What are the implications of these findings for clinical practice and/or further research? These findings can provide a theoretical basis for the further prevention of missed abortion.

A Precise Multi-Exposure Image Fusion Method Based on Low-level Features.

Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.

Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data.

Impact of epidural analgesia during labor on breastfeeding initiation and continuation: a retrospective study.

Objective: Although epidural analgesia is widely used during labor, its impact on breastfeeding has not yet reached a consensus. This retrospective cohort study was to investigate the association of patient-controlled epidural analgesia (PCEA) during labor with breastfeeding initiation and continuation.Methods: Medical records from 1 February, 2016 to 31 December, 2016 at Guangzhou Women and Children's Medical Center, China were reviewed for women received PCEA or not. Breastfeeding continuation was assessed by a questionnaire at 6 months after hospital discharge.Results: Nine hundred twenty-two women were enrolled in the study, with 527 of these women received PCEA for labor analgesia. The proportion of timely initiation of breastfeeding (within 1 h after birth), and exclusive or partial breastfeeding at any of the evaluation time points (1, 3, and 6 months) between two groups showed no statistically significant difference.Conclusion: Our data do not support an association between the PCEA and discontinuation of breastfeeding within 6 months postpartum.

An Image Fusion Method Based on Sparse Representation and Sum Modified-Laplacian in NSCT Domain.

Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, etc. Traditional multi-scale transform (MST) based image fusion solutions have difficulties in the selection of decomposition level, and the contrast loss in fused image. At the same time, traditional sparse-representation based image fusion methods suffer the weak representation ability of fixed dictionary. In order to overcome these deficiencies of MST- and SR-based methods, this paper proposes an image fusion framework which integrates nonsubsampled contour transformation (NSCT) into sparse representation (SR). In this fusion framework, NSCT is applied to source images decomposition for obtaining corresponding low- and high-pass coefficients. It fuses low- and high-pass coefficients by using SR and Sum Modified-laplacian (SML) respectively. NSCT inversely transforms the fused coefficients to obtain the final fused image. In this framework, a principal component analysis (PCA) is implemented in dictionary training to reduce the dimension of learned dictionary and computation costs. A novel high-pass fusion rule based on SML is applied to suppress pseudo-Gibbs phenomena around singularities of fused image. Compared to three mainstream image fusion solutions, the proposed solution achieves better performance on structural similarity and detail preservation in fused images.

A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure.

Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.