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BACKGROUND: Herba Patriniae and Coix seed (HC) constitute a widely utilized drug combination in the clinical management of colorectal cancer (CRC) that is known for its diuretic, anti-inflammatory, and swelling-reducing properties. Although its efficacy has been demonstrated in a clinical setting, the active compounds and their mechanisms of action in CRC treatment remain to be fully elucidated. AIM: To identify the active, CRC-targeting components of HC and to elucidate the mechanisms of action involved. METHODS: Active HC components were identified and screened using databases. Targets for each component were predicted. CRC-related targets were obtained from human gene databases. Interaction targets between HC and CRC were identified. A "drug-ingredient-target" network was created to identify the core components and targets involved. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to elucidate the key pathways involved. Molecular docking between core targets and key components was executed. In vitro experiments validated core monomers. RESULTS: Nineteen active components of HC were identified, with acacetin as the primary active compound. The predictive analysis identified 454 targets of the active compounds in HC. Intersection mapping with 2685 CRC-related targets yielded 171 intervention targets, including 30 core targets. GO and KEGG analyses indicated that HC may influence the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. Molecular docking showed that acacetin exhibited an optimal interaction with AKT1, identifying PI3K, AKT, and P53 as key genes likely targeted by HC during CRC treatment. Acacetin inhibited HT-29 cell proliferation and migration, as well as promoted apoptosis, in vitro. Western blotting analysis revealed increased p53 and cleaved caspase-3 expression and decreased levels of p-PI3K, p-Akt, and survivin, which likely contributed to CRC apoptosis. CONCLUSION: Acacetin, the principal active compound in the HC pair, inhibited the proliferation and migration of HT-29 cells and promoted apoptosis through the PI3K/Akt/p53 signaling pathway.
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Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NPhard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.
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Background: Escherichia coli is an important intestinal flora, of which pathogenic E. coli is capable of causing many enteric and extra-intestinal diseases. Antibiotics are essential for the treatment of bacterial infections caused by pathogenic E. coli; however, with the widespread use of antibiotics, drug resistance in E. coli has become particularly serious, posing a global threat to human, animal, and environmental health. While the drug resistance and pathogenicity of E. coli carried by tigers and leopards in captivity have been studied intensively in recent years, there is an extreme lack of information on E. coli in these top predators in the wild environment. Methods: Whole genome sequencing data of 32 E. coli strains collected from the feces of wild Amur tiger (Panthera tigris altaica, n = 24) and North China leopard (Panthera pardus japonensis, n = 8) were analyzed in this article. The multi-locus sequence types, serotypes, virulence and resistance genotypes, plasmid replicon types, and core genomic SNPs phylogeny of these isolates were studied. Additionally, antimicrobial susceptibility testing (AST) was performed on these E. coli isolates. Results: Among the E. coli isolates studied, 18 different sequence types were identified, with ST939 (21.9%), ST10 (15.6%), and ST3246 (9.4%) being the most prevalent. A total of 111 virulence genes were detected, averaging about 54 virulence genes per sample. They contribute to invasion, adherence, immune evasion, efflux pump, toxin, motility, stress adaption, and other virulence-related functions of E. coli. Sixty-eight AMR genes and point mutations were identified. Among the detected resistance genes, those belonging to the efflux pump family were the most abundant. Thirty-two E. coli isolates showed the highest rate of resistance to tetracycline (14/32; 43.8%), followed by imipenem (4/32; 12.5%), ciprofloxacin (3/32; 9.4%), doxycycline (2/32; 6.3%), and norfloxacin (1/32; 3.1%). Conclusions: Our results suggest that E. coli isolates carried by wild Amur tigers and North China leopards have potential pathogenicity and drug resistance.
Subject(s)
Escherichia coli , Feces , Panthera , Tigers , Whole Genome Sequencing , Animals , Tigers/microbiology , Escherichia coli/genetics , Escherichia coli/drug effects , Escherichia coli/pathogenicity , Escherichia coli/isolation & purification , Panthera/microbiology , Feces/microbiology , Escherichia coli Infections/veterinary , Escherichia coli Infections/microbiology , Phylogeny , Anti-Bacterial Agents/pharmacology , Genome, Bacterial/genetics , Microbial Sensitivity Tests , China , Virulence/genetics , Drug Resistance, Bacterial/genetics , Polymorphism, Single Nucleotide/genetics , Multilocus Sequence TypingABSTRACT
Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.
Subject(s)
Imaging, Three-Dimensional , Imaging, Three-Dimensional/methods , Microscopy, Electron/methods , Arabidopsis/ultrastructure , Arabidopsis/growth & development , Arabidopsis/cytology , Algorithms , Plant Cells/ultrastructure , Image Processing, Computer-Assisted/methodsABSTRACT
BACKGROUND: The purpose of this study is to explore the effects of CB2 on bone regulation during orthodontic tooth movement. METHODS: Thirty male mice were allocated into 2 groups (n = 15 in each group): wild type (WT) group and CB2 knockout (CB2-/-) group. Orthodontic tooth movement (OTM) was induced by applying a nickel-titanium coil spring between the maxillary first molar and the central incisors. There are three subgroups within the WT groups (0, 7 and 14 days) and the CB2-/- groups (0, 7 and 14 days). 0-day groups without force application. Tooth displacement, alveolar bone mass and alveolar bone volume were assessed by micro-CT on 0, 7 and 14 days, and the number of osteoclasts was quantified by tartrate-resistant acid phosphatase (TRAP) staining. Moreover, the expression levels of RANKL and OPG in the compression area were measured histomorphometrically. RESULTS: The WT group exhibited the typical pattern of OTM, characterized by narrowed periodontal space and bone resorption on the compression area. In contrast, the accelerated tooth displacement, increased osteoclast number (P < 0.0001) and bone resorption on the compression area in CB2-/- group. Additionally, the expression of RANKL was significantly upregulated, while OPG showed low levels in the compression area of the CB2 - / - group (P < 0.0001). CONCLUSIONS: CB2 modulated OTM and bone remodeling through regulating osteoclast activity and RANKL/OPG balance.
Subject(s)
Bone Remodeling , Bone Resorption , Receptor, Cannabinoid, CB2 , Tooth Movement Techniques , Animals , Male , Mice , Bone Remodeling/physiology , Osteoclasts , Receptor, Cannabinoid, CB2/geneticsABSTRACT
The development of chemoimmunotherapy with reduced systemic toxicity using local formulations is an effective strategy for combating tumor recurrence. Herein, we reported a localized hydrogel system for antitumor chemoimmunotherapy, formed by doxorubicin (DXR)-loaded bovine serum albumin (BSA) nanoparticles self-cross-linked with natural polysaccharide chitosan (CS). The drug-loaded hydrogel (DXR-CBGel) with antiswelling performance and prolonged drug-release profile was combined with antiprogrammed cell death protein 1 (aPD-1) as an in situ vaccine for treating glioblastoma multiforme (GBM) lesions. The antiswelling hydrogel system shows excellent biosafety for volume-sensitive GBM lesions. Both the albumin-bound formulation and the in situ gelation design facilitate the local retention and sustained release of DXR to generate long-term chemoimmunotherapy with reduced systemic toxicity. The chemotherapy-induced immunogenic cell death of DXR with the assistance of immunotherapeutic CS can trigger tumor-specific immune responses, which are further amplified by an immune checkpoint blockade to effectively inhibit cancer recurrence. The strategy of combining albumin-bound drug formulation and biocompatible polymer-based hydrogel for localized chemoimmunotherapy shows great potential against postsurgery glioblastoma recurrence.
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The increasing demand for accurate imaging spectral information in remote sensing detection has driven the development of hyperspectral remote sensing instruments towards a larger view field and higher resolution. As the core component of the spectrometer slit, the designed length reaches tens of millimeters while the precision maintained within the µm level. Such precision requirements pose challenges to traditional machining and laser processing. In this paper, a high-precision air slit was created with a large aspect ratio through MEMS technology on SOI silicon wafers. In particular, a MEMS slit was prepared with a width of 15 µm and an aspect ratio exceeding 4000:1, and a spectral spectroscopy system was created and tested with a Hg-Cd light source. As a result, the spectral spectrum was linear within the visible range, and a spectral resolution of less than 1 nm was obtained. The standard deviation of resolution is only one-fourth of that is seen in machined slits across various view fields. This research provided a reliable and novel manufacturing technique for high-precision air slits, offering technical assistance in developing high-resolution wide-coverage imaging spectrometers.
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Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit very small temperature differences compared with the surrounding area, complicating plantar foot segmentation in such cold-stressed active thermography. In this study, we investigate new plantar foot segmentation methods for thermal images obtained via cold-stressed active thermography without the complementary information from color or depth channels. To better deal with the temporal variations in thermal image contrast when planar feet are recovering from cold immersion, we propose an image pre-processing method using a two-stage adaptive gamma transform to alleviate the impact of such contrast variations. To improve upon existing deep neural networks for segmenting planar feet from cold-stressed infrared thermograms, a new deep neural network, the Plantar Foot Segmentation Network (PFSNet), is proposed to better extract foot contours. It combines the fundamental U-shaped network structure, a multi-scale feature extraction module, and a convolutional block attention module with a feature fusion network. The PFSNet, in combination with the two-stage adaptive gamma transform, outperforms multiple existing deep neural networks in plantar foot segmentation for single-channel infrared images from cold-stressed infrared thermography, achieving an accuracy of 97.3% and 95.4% as measured by Intersection over Union (IOU) and Dice Similarity Coefficient (DSC) respectively.
Subject(s)
Diabetes Mellitus , Diabetic Foot , Humans , Diabetic Foot/diagnostic imaging , Thermography/methods , Neural Networks, Computer , Foot/diagnostic imaging , Image Processing, Computer-Assisted/methodsABSTRACT
A coordinated interaction between osteogenesis and the osteoimmune microenvironment plays a vital role in regulating bone healing. However, disturbances in the pro- and anti-inflammatory balance hinder the therapeutic advantages of biomaterials. In this study, a novel composite hydrogel was successfully fabricated using graphene oxide (GO)-loaded processed pyritum (PP) in combination with poly(ethylene glycol) diacrylate (PEGDA) and carboxymethyl chitosan (CMC). Subsequently, the immunomodulatory effects and bone regenerative potential of PP/GO@PEGDA/CMC were investigated. The results demonstrated that the PP/GO@PEGDA/CMC hydrogel possessed excellent mechanical properties, swelling capacity, and stability. Moreover, PP/GO@PEGDA/CMC prominently promoted M2 polarization and increased the levels of anti-inflammatory factors (interleukin (IL)-4, IL-10, and transforming growth factor-ß). These beneficial effects facilitated the proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells in vitro. Additionally, the in vivo results further verified that the implantation of PP/GO@PEGDA/CMC markedly reduced local inflammation while enhancing bone regeneration at 8 weeks post-implantation. Therefore, the results of this study provide potential therapeutic strategies for bone tissue repair and regeneration by modulating the immune microenvironment.
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"Feicheng" peach is popular for its unique aroma, but its defect of being highly sensitive to chilling injury (CI) often leads to aroma loss and internal browning. Essential oils (EOs) are often used to enhance the antioxidant capacity of plants and fruits, as well as to trigger their defense against biotic/abiotic stresses. This study aimed to examine the effect of cinnamon essential oil (CEO) vapor treatment on the aroma quality of peach fruit during cold storage using HS-GC-IMS. The results showed that 50 µL/L CEO vapor reduced the severity of internal browning (IB) in peaches at the stage of 7 ~ 21 d during refrigeration (Significantly, the L* value was higher and the IB index was lower than that of control, p < 0.05). Meanwhile, the evident reduction or loss of aroma content caused by CI was restored to a higher level than the control (p < 0.05). Furthermore, CEO treatment promoted the release of aroma-related volatiles as evidenced by more propyl acetate, and the dimer of amyl acetate, isoamyl acetate, butyl acetate detected than that on harvest day and no-treated group after 21 d of cold storage plus 2 d of shelf life. Genes of PpLOX1, PpLOX2, PpHPL1 and PpADH1 associated with aroma-related volatile biosynthesis revealed higher transcript abundance in peach fruits treated with CEO than the control (p < 0.05). Overall, our study demonstrated that CEO in vapor phase may be beneficial to alleviate the quality deterioration in aroma and flesh color of "Feicheng" peaches caused by CI, which lays a theoretical reference for maintaining postharvest quality of peach fruits.
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Bagging-free apple is more vulnerable to postharvest disease, which severely limits the cultivation pattern transformation of the apple industry in China. This study aimed to ascertain the dominant pathogens in postharvest bagging-free apples, to evaluate the efficacy of essential oil (EO) on inhibition of fungal growth, and to further clarify the molecular mechanism of this action. By morphological characteristics and rDNA sequence analyses, Botryosphaeria dothidea (B. dothidea) and Colletotrichum gloeosporioides (C. gloeosporioides) were identified as the main pathogens isolated from decayed bagging-free apples. Cinnamon and clove EO exhibited high inhibitory activities against mycelial growth both in vapor and contact phases under in vitro conditions. EO vapor at a concentration of 60 µL L-1 significantly reduced the incidence and lesion diameter of inoculated decay in vivo. Observations using a scanning electron microscope (SEM) and transmission electron microscope (TEM) revealed that EO changed the mycelial morphology and cellular ultrastructure and destroyed the integrity and structure of cell membranes and major organelles. Using RNA sequencing and bioinformatics, it was demonstrated that clove EO treatment impaired the cell membrane integrity and biological function via downregulating the genes involved in the membrane component and transmembrane transport. Simultaneously, a stronger binding affinity of trans-cinnamaldehyde and eugenol with CYP51 was assessed by in silico analysis, attenuating the activity of this ergosterol synthesis enzyme. Moreover, pronounced alternations in the oxidation/reduction reaction and critical materials metabolism of clove EO-treated C. gloeosporioides were also observed from transcriptomic data. Altogether, these findings contributed novel antimicrobial cellular and molecular mechanisms of EO, suggesting its potential use as a natural and useful preservative for controlling postharvest spoilage in bagging-free apples.
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InGaAs/AlGaAs multiple quantum well lasers grown on silicon (001) by molecular beam epitaxy have been demonstrated. By inserting InAlAs trapping layers into AlGaAs cladding layers, misfit dislocations easily located in the active region can be effectively transferred out of the active region. For comparison, the same laser structure without the InAlAs trapping layers was also grown. All these as-grown materials were fabricated into Fabry-Perot lasers with the same cavity size of 20 × 1000 µm2. The laser with trapping layers achieved a 2.7-fold reduction in threshold current density under pulsed operation (5 µs-pulsed width, 1%-duty cycle) compared to the counterpart, and further realized a room-temperature continuous-wave lasing with a threshold current of 537â mA which corresponds to a threshold current density of 2.7â kA/cm2. When the injection current reached 1000â mA, the single-facet maximum output power and slope efficiency were 45.3â mW and 0.143 W/A, respectively. This work demonstrates significantly improved performances of InGaAs/AlGaAs quantum well lasers monolithically grown on silicon, providing a feasible solution to optimize the InGaAs quantum well structure.
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BACKGROUND: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. RESULTS: In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. CONCLUSIONS: We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.
Subject(s)
Electrons , Image Processing, Computer-Assisted , Consensus , Image Processing, Computer-Assisted/methods , Neurons/ultrastructure , AlgorithmsABSTRACT
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear conditioning significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.
Subject(s)
Deep Learning , Animals , Fear , Mice , Microscopy, Electron , Mitochondria/ultrastructure , Neuronal Plasticity , Synapses/metabolismABSTRACT
We determined the whole mtDNA genome of the Common Pochard (Aythya ferina) in the Ningxia Hui Autonomous Region, China. The complete mitochondrial genome is 16,599 bp in length and consists of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The nucleotide composition is 29.34% A, 22.23% T, 15.66% G, and 32.77% C. Phylogenetic analysis results showed close genetic relationship between A. ferina and Aythya americana.
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We determined the whole mtDNA genome of the gray-headed lapwing (Vanellus cinereus) in Ningxia Hui Autonomous Region, China. The complete mitochondrial genome is 17,078 bp in length and consists of 13 protein-coding genes (PCGs), 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The nucleotide composition is 31.65% A, 23.50% T, 13.76% G, and 31.09% C. The result of phylogenetic analysis showed that there was close genetic relationship between V. cinereus and V. vanellus.
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The burbot (Lota lota) is the only member of the order Gadiformes adapted solely to freshwater. This species has the widest longitudinal range among freshwater fish worldwide. Burbot serves as a good model for studies on adaptive genome evolution from marine to freshwater environments. However, a high-quality reference genome of burbot has not yet been released. Here, the first chromosome-level genome of burbot was constructed using PacBio long sequencing and Hi-C technology. A total of 95.24 Gb polished PacBio sequences were generated, and the preliminary genome assembly was 575.83 Mb in size with a contig N50 size of 2.15 Mb. The assembled sequences were anchored to 22 pseudochromosomes by using Hi-C data. The final assembled genome after Hi-C correction was 575.92 Mb, with a contig N50 of 2.01 Mb and a scaffold N50 of 22.10 Mb. A total of 22,067 protein-coding genes were predicted, 94.82% of which were functionally annotated. Phylogenetic analyses indicated that burbot diverged with the Atlantic cod approximately 43.8 million years ago. In addition, 377 putative genes that appear to be under positive selection in burbot were identified. These positively selected genes might be involved in the adaptation to the freshwater environment. These genome data provide an invaluable resource for the ecological and evolutionary study of the order Gadiformes.
Subject(s)
Adaptation, Biological/genetics , Gadiformes , Genome , Animals , Chromosomes , Fresh Water , Gadiformes/genetics , PhylogenyABSTRACT
The Ferruginous Duck (Aythya nyroca) is a diving duck that is widely distributed in Asia, Africa, and Europe. We determined the complete mitogenome of the Ferruginous Duck gathered at Ningxia, China. The total length of the complete mitochondrial genome is 16,623 bp and it consists of 13 protein-coding, 22 tRNA, 2 rRNA genes, and 1 control region (CR). Only one overlap among the 13 protein-coding genes was found: ND4L/ND4. The CR is 1068 bp in length. The nucleotide composition is 29.66% A, 22.28% T, 15.35% G, 32.71% C. The result of phylogenetic analysis showed that there is close genetic relationship among Aythya nyroca and three ducks in the Genus Aythya.
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The microRNA-based mechanisms underlying the antioxidant action(s) of co-existing flavonoids in response to oxidative stress are of high interest. This study aimed to extend the existing knowledge and provide insights into the potential regulatory network in response to oxidative stress and the co-presence of quercetin and catechin antioxidants, via a preclinical approach using H2O2-stimulated HepG2 cells. It was confirmed that BACH1 serves as an essential and direct negative regulator of the Keap1-Nrf2 signaling pathway and the antioxidant synergism between quercetin and catechin. BACH1 promoted reactive oxygen species (ROS) accumulation while inhibiting cell growth, which could be reversed by the synergistic action of let-7a-5p and miR-25-3p in the co-presence of quercetin and catechin. Both let-7a-5p and miR-25-3p could directly regulate the expression and function of BACH1 (e.g. upregulation of the two miRNAs could rescue largely overexpression of BACH1). Although these molecular interactions likely represented only some aspects of the overall regulatory network, this research confirms the feasibility of the combined uses of dietary flavonoids with chemopreventive properties in synergy during multiple-target interactions and multiple-pathway regulation.
Subject(s)
Catechin , MicroRNAs , Antioxidants/pharmacology , Basic-Leucine Zipper Transcription Factors , Catechin/pharmacology , Hep G2 Cells , Humans , Hydrogen Peroxide , Kelch-Like ECH-Associated Protein 1 , MicroRNAs/genetics , NF-E2-Related Factor 2/genetics , NF-E2-Related Factor 2/metabolism , Quercetin/pharmacologyABSTRACT
Multi-focus image fusion consists in the integration of the focus regions of multiple source images into a single image. At present, there are still some common problems in image fusion methods, such as block artifacts, artificial edges, halo effects, and contrast reduction. To address these problems, a novel, to the best of our knowledge, multi-focus image fusion method using energy of Laplacian and a deep neural network (DNN) is proposed in this paper. The DNN is composed of multiple denoising autoencoders and a classifier. The Laplacian energy operator can effectively extract the focus information of source images, and the trained DNN model can establish a valid mapping relationship between source images and a focus map according to the extracted focus information. First, the Laplacian energy operator is used to perform focus measurement for two source images to obtain the corresponding focus information maps. Then, the sliding window technology is used to sequentially obtain the windows from the corresponding focus information map, and all of the windows are fed back to the trained DNN model to obtain a focus map. After binary segmentation and small region filtering, a final decision map with good consistency is obtained. Finally, according to the weights provided by the final decision map, multiple source images are fused to obtain a final fusion image. Experimental results demonstrate that the proposed fusion method is superior to other existing ones in terms of subjective visual effects and objective quantitative evaluation.