An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNe...An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.展开更多
Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood cell.In the marrow of human bones,leukaemia is developed and is responsible for blood cell g...Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood cell.In the marrow of human bones,leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC,and if any cell gets blasted,then it may become a cause of death.Therefore,the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives.Subsequently,in terms of detection,image segmentation techniques play a vital role,and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia(ALL)type of blood cancer.Moreover,the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus,which is well-known as the Region Of Interest(ROI).As a result,in this article,we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios,including K-means,FCM(Fuzzy Cmeans),K-means with FFA(Firefly Algorithm),and FCM with FFA.Also,we determine the most effective method of blood cell nucleus segmentation,which we subsequently use for the Leukaemia classification model.Finally,using the Convolution Neural Network(CNN)as a classifier,we developed a leukaemia cancer classification model from the microscopic images.The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art.In terms of experimental analysis,we observed that the accuracy of the model is near to 99%,and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.展开更多
In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method ...In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method or the X-ray powder crystal diffraction method cannot accurately determine the rock. An X-ray powder diffraction method combined with thin-film microscopic image technique and rock identification method was proposed. The X-ray powder diffraction method was combined with the thin-film microscopic image technique to identify the rock, and the microscopic image technique was used to determine the rock. The particle size, structure, shape, mineral color and structure, determine the type of rock, and then determine the mineral and mineral content of the rock by X-ray powder diffraction method, name the rock, and complete the identification of the rock. The experimental results show that the X-ray powder diffraction method or the thin-film microscopic image technique can not accurately determine the rock and combine the X-ray powder diffraction method with the thin-film microscopic image technology to identify the rock. Improve the accuracy of rock identification results.展开更多
Triple-negative breast cancer (TNBC) is an aggressive and often fatal disease, especially since the brain metastasis of TNBC has been a particularly severe manifestation. However, brain metastasis in TNBC at early sta...Triple-negative breast cancer (TNBC) is an aggressive and often fatal disease, especially since the brain metastasis of TNBC has been a particularly severe manifestation. However, brain metastasis in TNBC at early stages often lacks noticeable symptoms, making it challenging to detect. Near-infrared II (NIR-II) fluorescence microscopic imaging obtains long wavelength, which enables reduced scattering, high spatial resolution and minimal autofluorescence, it is also a favorable imaging method for tumor diagnosis. PbS@CdS quantum dots (QDs) are one of the popular NIR-II fluorescence nanoprobes for well brightness. In this study, NIR-II emissive PbS@CdS QDs were utilized and further encapsulated with thiol-terminated poly(ethylene oxide) (SH-PEG, MW = 5000) to form PbS@CdS@PEG QDs nanoparticles (NPs). The obtained PbS@CdS@PEG QDs NPs were then characterized and further studied in detail. The PbS@CdS@PEG QDs NPs had large absorption spectra, exhibited strong NIR-II fluorescence emission at approximately 1300nm, and possessed good NIR-II fluorescence properties. Then, the mice model of early-stage brain metastases of TNBC was established, and the PbS@CdS@PEG QDs NPs were injected into the tumor-bearing mice for NIR-II fluorescence microscopic bioimaging. The brain vessels and tumors of the living mice were detected with high spatial resolution under the NIR-II fluorescence microscopic imaging system with irradiation of 808nm laser. The tumor tissues were further restricted and prepared as thin slices. The NIR-II fluorescence signals were collected from the tumor slices with high spatial resolution and signal-to-background ratio (SBR). Thus, the PbS@CdS@PEG QDs NPs-assisted NIR-II fluorescence microscopic system can effectively achieve targeting brain metastases of TNBC imaging, offering a novel and promising approach for TNBC-specific diagnosis.展开更多
This letter presents an autofocus （AF） method to position a high-magnification microscope lens that automatically captures hundreds of images from a single moving slide. These images are taken by a mobile clinic uni...This letter presents an autofocus （AF） method to position a high-magnification microscope lens that automatically captures hundreds of images from a single moving slide. These images are taken by a mobile clinic unit in a rural location, and are later automatically processed and revised by a remote specialist. This process requires high focus precision to enable image processing techniques to achieve proper results. Low focusing times are also required for the system to be operative. We propose a novel method that combines two focus measures with an adapted searching scheme to cope with both constraints.展开更多
Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more...Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.展开更多
A new polarization–interference biomedical diagnostic three-dimensional(3D)Jones-matrix technology with digital Fourier reconstruction of layered maps of optical anisotropy(thesiograms)of dehydrated films(facies)of b...A new polarization–interference biomedical diagnostic three-dimensional(3D)Jones-matrix technology with digital Fourier reconstruction of layered maps of optical anisotropy(thesiograms)of dehydrated films(facies)of biological fluids of human organs is presented and experimentally tested.An original model of layered phase scanning of polycrystalline architectonics of supramolecular networks of biological fluid facies is proposed for the purpose of theoretical justification and prognostic use of the obtained results.On its basis,algorithms of Jones-matrix reconstruction of thesiograms of birefringence and dichroism of facies of synovial fluid,bile and blood are found.As a result,layered thesiograms of linear and circular birefringence and dichroism of facies with different spatial–angular architectonics of supramolecular networks are experimentally obtained for the first time.Within the framework of statistical analysis of experimental data,new objective markers(asymmetry and excess of optical anisotropy parameter distributions)for diagnostics of pathological changes in the optical anisotropy of biological fluid facies were defined and clinically tested.As a result,an excellent level of balanced accuracy of the developed polarization–interference Jones-matrix method of layer-by-layer reconstruction of thesiograms of polycrystalline supramolecular networks in differential diagnostics of bile facies(cholelithiasis),synovial fluid(reactive synovitis–septic arthritis)and whole blood(follicular adenoma–papillary thyroid cancer)was achieved.展开更多
Optimal vision and ergonomics are essential factors contributing to the achievement of good results during microsurgery.The three-dimensional(3D)digital image microscope system with a better 3D depth of field can rele...Optimal vision and ergonomics are essential factors contributing to the achievement of good results during microsurgery.The three-dimensional(3D)digital image microscope system with a better 3D depth of field can release strain on the surgeon's neck and back,which can improve outcomes in microsurgery.We report a randomized prospective study of vasoepididymostomy and vasovasostomy using a 3D digital image microscope system(3D-DIM)in rats.A total of 16 adult male rats were randomly divided into two groups of 8 each:the standard operating microscope(SOM)group and the 3D-DIM group.The outcomes measured included the operative time,real-time postoperative mechanical patency,and anastomosis leakage.Furthermore,a user-friendly microscope score was designed to evaluate the ergonomic design and equipment characteristics of the microscope.There were no differences in operative time between the two groups.The real-time postoperative mechanical patency rates were 100.0%for both groups.The percentage of vasoepididymostomy anastomosis leakage was 16.7%in the SOM group and 25.0%in the 3D-DIM group;however,no vasovasostomy anastomosis leakage was found in either group.In terms of the ergonomic design,the 3D-DIM group obtained better scores based on the surgeon's feelings;in terms of the equipment characteristics,the 3D-DIM group had lower scores for clarity and higher scores for flexibility and adaptivity.Based on our randomized prospective study in a rat model,we believe that the 3D-DIM can improve surgeon comfort without compromising outcomes in male infertility reconstructive microsurgery,so the 3D-DIM might be widely used in the future.展开更多
Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the ch...Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution,which signicantly affects the absorption and reflection of light,the spectral feature is proved to be important for leukocytes classication and identication.This paper proposes an accurate identication method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging(HSI)technology which combines the spectral information.The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm.Then,the spectral features are extracted and combined with the spatial features.Based on this,the support vector machine(SVM)is applied for classication ofve types of leukocytes and abnormal leukocytes.Compared with different classication methods,the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes,improving the accuracy in the classication and identication of leukocytes.This paper only selects one subtype of ALL for test,and the proposed method can be applied for detection of other leukemia in the future.展开更多
A leukocyte image fast scanning method based on max min distance clustering is proposed.Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood,there will not be any leukocyte i...A leukocyte image fast scanning method based on max min distance clustering is proposed.Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood,there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power(100^(x))objective.Due to the larger field of view of low power(10^(x))objective,the captured low power blood smear images can be used to locate leukocytes.All of the located positions make up a specific routine,if we scan the blood smear along this routine with high power objective,there will be definitely leukocytes in almost all of the captured images.Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more,a leukocyte clustering method based on max-min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes.This method can improve the scanning eficiency obviously.The experimental results show that the proposed method can shorten scanning time from 8.0-14.0min to 2.54.0 min while extracting 110 nonredundant individual high power leukocyte images.展开更多
In dendroclimatology,tree ring chronology is ordinarily established to reveal the fluctuation law of climate change on the interannual,interdecadal,and centennial scales.However,since traditional dendrochronology can ...In dendroclimatology,tree ring chronology is ordinarily established to reveal the fluctuation law of climate change on the interannual,interdecadal,and centennial scales.However,since traditional dendrochronology can only use one variable(tree ring width)to reflect environmentally related information,this causes the richer information recorded in the tree rings to be discarded.In this study,we examined the potential of hyperspectral chronological indices(shortened as“hyperspectral index/indices”)with samples collected in Shennongjia woodland in central China.The correlation analysis of the tree ring series on different samples indicated that hyperspectral indices outperform the traditional width index in chronology statistics including Signal-to-noise Ratio(SNR)and Expressed Population Signal(EPS).The reliability test shows that hyperspectral chronologies have more periods reaching the threshold of EPS or Subsample Signal Strength(SSS)>0.85,which means that hyperspectral chronologies provide more reliable periods for accurate climate reconstruction.Based on this,chronologies built by the three dendroclimatic indices were used to reconstruct the average temperature changes in Shennongjia over the last 103 years.The reconstruction results indicate that in our study area,the traditional width index model failed the split-sample calibration test and exhibited a low reconstruction accuracy,while the hyperspectral index model has a higher explained variance of 46.4%(p<0.01),compared to the width index(21.4%)and the grayscale index(38.3%).Our research results show that hyperspectral indices have greater potential for climate reconstruction in regions with lower susceptibility to climate stress.This is attributed to their ability to effectively extract subtle climate signals from the spectral variations on the surface of tree rings.Such ring spectral changes may be caused by complex and currently unknown responses of the trees to the climate.展开更多
Metalenses have exhibited significant promise across various applications due to their ultrathin,lightweight,and flat architecture,which allows for integration with microelectronic devices.However,their overall imagin...Metalenses have exhibited significant promise across various applications due to their ultrathin,lightweight,and flat architecture,which allows for integration with microelectronic devices.However,their overall imaging capabilities,particularly in microscopy,are hindered by substantial off-axis aberrations that limit both the field of view(FOV)and resolution.To address these issues,we introduce a meta-microscope that utilizes a metalens doublet incorporated with annular illumination,enabling wide FOV and high-resolution imaging in a compact design.The metalens-doublet effectively mitigates off-axis aberrations,whereas annular illumination boosts resolution.To validate this design,we constructed and tested the meta-microscope system,attaining a record resolution of 310 nm(for metalens image)with a 150μm FOV at 470 nm wavelength.Moreover,by utilizing the integration of metasurface,we implemented a compact prototype achieving an impressive 1-mm FOV with a resolution of 620 nm.Our experimental results demonstrate high-quality microscopic bio-images that are comparable to those obtained from traditional microscopes within a compact prototype,highlighting its potential applications in portable and convenient settings,such as biomedical imaging,mobile monitoring,and outdoor research.展开更多
In order to determine the structures of Si（111）-√7 √3-In surfaces and to understand their electronic properties, we construct six models of both hexagonal and rectangular types and perform first-principles calcula...In order to determine the structures of Si（111）-√7 √3-In surfaces and to understand their electronic properties, we construct six models of both hexagonal and rectangular types and perform first-principles calculations. Their scanning tunneling microscopic images and work functions are simulated and compared with experimental results. In this way, the hex-H3＇ and rect-T1 models are identified as the experimental configurations for the hexagonal and rectangular types, respectively. The structural evolution mechanism of the In/Si（lll） surface with indium coverage around 1.0 monolayer is discussed. The 4×1 and -√7× √3 phases are suggested to have two different types of evolution mechanisms, consistent with experimental results.展开更多
This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blur...This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.展开更多
Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transformi...Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.展开更多
The discoveries of so-called quasicrystals have broken through the theoretic foundation set up by the classical crystallographic group theory since 1891 and proposed new topics for study of solid structures. Electron ...The discoveries of so-called quasicrystals have broken through the theoretic foundation set up by the classical crystallographic group theory since 1891 and proposed new topics for study of solid structures. Electron diffraction patterns (EDP' s) and high-resolution microscopic (HREM) images have proved invaluable tools of studying the structures of crystals. The recognition and determination of EDP's and HREM images of a real-structure play a key role for understanding the structure. This paper will introduce some new developments about crystallographic group theory and new image processing methods on EDP's and HREM images. Contrary to popular beliefs, the research shows that quasicrystals can be understood (perturbed) complex periodic structures.展开更多
Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Pred...Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks.Human hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab.Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept.Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells.Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data.The proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in vitro.The developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other approaches.The high performance reveals the effectiveness of the approach.It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.展开更多
Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce ...Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce the mortality rate.In this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)method.Identifying different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture.Ten thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS.Results prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of specificity.The overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.展开更多
Objective:Renal microvascular injury,as the result of diabetic toxicity,plays a vital role in the pathogenesis of diabetic kidney disease(DKD)during diabetes progression.Here,we investigated whether electroacupuncture...Objective:Renal microvascular injury,as the result of diabetic toxicity,plays a vital role in the pathogenesis of diabetic kidney disease(DKD)during diabetes progression.Here,we investigated whether electroacupuncture(EA)could ameliorate renal microvascular impairment to prevent DKD and its underlying mechanism.Methods:The male db/db mice with Leprdb mutation were used as the model of type 2 diabetes mellitusinduced DKD and treated with EA at"Zusanli(ST36)"and"Weiwanxiashu(EX-B3)"acupoints for 4 weeks.Age-matched wild-type mice were used as control group.Renal protection of EA was evaluated by mouse urine production,water consumption,renal index and tubules dilation.Two-photon microscope imaging was applied to visualize renal microvascular blood flow in vivo.Immunostaining and western blot analysis were used to detect the glomerular alternations and inflammatory signaling.Results:EA significantly attenuated renal dysfunction in db/db mice.The protective effect of EA on renal microvascular recovery was observed both in function and structure analysis.Firstly,EA restored the renal microvascular blood flow in db/db mice.Then,glomerular hypertrophy and glomerular barrier destruction were suppressed after EA,as respectively demonstrated by the reduction of glomerular dilation,Collagen IV and Claudin-1 deposits.In mechanism,EA suppressed the diabetes-induced inflammatory response in renal microvessels,presenting as the downregulation of inflammatory cytokines interleukin-1β(IL-1β)and tumor necrosis factor(TNF-α),intercellular cell adhesion molecule-1(ICAM-1)activation,and macrophage infiltration after EA treatment.Conclusion:These findings indicated the benefits of EA against renal microvascular impairment and DKD progression,which was associated with the action of anti-inflammation,and supported EA as a promising modality for DKDmanagement.展开更多
Nonlinear optics,which is a subject for studying the interaction between intense light and materials,has great impact on various research fields.Since many structures in biological tissues exhibit strong nonlinear opt...Nonlinear optics,which is a subject for studying the interaction between intense light and materials,has great impact on various research fields.Since many structures in biological tissues exhibit strong nonlinear optical effects,nonlinear optics has been widely applied in biomedical studies.Especially in the aspect of bio-imaging,nonlinear optical techniques can provide rapid,label-free and chemically specific imaging of biological samples,which enable the investigation of biological processes and analysis of samples beyond other microscopy techniques.In this review,we focus on the introduction of nonlinear optical processes and their applications in bio-imaging as well as the recent advances in this filed.Our perspective of this field is also presented.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.52022053 and 52009073)the Natural Science Foundation of Shandong Province(Grant No.ZR201910270116).
文摘An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.
文摘Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood cell.In the marrow of human bones,leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC,and if any cell gets blasted,then it may become a cause of death.Therefore,the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives.Subsequently,in terms of detection,image segmentation techniques play a vital role,and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia(ALL)type of blood cancer.Moreover,the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus,which is well-known as the Region Of Interest(ROI).As a result,in this article,we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios,including K-means,FCM(Fuzzy Cmeans),K-means with FFA(Firefly Algorithm),and FCM with FFA.Also,we determine the most effective method of blood cell nucleus segmentation,which we subsequently use for the Leukaemia classification model.Finally,using the Convolution Neural Network(CNN)as a classifier,we developed a leukaemia cancer classification model from the microscopic images.The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art.In terms of experimental analysis,we observed that the accuracy of the model is near to 99%,and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.
文摘In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method or the X-ray powder crystal diffraction method cannot accurately determine the rock. An X-ray powder diffraction method combined with thin-film microscopic image technique and rock identification method was proposed. The X-ray powder diffraction method was combined with the thin-film microscopic image technique to identify the rock, and the microscopic image technique was used to determine the rock. The particle size, structure, shape, mineral color and structure, determine the type of rock, and then determine the mineral and mineral content of the rock by X-ray powder diffraction method, name the rock, and complete the identification of the rock. The experimental results show that the X-ray powder diffraction method or the thin-film microscopic image technique can not accurately determine the rock and combine the X-ray powder diffraction method with the thin-film microscopic image technology to identify the rock. Improve the accuracy of rock identification results.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant Nos.62035011,82202220 and 82060326State Key Laboratory of Pathogenesis,Prevention and treat ment of High Incident Diseases in central Asia(Nos.SKL-HIDCA-2022-3 and SKL-HIDCA-2022-GJ1)+3 种基金the Xinjiang Uygur Autonomous Region Regional Collaborative Innovation Special Science and Technology Assistance Program(No.2022E02130)Xinjiang Uygur Autonomous Region Natural Sci ence Foundation Key Project(No.2022D01D40)Outstanding Youth Project(2023D01E06)Y.Gao and C.Zhang authors contributed equally to this work.
文摘Triple-negative breast cancer (TNBC) is an aggressive and often fatal disease, especially since the brain metastasis of TNBC has been a particularly severe manifestation. However, brain metastasis in TNBC at early stages often lacks noticeable symptoms, making it challenging to detect. Near-infrared II (NIR-II) fluorescence microscopic imaging obtains long wavelength, which enables reduced scattering, high spatial resolution and minimal autofluorescence, it is also a favorable imaging method for tumor diagnosis. PbS@CdS quantum dots (QDs) are one of the popular NIR-II fluorescence nanoprobes for well brightness. In this study, NIR-II emissive PbS@CdS QDs were utilized and further encapsulated with thiol-terminated poly(ethylene oxide) (SH-PEG, MW = 5000) to form PbS@CdS@PEG QDs nanoparticles (NPs). The obtained PbS@CdS@PEG QDs NPs were then characterized and further studied in detail. The PbS@CdS@PEG QDs NPs had large absorption spectra, exhibited strong NIR-II fluorescence emission at approximately 1300nm, and possessed good NIR-II fluorescence properties. Then, the mice model of early-stage brain metastases of TNBC was established, and the PbS@CdS@PEG QDs NPs were injected into the tumor-bearing mice for NIR-II fluorescence microscopic bioimaging. The brain vessels and tumors of the living mice were detected with high spatial resolution under the NIR-II fluorescence microscopic imaging system with irradiation of 808nm laser. The tumor tissues were further restricted and prepared as thin slices. The NIR-II fluorescence signals were collected from the tumor slices with high spatial resolution and signal-to-background ratio (SBR). Thus, the PbS@CdS@PEG QDs NPs-assisted NIR-II fluorescence microscopic system can effectively achieve targeting brain metastases of TNBC imaging, offering a novel and promising approach for TNBC-specific diagnosis.
基金supported by the CONACYT/204212the DGEST of the Mexican Government under the PROMEP/107.5/10/5417
文摘This letter presents an autofocus （AF） method to position a high-magnification microscope lens that automatically captures hundreds of images from a single moving slide. These images are taken by a mobile clinic unit in a rural location, and are later automatically processed and revised by a remote specialist. This process requires high focus precision to enable image processing techniques to achieve proper results. Low focusing times are also required for the system to be operative. We propose a novel method that combines two focus measures with an adapted searching scheme to cope with both constraints.
基金supported by National Natural Science Foundation of China(No.62101040).
文摘Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.
文摘A new polarization–interference biomedical diagnostic three-dimensional(3D)Jones-matrix technology with digital Fourier reconstruction of layered maps of optical anisotropy(thesiograms)of dehydrated films(facies)of biological fluids of human organs is presented and experimentally tested.An original model of layered phase scanning of polycrystalline architectonics of supramolecular networks of biological fluid facies is proposed for the purpose of theoretical justification and prognostic use of the obtained results.On its basis,algorithms of Jones-matrix reconstruction of thesiograms of birefringence and dichroism of facies of synovial fluid,bile and blood are found.As a result,layered thesiograms of linear and circular birefringence and dichroism of facies with different spatial–angular architectonics of supramolecular networks are experimentally obtained for the first time.Within the framework of statistical analysis of experimental data,new objective markers(asymmetry and excess of optical anisotropy parameter distributions)for diagnostics of pathological changes in the optical anisotropy of biological fluid facies were defined and clinically tested.As a result,an excellent level of balanced accuracy of the developed polarization–interference Jones-matrix method of layer-by-layer reconstruction of thesiograms of polycrystalline supramolecular networks in differential diagnostics of bile facies(cholelithiasis),synovial fluid(reactive synovitis–septic arthritis)and whole blood(follicular adenoma–papillary thyroid cancer)was achieved.
基金This work was supported by grants from the National Nature Science Foundation of China(81701524,81871215)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA16020701)the National Key R&D Program of China(2017YFC1002003).
文摘Optimal vision and ergonomics are essential factors contributing to the achievement of good results during microsurgery.The three-dimensional(3D)digital image microscope system with a better 3D depth of field can release strain on the surgeon's neck and back,which can improve outcomes in microsurgery.We report a randomized prospective study of vasoepididymostomy and vasovasostomy using a 3D digital image microscope system(3D-DIM)in rats.A total of 16 adult male rats were randomly divided into two groups of 8 each:the standard operating microscope(SOM)group and the 3D-DIM group.The outcomes measured included the operative time,real-time postoperative mechanical patency,and anastomosis leakage.Furthermore,a user-friendly microscope score was designed to evaluate the ergonomic design and equipment characteristics of the microscope.There were no differences in operative time between the two groups.The real-time postoperative mechanical patency rates were 100.0%for both groups.The percentage of vasoepididymostomy anastomosis leakage was 16.7%in the SOM group and 25.0%in the 3D-DIM group;however,no vasovasostomy anastomosis leakage was found in either group.In terms of the ergonomic design,the 3D-DIM group obtained better scores based on the surgeon's feelings;in terms of the equipment characteristics,the 3D-DIM group had lower scores for clarity and higher scores for flexibility and adaptivity.Based on our randomized prospective study in a rat model,we believe that the 3D-DIM can improve surgeon comfort without compromising outcomes in male infertility reconstructive microsurgery,so the 3D-DIM might be widely used in the future.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61975056 and 61901173)the Shanghai Natural Science Foundation(Grant No.19ZR1416000)the Science and Technology Commission of Shanghai Municipality(Grant Nos.14DZ2260800 and 18511102500).
文摘Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution,which signicantly affects the absorption and reflection of light,the spectral feature is proved to be important for leukocytes classication and identication.This paper proposes an accurate identication method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging(HSI)technology which combines the spectral information.The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm.Then,the spectral features are extracted and combined with the spatial features.Based on this,the support vector machine(SVM)is applied for classication ofve types of leukocytes and abnormal leukocytes.Compared with different classication methods,the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes,improving the accuracy in the classication and identication of leukocytes.This paper only selects one subtype of ALL for test,and the proposed method can be applied for detection of other leukemia in the future.
基金supported by the 863 National Plan Foundation of China under Grant No.2007AA01Z333 and Special Grand National Project of China under Grant No.2009ZX02204-008.
文摘A leukocyte image fast scanning method based on max min distance clustering is proposed.Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood,there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power(100^(x))objective.Due to the larger field of view of low power(10^(x))objective,the captured low power blood smear images can be used to locate leukocytes.All of the located positions make up a specific routine,if we scan the blood smear along this routine with high power objective,there will be definitely leukocytes in almost all of the captured images.Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more,a leukocyte clustering method based on max-min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes.This method can improve the scanning eficiency obviously.The experimental results show that the proposed method can shorten scanning time from 8.0-14.0min to 2.54.0 min while extracting 110 nonredundant individual high power leukocyte images.
基金supported by the National Natural Science Foundation of China(NSFC)Projects[grant numbers 42271476 and 41771227]Key Technology Projects of the Hubei Provincial Company of the China National Tobacco Corporation(grant number 027Y2021-020 and 027Y2022-006)Young Scholar of Wuhan University 351 Talent Program[grant number 202017].
文摘In dendroclimatology,tree ring chronology is ordinarily established to reveal the fluctuation law of climate change on the interannual,interdecadal,and centennial scales.However,since traditional dendrochronology can only use one variable(tree ring width)to reflect environmentally related information,this causes the richer information recorded in the tree rings to be discarded.In this study,we examined the potential of hyperspectral chronological indices(shortened as“hyperspectral index/indices”)with samples collected in Shennongjia woodland in central China.The correlation analysis of the tree ring series on different samples indicated that hyperspectral indices outperform the traditional width index in chronology statistics including Signal-to-noise Ratio(SNR)and Expressed Population Signal(EPS).The reliability test shows that hyperspectral chronologies have more periods reaching the threshold of EPS or Subsample Signal Strength(SSS)>0.85,which means that hyperspectral chronologies provide more reliable periods for accurate climate reconstruction.Based on this,chronologies built by the three dendroclimatic indices were used to reconstruct the average temperature changes in Shennongjia over the last 103 years.The reconstruction results indicate that in our study area,the traditional width index model failed the split-sample calibration test and exhibited a low reconstruction accuracy,while the hyperspectral index model has a higher explained variance of 46.4%(p<0.01),compared to the width index(21.4%)and the grayscale index(38.3%).Our research results show that hyperspectral indices have greater potential for climate reconstruction in regions with lower susceptibility to climate stress.This is attributed to their ability to effectively extract subtle climate signals from the spectral variations on the surface of tree rings.Such ring spectral changes may be caused by complex and currently unknown responses of the trees to the climate.
基金the Micro-fabrication Center of the National Laboratory of Solid State Microstructures(NLSSM)for technique supportfinancial support from the National Key Research and Development Program of China(Grant Nos.2024YFA1012600 and 2022YFA1404301)+1 种基金National Natural Science Foundation of China(Grant Nos.62325504,62305149,92250304,and 62288101)Dengfeng Project B of Nanjing University。
文摘Metalenses have exhibited significant promise across various applications due to their ultrathin,lightweight,and flat architecture,which allows for integration with microelectronic devices.However,their overall imaging capabilities,particularly in microscopy,are hindered by substantial off-axis aberrations that limit both the field of view(FOV)and resolution.To address these issues,we introduce a meta-microscope that utilizes a metalens doublet incorporated with annular illumination,enabling wide FOV and high-resolution imaging in a compact design.The metalens-doublet effectively mitigates off-axis aberrations,whereas annular illumination boosts resolution.To validate this design,we constructed and tested the meta-microscope system,attaining a record resolution of 310 nm(for metalens image)with a 150μm FOV at 470 nm wavelength.Moreover,by utilizing the integration of metasurface,we implemented a compact prototype achieving an impressive 1-mm FOV with a resolution of 620 nm.Our experimental results demonstrate high-quality microscopic bio-images that are comparable to those obtained from traditional microscopes within a compact prototype,highlighting its potential applications in portable and convenient settings,such as biomedical imaging,mobile monitoring,and outdoor research.
基金V. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China （No.20603032, No.20733004, No.21121003, No.91021004, No.20933006）, the National Key Basic Research Program （No.2011CB921400）, the Foundation of National Excellent Doctoral Dissertation of China （No.200736）, the Fundamental Research Funds for the Central Universities （No.WK2340000006 and No.WK2060140005）, and the Shanghai Supercompurer Center, the USTC-HP HPC Project, and the SCCAS.
文摘In order to determine the structures of Si（111）-√7 √3-In surfaces and to understand their electronic properties, we construct six models of both hexagonal and rectangular types and perform first-principles calculations. Their scanning tunneling microscopic images and work functions are simulated and compared with experimental results. In this way, the hex-H3＇ and rect-T1 models are identified as the experimental configurations for the hexagonal and rectangular types, respectively. The structural evolution mechanism of the In/Si（lll） surface with indium coverage around 1.0 monolayer is discussed. The 4×1 and -√7× √3 phases are suggested to have two different types of evolution mechanisms, consistent with experimental results.
文摘This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.
基金funding support from the US National Science Foundation(2229092)supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a program of Schmidt Sciences,LLC.
文摘Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.
文摘The discoveries of so-called quasicrystals have broken through the theoretic foundation set up by the classical crystallographic group theory since 1891 and proposed new topics for study of solid structures. Electron diffraction patterns (EDP' s) and high-resolution microscopic (HREM) images have proved invaluable tools of studying the structures of crystals. The recognition and determination of EDP's and HREM images of a real-structure play a key role for understanding the structure. This paper will introduce some new developments about crystallographic group theory and new image processing methods on EDP's and HREM images. Contrary to popular beliefs, the research shows that quasicrystals can be understood (perturbed) complex periodic structures.
基金The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No.R-2021-152.
文摘Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks.Human hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab.Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept.Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells.Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data.The proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in vitro.The developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other approaches.The high performance reveals the effectiveness of the approach.It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.
文摘Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce the mortality rate.In this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)method.Identifying different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture.Ten thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS.Results prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of specificity.The overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.
基金Supported by National Natural Science Foundation of China:82274628Natural Science Foundation of Guangdong Province:2023A1515030167Discipline Collaborative Innovation Team Program of Double First-class and High-level Universities for Guangzhou University of Chinese Medicine:2021XK01。
文摘Objective:Renal microvascular injury,as the result of diabetic toxicity,plays a vital role in the pathogenesis of diabetic kidney disease(DKD)during diabetes progression.Here,we investigated whether electroacupuncture(EA)could ameliorate renal microvascular impairment to prevent DKD and its underlying mechanism.Methods:The male db/db mice with Leprdb mutation were used as the model of type 2 diabetes mellitusinduced DKD and treated with EA at"Zusanli(ST36)"and"Weiwanxiashu(EX-B3)"acupoints for 4 weeks.Age-matched wild-type mice were used as control group.Renal protection of EA was evaluated by mouse urine production,water consumption,renal index and tubules dilation.Two-photon microscope imaging was applied to visualize renal microvascular blood flow in vivo.Immunostaining and western blot analysis were used to detect the glomerular alternations and inflammatory signaling.Results:EA significantly attenuated renal dysfunction in db/db mice.The protective effect of EA on renal microvascular recovery was observed both in function and structure analysis.Firstly,EA restored the renal microvascular blood flow in db/db mice.Then,glomerular hypertrophy and glomerular barrier destruction were suppressed after EA,as respectively demonstrated by the reduction of glomerular dilation,Collagen IV and Claudin-1 deposits.In mechanism,EA suppressed the diabetes-induced inflammatory response in renal microvessels,presenting as the downregulation of inflammatory cytokines interleukin-1β(IL-1β)and tumor necrosis factor(TNF-α),intercellular cell adhesion molecule-1(ICAM-1)activation,and macrophage infiltration after EA treatment.Conclusion:These findings indicated the benefits of EA against renal microvascular impairment and DKD progression,which was associated with the action of anti-inflammation,and supported EA as a promising modality for DKDmanagement.
基金the National Natural Science Foundation of China(61722508/61525503/61620106016/61835009/61935012/61961136005)(Key)Project of Department of Education of Guangdong Province(2016KCXTD007)Shenzhen Basic Research Project(JCYJ20180305124902165).
文摘Nonlinear optics,which is a subject for studying the interaction between intense light and materials,has great impact on various research fields.Since many structures in biological tissues exhibit strong nonlinear optical effects,nonlinear optics has been widely applied in biomedical studies.Especially in the aspect of bio-imaging,nonlinear optical techniques can provide rapid,label-free and chemically specific imaging of biological samples,which enable the investigation of biological processes and analysis of samples beyond other microscopy techniques.In this review,we focus on the introduction of nonlinear optical processes and their applications in bio-imaging as well as the recent advances in this filed.Our perspective of this field is also presented.
