The viable community of microorganisms in the rhizosphere significantly impacts the physiological development and vitality of plants. The assembly and functional capacity of the rhizosphere microbiome are greatly influenced by various factors within the rhizosphere. The primary factors are the host plant genotype, developmental stage and status, soil properties, and resident microbiota. These factors drive the composition, dynamics, and activity of the rhizosphere microbiome. This review addresses the intricate interplay between these factors and how it facilitates the recruitment of specific microbes by the host plant to support plant growth and resilience under stress. This review also explores current methods for engineering and manipulating the rhizosphere microbiome, including host plant-mediated manipulation, soil-related methods, and microbe-mediated methods. Advanced techniques to harness the plant's ability to recruit useful microbes and the promising use of rhizo-microbiome transplantation are highlighted. The goal of this review is to provide valuable insights into the current knowledge, which will facilitate the development of cutting-edge strategies for manipulating the rhizosphere microbiome for enhanced plant growth and stress tolerance. The article also indicates promising avenues for future research in this field. A. koreana has been extensively studied to explore the causes of their decline and to suggest possible intervention strategies to preserve the tree population . These fungi significantly influence the regulation of plant root exudates. Plants recruit and enhance the colonization with AMF through root exudates, mainly flavonoids  on the microbiota composition of the tree rhizosphere. The evident impact of Ca. L. asiaticus on the below-ground microbial composition and functional potential is linked to the strong influence of the phytopathogen on the petitioning of photo-assimilates and the changes in root exudation. Several studies involving the model plant Arabidopsis thaliana have confirmed the involvement of above-ground pathogenic infection in manipulating the rhizosphere microbial community. The finding reflects the influence of the root exudate pattern for the recruitment of specific beneficial microbes, which enhances plant defense against infection. Rudrappa et al. . The three enriched bacteria were isolated and demonstrated to synergistically colonize the Arabidopsis roots to promote growth and induce systemic resistance against downy mildew. The infection in the first plant population increased the resistance against the same pathogen in subsequent plants grown in the same soils, confirming the soil legacy effect  rapidly increased in relative abundance following whitefly infestation, which could contribute to plant protection  plant.The more we understand about the complex interaction between and among plants, beneficial microbiota, and pathogens, the more we realize the sophisticated levels of organization and synchronization within plant ecosystems. The intriguing findings suggesting the possible signaling between neighboring plants upon pathogenic infection could be seen as evidence of co-evolutionary crosstalk synchronization. In this scenario, upon infection with a pathogen (under attack), a plant produces messenger alerting molecules (volatiles) to neighboring plants. The latter plants receive the alerting signals and respond by assembling a defensive rhizosphere microbiota for protection against the pathogen. This scenario is supported by several results, confirming the hypothesis of plant distal rhizosphere microbiota recruitment  proteins, phenolics, alkaloids, chitinases, and other hydrolytic enzymes that contribute to enhancing plant defense against pathogens, parasites, and insects  against Fusarium oxysporum (the cause of root disease), the rhizosphere microbiome composition and functional capacity shifted significantly between susceptible and resistant cultivars. The findings of the greater abundance in resistant cultivars of specific rhizosphere-competent bacterial taxa, such as pseudomonadaceae, bacillaceae, solibacteraceae, and cytophagaceae, indicate their roles in providing complementary protection against fungal infections . These compounds are exclusively utilized by the bacteria as nitrogen, carbon, phosphorus, and sulfur sources, providing a competitive advantage over other soil or root endophytic microbes  to rhizosphere bacteria. The established synthetic rhizopine-mediated signaling could potentially be utilized for targeted regulation of the composition and gene expression in the rhizosphere microbiome for the delivery of beneficial traits to plants  studies and quantitative trait locus (QTL), have been applied to map the potential genetic background for host plant selection of the rhizosphere microbiome. Deng et al. , demonstAgrobacterium rhizogenes-mediated hairy root transformation in melon to reveal the involvement of a root-secreted metabolite (cucurbitacin B) in the selective enrichment of Bacillus and Enterobacter in the rhizosphere. These microbes confer resistance against the soil-borne fungal pathogen Fusarium oxysporum. The findings of this study provide applicable evidence of the targeted manipulation of plant genomes for improved plant fitness by the indirect influence on the rhizosphere microbiome composition. The findings also pave the way for prospective host-mediated rhizosphere microbiome engineering applications on other crops. The use of plant genome editing tools to target traits involved in rhizosphere microbiome assembly and plant–microbe interactions has immense potential and could contribute significantly to the next green revolution for crop productivity and stress tolerance.Advanced genome editing tools, such as zinc finger nucleases, site-specific transcription activator-like effector nucleases, and the CRISPR/Cas system, could be utilized for the manipulation of key genes in the plant genome involved in biosynthesis and metabolism, which are responsible for the recruitment of desirable rhizosphere microbes  support plant growth, antagonize pathogens, and maintain a healthy rhizosphere , Arthrobacter (a known PGBR for tomato), and Gaiella. The three are members of the core microbiome in suppressive soils. The beneficial recruitment and stimulation of such beneficial microbial taxa were suggested to contribute to priming plant defense and promote plant growth  and evaluated their potential to support the growth of Arabidopsis plants under drought stress conditions in whole microbiome transplantation experiments. The microbiome adapted to Arabidopsis improved drought tolerance in the target plants, as demonstrated by the increased plant biomass and decreased expression of drought response genes in Arabidopsis. Furthermore, microbiome composition analysis revealed that several bacterial genera with plant growth-promoting properties, including Burkholderia, Phormidium, Bacillus, and Aminobacter, were enriched in the Arabidopsis-adapted microbiome compared to the other microbiomes examined. These findings suggest the utility of whole rhizo-microbiome transplantation in supporting plant tolerance to environmental stress and highlight the importance of ensuring compatibility between the transplanted microbiome and the host plant.Increasing knowledge of the potential benefits of the rhizosphere microbiome on plant growth promotion and stress tolerance has spurred efforts to harness these benefits through the application of rhizo-microbiome transplantation techniques. Zolla et al.  sampled The utilization of microbiome transplantation to enhance plant survival in highly petroleum-contaminated soils was investigated by Yergeau et al. . Willow The potential of rhizo-microbiome transplantation to modulate plant physiological traits has also been investigated. Panke-Buisse et al.  evaluatein vitro assays were performed to explore the potential microbial taxa with suppressive abilities against Ralstonia solanacearum. The authors reported that only one of the six microbiomes used in the study was successful in rhizosphere microbiome transplantation. The suggested potential causes of unsuccessful transplantation included environmental, soil-related, and microbiome complexity-related factors. The findings of this study are a seminal contribution to the field, representing a shift from a trial-and-error-based approach to a more informed process for rhizosphere microbiome transplantation by characterizing the features that are necessary for successful transplantation (Jousset and Lee, Jiang et al.  recentlyClostridium difficile infection has a success rate of over 90% (Bakken et al., The phenomenon of transferring the entire microbiome, commonly referred to as microbiome transplantation, has been extensively studied in human microbiome research. The many purported benefits include, but are not limited to, the suppression of disease, modulation of immunity, and even reduction of obesity (Lee et al., The microbe-based approaches for rhizosphere microbiome engineering are summarized in The rhizosphere is a critical element for plant growth and health. The vital role of the rhizosphere microbiome in supporting plant growth and tolerance to stress has been experimentally confirmed. Recent advances in the metagenomic analysis have greatly improved our understanding of the diversity and complexity of microorganisms present in the rhizosphere, as well as their interactions with plants and other microorganisms. To maximize the benefits of rhizosphere-based methods, a thorough understanding of the factors that govern the assembly and functional capacity of the rhizosphere microbiome is necessary. These factors can be broadly categorized as host plant-related, soil-related, and microbe-related determinants.In addition to understanding the rhizosphere microbiome assembly, methods have been proposed to manipulate and engineer the microbiome to promote plant growth, adaptation, and survival under harsh conditions, or to enhance the tolerance to stress and disease. These methods can also be broadly categorized as host plant-related, soil-related, and microbe-related approaches. The rhizosphere microbiome engineering methods have shown considerable success and hold promise for improving the quality and productivity of agricultural crops. However, much remains to be understood about the specific mechanisms by which microorganisms interact with plants, and how these interactions can be harnessed to optimize plant growth and health. Further research is required to fully comprehend the complex interactions within the rhizosphere and to develop effective strategies for microbiome engineering. Additionally, the integration of sustainable and environmentally friendly agricultural practices that consider the rhizosphere microbiome can play a crucial role in achieving a more sustainable future.We believe that integrating the modulation of the different variants involved in the assembly and functionality of the rhizosphere microbiome, including host plant, soil-related factors, and resident microbiome and microbe–microbe interactions, will be crucial to improve the success rate of rhizosphere microbiome engineering and enhance the benefits to plants. Future research discoveries will help fully realize the potential of rhizosphere microbiome engineering in plants.MM and Y-SS conceived and designed the concept. IP and MM collected and performed literature data interpretation. IP, MM, and Y-SS wrote and revised the manuscript. All authors read and approved the manuscript.


Diseases such as diabetes affect the retinal vasculature and the health of the neural retina, leading to vision problems. We describe here an imaging method and analysis procedure that enables characterization of the retinal vessel walls with cellular-level resolution, potentially providing markers for eye diseases. Adaptive optics scanning laser ophthalmoscopy is used with a modified detection scheme to include four simultaneous offset aperture channels. The magnitude of the phase gradient derived from these offset images is used to visualize the structural characteristics of the vessels. The average standard deviation image provides motion contrast and enables segmentation of the vessel lumen. Segmentation of blood vessel walls provides quantitative measures of geometrical characteristics of the vessel walls, including vessel and lumen diameters, wall thickness, and wall-to-lumen ratio. Retinal diseases may affect the structural integrity of the vessel walls, their elasticity, their permeability, and their geometrical characteristics. The ability to measure these changes is valuable for understanding the vascular effects of retinal diseases, monitoring disease progression, and drug testing. In addition, loss of structural integrity of the blood vessel wall may result in microaneurysms, a hallmark lesion of diabetic retinopathy, which may rupture or leak and further create vision impairment. Early identification of such structural abnormalities may open new treatment avenues for disease management and vision preservation. Functional testing of retinal circuitry through high-resolution measurement of vasodilation as a response to controlled light stimulation of the retina (neurovascular coupling) is another application of our method and can provide an unbiased evaluation of one’s vision and enable early detection of retinal diseases and monitoring treatment results. The retina, the innermost light-sensitive layer of the eye fundus, is a highly metabolically active neurovascular tissue and is vulnerable to diseases that affect its neural or vascular components, thereby potentially resulting in severe vision loss. The retinal endothelium is responsible for the exchange of oxygen, nutrients, and waste. The retina is the tissue with the greatest metabolic demand in the body and since the retinal capillaries lack precapillary sphincters, they remain perfused continuously ,2; thereHyperglycemia, inflammation, and hypoxia are key factors of DM that contribute to retinal neurovascular coupling degeneration in eyes with diabetic retinopathy (DR) with a characteristic pericyte loss and alterations in the neuronal cells’ synapses. DR is the most common microvascular complication of DM and the leading cause of blindness among working-age adults in the US ,9. It isThe major arteries and veins of the eye are plainly visible by standard color fundus photography. However, the smallest retinal capillaries cannot be fully visualized by using this technique due to the low lateral resolution. One way to reveal more details of the retinal vasculature is the use of fluorescein angiography that requires the injection of a dye into a peripheral vein to enhance contrast. Although this technique can quantify retinal perfusion, it still lacks the resolution for revealing the finest capillaries and it is an invasive method that involves the risk of adverse effects due to the contrast agent. More recently, it was recognized that the motion contrast generated by the flow of erythrocytes through the blood vessels cause inter-frame intensity fluctuations, enabling visualization of the vessels; by plotting the standard deviation of each pixel between video frames, blood flow itself becomes a contrast agent . Images Optical coherence tomography angiography (OCTA), recently introduced into clinical practice ,13,14, pA new approach for imaging smaller arterioles and venules involves adaptive optics (AO) to increase image resolution and quality. Ocular aberrations due to imperfections in the cornea, lens, and tear film are measured with a wavefront sensor and are corrected by a wavefront compensator in a closed-loop control algorithm. Real-time measurement of ocular aberrations is generally performed using a Hartmann–Shack wavefront sensor comprising a lenslet array and a CCD camera , and theScanning laser ophthalmoscopes (SLOs) have been used for a long time to image the retina in a confocal configuration using a pinhole in front of the detector that enables light collection only from the retinal location conjugate to the pinhole. However, the eye can only produce a diffraction-limited illumination spot on the retina for a small pupil diameter less than ~2 mm, and SLOs can achieve a lateral resolution of approximately 8–10 µm. To increase the lateral resolution of the retinal image, the eye pupil needs to be dilated such that a larger diameter imaging beam (6–9 mm) can be used to enable a tighter focus on the retina. As the pupil diameter is increased, ocular aberrations dynamically deform the imaging beam and AO is needed to correct ocular aberrations in real time; therefore, AO-corrected SLOs have been developed (AO-SLOs) to produce diffraction limited illumination spots (~2.5 µm) on the retina  for the Nevertheless, due to a low refractive index contrast, the capillaries are relatively transparent and difficult to visualize even in high-resolution imaging of the retina as provided by AO-SLOs in a typical reflective confocal configuration. Only dim specular reflections from blood cells or the shadow cast on deeper layers can be seen sometimes. About a decade ago, a new method was introduced to take advantage of the multiply scattered light  and to enable visualization of the retinal capillaries with unprecedented details . By usinWe developed a multimodal AO retinal imager (MAORI) as a modular, compact, clinical prototype that allows researchers, in the clinic or the lab, to explore the fine cellular and lamellar retinal structure of subjects’ retinas with near-isotropic micron-level resolution. The multimodal system includes a scanning laser ophthalmoscope (SLO) with closed-loop AO wavefront sensing and compensation, and a spectral domain optical coherence tomography (SDOCT) channel. The OCT channel provides complimentary cross-sectional images (B-scans) simultaneously recorded and co-registered with the SLO image. The OCT beam is also the wavefront sensor (WS) beacon. A fixation display and a point spread function camera are also included, as shown in the optical diagram in Following the demonstration of offset aperture AO-SLO, a new technique, split-detector imaging , has beeA new detection scheme has been introduced recently  with an This was an IRB-approved observational study conducted at the Beetham Eye Institute of the Joslin Diabetes Center in Boston, evaluating adults with type 1 diabetes (T1D). The exclusion criteria included pupillary miosis or an inability to dilate, prior panretinal photocoagulation, non-diabetic retinal pathology, media opacities limiting the ability to acquire high-quality AOSLO/OCT images, and hypertension. Each study eye underwent mydriasis, ultrawide fundus photography (UWF), and AOSLO/OCT imaging in a single-visit study. DR severity was graded by certified graders on colored UWF based on the ETDRS classification system.An example of images obtained with the 5-fiber bundle including the confocal and the four offset channels is shown in A significant problem with split detection is its directionality artefact. If a blood vessel is parallel with the offset/split direction, it cannot be seen, as illustrated in the split-1 image within the white oval in Typically, a stack of 100–200 images is recorded at one location. The images are aligned using a non-rigid registration method , and oneSplit-detection analysis can be performed using multiple combinations of the four offset images. Two orthogonal split images are generated for fiber pairs 1–3 (split-1) and 2–4 (split-2) as illustrated in The horizontal and vertical splits are similar to the half aperture, as in . They caThe standard deviation (SD) among the four simultaneous offsets, a concept first introduced in , highligIt should be noted here that SD, STD, sum, and MPG are all isotropic, direction-independent, as they were derived from all four offset images and are therefore directionally diverse. STD reveals maps of capillaries with much better contrast than those in other imaging modalities and in a mode similar to OCTA. STD can also be used to segment the flow location in large vessels and to quantify the inside diameter of the vessels.Two orthogonal split-detector images are used to calculate the magnitude of the phase gradient, which highlights local variations in the refractive index. The gradient maxima indicate the location of structural changes (edges) associated with the blood vessel wall boundaries. STD images highlight the blood flow through motion contrast, and the edges of the flow shown in these images correspond to the lumen boundaries. Segmentation of the gradient maxima and of the STD images provides the inside and outside boundaries of the vessel walls, as illustrated in An example of the measured images (offset images im 1–4 and SLO) and derived images  is shown in The results shown in Diffraction-limited imaging, as in AO-SLO, is generally performed with a large diameter illumination beam  associated with the blood vessel wall boundaries, as shown in Segmentation of the MPG and STD images is currently performed semi-automatically using a custom-made Matlab program that identifies the gradient maxima in the MPG image and the borders of the flow in the STD image. Some gradient maxima such as the borders of the mural cells within the vessel wall, as can be seen in the top right image in The MPG and STD images were chosen for wall segmentation because they are isotropic, independent of the offset/split direction since they were derived from all four offset/split images. SD, SLO, and sum are also isotropic, as illustrated in The imaging method and the analysis procedure presented here enable the identification of structural abnormalities that are the result of disease, DR due to T1D in particular in this study. Loss of structural integrity of the blood vessel wall may result in MAs, a hallmark lesion of DR, which may leak or rupture and further cause visual decline. The AO imaging modality described here enables the visualization and characterization of retinal blood vessels with cellular resolution. The segmentation of blood vessel walls provides quantitative measures of geometrical characteristics of the vessel walls, including vessel and lumen diameters, wall thickness, and wall-to-lumen ratio. Retinal diseases such as DR affect the structural integrity of the vessel walls, their elasticity, their permeability, and their geometrical characteristics. The ability to measure disease-induced changes may prove valuable for understanding the vascular effects of retinal diseases, monitoring disease progression, and drug testing. A study relating WLR changes to the level of DR severity is ongoing. Early detection of such abnormal alterations may also open new treatment avenues for vision preservation.Functional testing of retinal circuitry can provide an unbiased evaluation of visual function, enable early detection of retinal diseases, and allow for monitoring of the response to treatment. Light stimulation evokes neuronal activity in the retina, resulting in the dilation of retinal blood vessels and increased blood flow. This response, named functional hyperemia or neurovascular coupling, brings additional oxygen and nutrients to active neurons and is one of the critical physiological mechanisms that allow for the adaptation of the retinal vascular system to changing neural function and increased metabolic demand. The analysis method described here enables future high-resolution measurement of vasodilation as a response to controlled light stimulation of the retina and may reveal functional alterations in diseased eyes that could warrant early interventions.


The prognosis and treatment of multiple myeloma (MM) has evolved greatly over the past decade. The development and incorporation of new agents such as immunomodulators and proteasome inhibitors into therapy has improved outcomes and is helping patients enjoy longer periods of remission.To review current treatments for MM, including overview of drug therapy and management of adverse effects of therapy and comorbidities. Additionally, an overview of agents being studied and evaluated for use in MM and myeloma-related conditions, such as metastatic bone disease and venous thromboembolism, will be discussed.Great strides have been made regarding the understanding of disease pathology in MM, leading to therapies that may be targeted to each individual, based on their unique biology of disease. Therapy is currently tailored based on patient issues and stage of disease, but may soon betailored individually based on the cytogenetic profile of a patient. Recent treatment guidelines have been published by the National Comprehensive Cancer Network which were updated with impressive results fromclinical trials involving agents such as immunomodulators and proteasome inhibitors. This guideline also provides information on the management of myeloma and treatment-related morbidities. As with the treatment of any cancer, clinicians must weigh risk versus benefit when determining the most appropriate therapy. Currently, corticosteroids, lenalidomide, thalidomide, and bortezomib are all used in patient swith MM. The use of chemotherapy, including high-dose therapy with stemcell transplant, is an important component of treatment for many patients.The use of high-dose therapy is continually being evaluated, and the issueof risk versus benefit is weighed for individual patients. Depending on the prognosis, it may be of benefit to endure the toxicity of higher doses toachieve a better overall response and achieve longer remission periods. Although stem cell transplantation is often performed in MM to improve survival and remission rates, some patients are unable to undergo transplant for a variety of reasons, including age (older than 65 years), comorbidities, and/or organ dysfunction.Newer drug therapies and combinations of therapy are being evaluated to better manage this population and patients who previously received high-dose chemotherapy and a stem-cell transplant. Additionally, the management of relapsed, or refractory, disease continues to be a challenge in treating the myeloma patient. Despite aggressive and improved treatments, most myeloma patients will eventually have resistance to therapyor relapse. Treatment strategies in these patients are also evolving.Major advancements in the diagnosis, staging, and treatmentof myeloma offer promise in the future for changing MM from a terminal illness into a chronic, manageable condition.


Inflammatory bowel disease (IBD) is a chronic and debilitating condition that impacts a substantial number of individuals globally. It manifests as inflammation within the gastrointestinal tract, resulting in a diverse array of symptoms such as abdominal pain, diarrhea, and rectal bleeding. The primary subtypes of IBD are Crohn’s disease (CD) and ulcerative colitis (UC).The complex and multifactorial relationship between IBD and mental health has become increasingly evident. Individuals with IBD are at increased risk of developing depression and anxiety, which can further worsen their symptoms and overall well-being. Psychological stress has been identified as a prevalent factor in IBD with detrimental effects on patients’ quality of life and potentially impacting disease progression. Moreover, fatigue is a significant clinical concern in IBD, affecting a substantial number of patients even during clinical remission. Fatigue not only impairs quality of life but also hampers work productivity and overall functioning. The management of IBD has faced considerable challenges during the COVID-19 pandemic, with restriction of hospitalizations and unprecedented redeployment of health care resources. This situation has been associated with increased psychological distress, particularly among patients with chronic diseases, including IBD.In this editorial, we aim to emphasize the importance of addressing psychological health in patients with IBD. We will explore the prevalence of depression, anxiety, and fatigue in these patients, shedding light on their impact on quality of life. Additionally, we will discuss the use of patient-reported outcomes (PROs) in IBD research and clinical evaluation, which have significantly contributed to our understanding of the psychological burden associated with the disease. Furthermore, we will examine the impact of the COVID-19 pandemic on the mental health and behavior of IBD patients. Finally, we will explore the future of psychological health in clinical practice and strategies to better address this crucial aspect of patient care.Anxiety and depression are common psychological symptoms among patients with IBD. Anxiety symptoms have a prevalence of 38.5%, and depression symptoms 22.8% , as seenPatient-reported outcomes (PROs) have become increasingly important in clinical trials and research studies. They provide valuable information about patients’ experiences with their disease and treatment, which can help inform clinical decision-making. The inclusion of PROs in IBD research has enhanced our understanding of the psychological impact of the disease and underscores the significance of addressing this aspect in patient care. Fatigue, a prevalent and debilitating symptom in IBD, is often overlooked and challenging to treat. Up to 80% of patients with active disease and 50% of those in remission report experiencing fatigue . FurtherThe COVID-19 pandemic has had a considerable impact on IBD patients by limiting their access to medical services due to restrictions and the reorganization of the healthcare systems, which affects their quality of life. Despite initial concerns about their vulnerability to SARS-CoV-2 infection, it has been found that the risk of severe COVID-19 is not increased in IBD patients . NeverthIt is crucial to acknowledge the potential mental health implications for patients with IBD and provide appropriate support and resources. Healthcare providers should consider the holistic needs of patients, including their mental well-being, and ensure access to mental health services, telemedicine options, patient education initiatives, and other resources to help manage the psychosocial impact of the pandemic. Research on acceptance and commitment therapy (ACT) interventions for stress in IBD populations has shown promise . The stuIt is important to integrate psychological treatments into a comprehensive care plan that includes medical management, dietary interventions, and other aspects of IBD treatment. Collaboration between healthcare providers, including gastroenterologists, psychologists, and other mental health professionals, is essential. As our understanding of the disease deepens and our treatment options expand, it is our moral imperative to prioritize the well-being of our patients and strive for an improved quality of life. This includes addressing the often overlooked aspects of mental health, which play a crucial role in their overall well-being.


Hard coatings are widely employed on blades to enhance impact resistance and mitigate fatigue failure caused by vibration. While previous studies have focused on the dynamic characteristics of beams and plates, research on real blades remains limited. Specifically, there is a lack of investigation into the dynamic characteristics of hard-coated blades under base excitation. In this paper, the finite element model (FEM) of blade-hard coating (BHC) composite structure is established based on finite element methods in which the hard coating (HC) material and the substrate are considered as the isotropic material. Harmonic response analysis is conducted to calculate the resonance amplitude of the composite under base excitation. Numerical simulations and experimental tests are performed to examine the effects of various HC parameters, including energy storage modulus, loss factors, coating thickness, and coating positions, on the dynamic characteristics and vibration reduction of the hard-coated blade composite structures. The results indicate that the difference in natural frequency and modal loss factor of blades increases with higher storage modulus and HC thickness. Moreover, the vibration response of the BHC decreases with higher storage modulus, loss factor, and coating thickness of the HC material. Blades with a complete coating exhibit superior damping effects compared to other coating distributions. These findings are significant for establishing accurate dynamic models of HC composite structures, assessing the effectiveness of HC vibration suppression, and guiding the selection and preparation of HC materials. Blades play a critical role in aircraft engines and gas turbines. However, they are susceptible to failure caused by vibration fatigue due to exposure to complex vibration patterns. To address this issue, coatings are commonly employed in engineering applications to enhance the wear resistance, corrosion resistance, heat resistance, and other properties of components. The functions and types of coatings vary depending on the specific application field. Soft coatings, such as organic polymer coatings like rubber and viscoelastic materials, have a lower elastic modulus. In contrast, hard coatings encompass metal-based coatings, ceramic-based coatings, and composite coatings that combine metal and ceramic, which possess a relatively higher elastic modulus. Hard coatings offer numerous advantages, including wear resistance, corrosion resistance, fatigue resistance, and radiation resistance, as well as unique dynamic properties and high damping capacity . Consequ2O3 ceramic matrix coatings, and Sn-Cr-MgO coatings [2O3 coatings. Their findings indicated that when MgO + Al2O3 coatings were deposited on plates, they exhibited significant damping effects, with the damping behavior relying on the plate’s modes. Ivancic et al. [2O3 coatings on clamped-free titanium plates and identified mode dependence as the dominant characteristic, rather than frequency dependence. A variety of hard coatings, such as CrAlSiN, TiAlN, NiCrAlY, MgO + Al2O3, and AlCuFeCr/ZrO2, have been widely used because they can suppress the vibration of composited structures [HC is commonly utilized as a damper to enhance the dynamic characteristics of structures. It is categorized into three main types: Fe-Cr-Al/Mo metal matrix coatings, MgO + Alructures . Upon diThe application of hard coatings in the existing literature mostly focuses on simplifying coated discs ,13,14,15Base excitation is important in the vibration characteristics of testing the structure. Base excitation is widely used since the input energy is stable and excitation is close to the actual stress state of the structure ,33,34,35Existing studies primarily concentrate on the vibration characteristics analysis of simplified structures, such as coated discs, straight blades, plates, thin shells, and disk drums. However, limited research has been conducted on the vibration and HC parameters of complex structures, specifically blades, under base excitation. This paper utilizes harmonic response analysis to calculate the resonant amplitude of the composite under base excitation and applies the modal strain energy method to determine the modal loss factor of the composite blade. The study considers the effects of the storage modulus, loss factor, coating thickness, and distribution on the dynamic characteristics of the hard-coated blade composite.The process of basic excitation can be simply described as connecting the BHC composite structural to a rigid platform and using the vibration table to excite the platform foundation. The main characteristic of the basic excitation method is inertial force excitation. The elastic modulus of the metal base, the damping material layer, and the whole composite structure is expressed by the complex modulus asThe modal loss factor obtained based on the complex eigenvalue method isThe relation between The dynamic equation of the BHC isEquation (3) can be used in Equation (4) to obtainEquation (6) indicates that the vibration of BHC is generated by base excitation. Assuming that the excitation is harmonic, Equation (5) is converted into the dynamic equation of the BHC under base excitation in the frequency domain ,36.(7)[The corresponding characteristic equation isThe obtained modal mode vectors of each order can form a modal mode vector matrix The corresponding modal coordinate According to the mode superposition method, the motion equation of BHC in regular coordinates According to the proportional damping assumption, Equation (11) can be expressed asSubstituting Equations (12) and (13) into (11), we haveThen, the steady-state response of the equation in regular coordinates isFinally, the vibration response of BHC under base excitation isThe three-dimensional solid model of the blade was obtained by lofting 12 cross-sectional profiles in PTC Creo 4.0 software and then importing ANSYS 17.0 software to obtain the FEM of the blade. During the modeling process, the blade was simplified, ignoring the rounded corners of the actual blade. The complex aerodynamic load was simplified into a simple harmonic force, and the HC material and substrate were simplified into isotropic materials. The specific details are shown in The FEM of BHC with a thickness of 20 μm is shown in From In this section, the effectiveness of the finite element modeling method for blades and BHC is verified by comparing the natural frequencies and responses of simulations and experiments. The experimental tests include a sweep frequency test and response test under constant frequency excitation. From Based on the test system, the natural frequencies and vibration response of the pure blade and BHC are obtained. The blade, as an object of vibration reduction, has fixed geometric dimensions and material parameters, which generally do not change. We consider the effect of the material parameters of HC on the dynamic characteristics of the blade. To determine the best coating method, the influence of coating parameters on the vibration characteristics of the blade structure under base excitation is studied. The variable parameters of HC are selected as design parameters, including coating storage modulus, loss factor, thickness, and position. Simulation cases are listed in The influence of the loss factor on displacement response and the modal loss factor of the pickup point on the blade is shown in The effects of HC position on the difference in natural frequency, displacement response of the pickup point on the blade, and modal loss factor are depicted in This article verifies the effectiveness of the simulation method for BHC by comparing the natural frequency and displacement response of BHC through experiments and simulations. The findings of this study confirm that the resonance response of the pickup point of the BHC decreases with an increase in the storage modulus, loss factor, and coating thickness of the HC material, aligning with previous research findings ,27,29. SThe study was conducted under static conditions, with certain assumptions made during simulation or experimental settings. These assumptions include disregarding the rounded corners of the actual blade, simplifying complex aerodynamic loads to simple harmonic forces, and treating HC materials and substrates as isotropic materials, which may differ from the actual working conditions. The next step is to address these limitations in order to conduct a comprehensive evaluation of the study.This paper utilized harmonic response analysis under base excitation to calculate the resonant amplitude of the composite. The modal strain energy method was employed to determine the modal loss factor of the composite blade. The study analyzed the influence of storage modulus, loss factor, HC thickness, and coating positions on the natural frequency difference, displacement response of pickup points, modal loss factor, and resonance response of blades. The obtained results hold significant implications for establishing a rational dynamic model of HC composite structures and validating the effectiveness of HC vibration suppression. They also provide valuable insights for the selection and preparation of HC materials. The conclusions are as follows:(1) The HC effectively enhances blade damping, leading to changes in the dynamic characteristics of the blade. The difference in natural frequency and modal loss factor of the blades increases with a higher storage modulus and HC thickness. Additionally, the resonance response and displacement response of the pickup point on the hard-coated blade decrease as the storage modulus, loss factor, and coating thickness of the HC material increase.(2) The amplitude of a blade with a complete coating is lower compared to that with a partial coating, indicating a more pronounced damping effect of the overall coating. When applying HC locally, for the first-order blade vibration, placing the coating at the bottom yields better vibration reduction results due to the maximum stress concentration at the lower part during the first mode of vibration. Conversely, for higher-order blade vibrations, coating the top exhibits superior effectiveness compared to the middle and bottom positions.


Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics . Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer. Oral cancer is one of the most common cancers in the head and neck . In termThe conventional visual examination is the most commonly screening procedure for oral lesions, but its sensitivity and specificity vary greatly . AuxiliaThe study has shown that the thickness of oral mucosa (epithelium and lamina propria) is less than 1 mm . For oraAutomatic image recognition and classification play an important role in biomedicine. To identify oral lesions automatically, texture feature-based methods were proposed. Krishnan et al. made use of high-order spectra, local binary pattern and laws texture energy from histopathological images to identify oral sub-mucous fibrosis . Thomas In addition, deep learning has been surprisingly successful in recent years ,27,28. IThe goal of this study is to explore the potential of automatic recognition of oral cancer based on deep learning in OCT images and evaluate the effectiveness by identifying precancerous and cancerous tissues. In addition, feature visualization is also studied to evaluate the rationality and interpretability of the network. It has the great potential to assist clinicians in screening and diagnosis of oral cancer and precancerous lesions.Fresh tissue samples investigated in this study were obtained from the Tianjin Stomatology Hospital, China. All procedures performed in this study were in accordance with the ethical standards of the Ethics Committee of Tianjin Stomatological Hospital. These samples came from 19 patients who were diagnosed with oral diseases, including leukoplakia with hyperplasia (LEH) and OSCC. The normal and diseased oral tissues were sequentially scanned, and then were fixed and stained with H&E. The slices were evaluated by an experienced pathologist. The details about the OCT system and imaging protocol were described in the previous work ,32.Different morphological features of oral tissues were marked in According to the above analysis of morphological characteristics of different oral tissues in OCT images, OCT images matched with the histopathological images were manually segmented to the appropriate size (256 × 256 pixels) as the regions of interest (ROIs), which contain information unique to different tissues, as shown in the green square boxes in Three CNNs, including LeNet-5, VGG16, and ResNet18, were used for the classification and identification of these oral tissues . As one Further, considering that CNN can perform feature extraction, we used CNN as the feature extractor and machine learning (ML) as the classifier to evaluate the effectiveness in oral tissues classification (CNN + ML). Here, the features were extracted from the last layer before the classification layer (the last fully connected layer) of the pre-trained networks. After that, the feature dimensionality reduction was carried out by the principal component analysis (PCA) algorithm . FinallyIn this method, transfer learning was applied and the networks were trained on the ImageNet dataset in advance. Transfer learning can transfer the acquired powerful skills to relevant problems, thus saving time and computing costs . For theIn order to apply this method to a common scenario, the algorithms were executed on a desktop computer with an eight-core Intel Xeon 3.5 GHz (E5-1620) processor and a 24 GB random-access memory using the Python programming software (Version 3.7.3).Sen), specificity (Spe), precision (Pre), and accuracy (Acc) were calculated.TP: true positives, FP: false positives, FN: false negatives, TN: true negativesTo evaluate the performance of different CNNs and approaches in distinguishing oral tissues, four metrics including sensitivity  were plotted and areas under ROC (AUCs) were also calculated. ROCs and AUCs can be used to describe the classification performance of models objectively. The degree of convergence of the networks was determined by the loss value obtained by the loss function (cross entropy loss).To more directly display classification performance, the predictions of the CNNs were calculated. We extracted 384 overlapping patches from each image. Each patch was input the trained network one by one, and the prediction results were visualized using pseudo-color map.To enhance interpretability of networks, gradient weighted class activation mapping (Grad-CAM) technique was used to highlight the important regions in the OCT images of oral tissues, which creates the visual explanation for CNNs and helps determine more information about the models when performing detection or prediction work .All the CNNs were trained and tested using PyTorch, which is a deep learning framework enabling fast implementation. After 40 epochs of training, loss values of three kinds of CNNs tend to converge . The resThe performances of three kinds of CNNs were presented in Given that ML-based methods often require manual feature extraction and feature selection, it brings about contingency and inconvenience for accurate recognition. To address these issues, we used CNNs as feature extractor, and then used ML as classifier to identify different tissues in OCT images. To further evaluate the best strategy, the comparison of using CNNs as feature extraction and SVM as classifier is shown in t test, there was a statistical significance between the accuracies of LeNet-5 (CNN only) and LeNet-5 combined with SVM (CNN + ML) at p < 0.05.Two classification strategies including the use of CNN alone and the use of CNN combined with ML were evaluated from accuracy . It can In addition, the training time spent on both strategies was also assessed . For usiThe interpretability of neural networks using Grad-CAM was also evaluated for making efficient and confident decisions. As shown in We studied deep-learning-based identification of oral precancerous and cancerous lesions in this paper. Firstly, three basic kinds of CNNs were trained and evaluated based on oral OCT image data sets that includes normal mucosa, LEH, and OSCC. Next, to avoid the contingency and inconvenience of traditional machine learning methods when extracting features manually, CNNs were used as the feature extractors. DT, RF, and SVM were trained by using the activations of the last layer before the classification layer. Both of two strategies obtained excellent classification results. In addition, the performance of the networks was further verified by feature visualization in OCT images.Compared to traditional ML methods, deep learning reduces the dependence on feature extraction. The methods used in deep learning are substantially effective to describe the characteristics of images than texture features. Through comparison on the same dataset, it can be found that using CNN alone especially using LeNet-5 obtained better classification results than that of using CNN + ML, whereas the training time of the former about several hundreds of times longer than that of the latter.(1)According to the requirements of the network on the input size, the size of ROI is determined as 256 × 256 pixels, which can speed up network training compared to the whole image being input.(2)Each ROI must contain the unique characteristics of oral tissue, for example, the epithelium and lamina propria must be included for normal tissue. It can be found that the size of 256 × 256 pixels can not only include the features of oral tissue, but also effectively reduce the interference of background area.(3)In order to effectively use the information in the image, we selected ROI areas in an overlapping approach.(4)Areas with poor image quality were discarded, such as areas without focus due to large fluctuation of tissue surface.To speed up network training, and expand the data set, we segmented the entire image into a certain size. The images were split according to the following segmentation criteria:Similar to the data augmentation used in conventional deep learning, the ROIs we obtained were used to make the network learn invariable features and prevent the network from learning irrelevant features, thus improving network performance. According to the high-performance model, we evaluated the oral tissue OCT images to automatically predict and visualize the lesions, which will be more in line with the actual needs and suitable for intuitive judgement between normal and cancerous areas. In addition, neural networks are often seen as black boxes in disease screening because they provide only the final diagnosis of the subject without any details of the basis of the diagnosis, which brings a major challenge to the application of artificial intelligence in clinical devices. In our study, the Grad-CAM was used to visualize the important regions in the oral tissue OCT images. It can be found that three types of oral tissues showed different characteristics in the deep learning network. Moreover, the aggregation of characteristics can reflect the unique feature of each oral tissue. Through the feature visualization, there is reasonable basis for understanding the model classification and identification.Although microscopic or histopathological examination of tissue is the gold standard, an accurate result of biopsy may depend on the clinician’s experience and confidence, and the selection of biopsy site. A more accurate diagnosis was achieved via multiple site biopsies and larger volume samples , which sIn addition, intraoperative frozen section biopsy for surgical margin is a routine procedure after oral cancers are resected en bloc. Surgical margins are usually selected according to surgeons’ estimate for suspicious sites of inadequate resection, which may result in omission of positive margins. As auxiliary tools, imaging techniques have become indispensable in clinic, where image identification algorithms play an important role . This stIt is noted that the robustness of deep-learning-based identification methods is worth further exploration with different OCT systems. Deep-learning-based identification is performed by extracting differential features from OCT images of oral tissues. That is, the OCT images of different lesions contain differentiated morphological features, which are recorded by the OCT system. In this case, if different OCT systems are employed, such as different wavelengths or different bandwidths, then the acquired OCT images contain different features. Therefore, collaboration between different devices to obtain more data and conduct robust research based on deep learning is the next direction.Fortunately, using the powerful learning capabilities of deep learning and the advantage of high-speed and high-resolution imaging of swept-source OCT system, it laid a foundation for guiding clinicians to screen and resect tumors in real time accurately. In conclusion, the feasibility and validity of automatic recognition strategies for OSCC based on OCT and deep learning have been demonstrated. The interpretability of disease assessment was further investigated by visualizing network feature maps. It is proved that automatic identification methods combining the powerful learning capabilities of deep learning with the advantages of OCT imaging are feasible, which is expected to provide decision support for effective screening and diagnosis of oral cancer and precancerous tissues.


In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning. The global trend of urbanization stands as one of the prominent features of contemporary world development. Over time, an increasing number of people are flocking to urban areas in search of improved living conditions and broader opportunities. However, this trend exhibits marked differences across regions. In some areas, such as Europe and North America, urbanization has been a historical process spanning several centuries, resulting in relatively mature urban planning and infrastructure development  images. The use of Fully Convolutional Networks (FCNs) for semantic segmentation has become the mainstream approach in this domain. Various studies  and (5) above, where t = 0, in an undirected graph G =  where V represents the set of nodes and E represents the set of edges. The update rule for each layer can be formalized as the following equation:Graph Neural Networks (GNNs) are a type of deep learning model used for processing graph-structured data, and have achieved significant breakthroughs in fields such as image processing, social network analysis, and chemical molecule design. GNNs learn to represent the entire graph by iteratively propagating and aggregating information on the nodes and edges of the graph, enabling efficient processing of graph-structured data. The algorithmic principle of GNNs is as follows.The core idea of GNNs is to update the node representations through iterative information propagation and aggregation in the graph, starting from an initial feature representation for each node vi at time step t, N(i) represents the set of neighboring nodes of node vi, wt) represents the update function for node features. This update function usually consists of two parts: information aggregation and activation function. Information aggregation updates the feature representation of the current node by aggregating the features of its neighboring nodes, which can be achieved through simple weighted averaging or more complex attention mechanisms. The activation function introduces non-linearity to increase the expressiveness of the model. After multiple layers of information propagation and aggregation, the final node feature representation where During the training process of GNNs, supervised learning methods are commonly used, optimizing the model parameters by minimizing the loss function between the predicted results and the true labels. The specific form of the loss function can vary depending on the task type and dataset, such as cross-entropy loss for node classification tasks, average pooling loss for graph classification tasks, etc. The algorithmic pseudo-code for the graphical convolutional neural network is shown in In summary, GNNs learn to represent the entire graph by propagating and aggregating information on the nodes and edges of the graph, enabling efficient processing of graph-structured data.Convolutional neural networks mimic the mechanism of biological visual perception, solving the tedious engineering of traditional manual feature extraction and realizing automatic feature extraction from data. However, the excellent performance of convolutional neural network is based on a large number of training samples, and the performance of convolutional neural network decreases dramatically under small samples because the training data is small and the sample features cannot be extracted comprehensively and effectively, so the attention mechanism is introduced into the convolutional neural network.Visual attention mechanism as an important feature of human visual system. Combining attention mechanism with deep learning models, attention mechanism can help deep learning models to better understand external information. In deep learning attention mechanism is an effective tool to extract the most useful information from the input signal. Attention mechanisms usually use higher-level semantic information to reweight lower-level information to suppress background and noise. The attention mechanism is implemented by using filtering functions  and sequential techniques. Attention mechanisms combined with deep learning models have been applied to target detection, natural language processing, image classification, semantic segmentation, etc. with some success. The attention mechanism mimics the human visual attention pattern, focusing on only the most relevant originating information to the current task at a time, making the information request more efficient. The combination of attention mechanism and convolutional neural network can make the network model pay more attention to important features, increase the weight of important features, and suppress unnecessary features to further improve the feature extraction ability of convolutional neural network for important information. In this paper, a dual attention mechanism combining channel attention mechanism and spatial attention mechanism is introduced to combine Graph Neural Networks with a custom sample data set for more accurate detection and recognition of roads and transportation tools.The channel attention mechanism is based on a basic understanding of convolutional neural networks: features of different parts of an object are encoded on different channels of the convolutional feature map. The basic idea of the channel attention mechanism is to continuously adjust the weights of each channel through learning, and generate a vector of length equal to the number of channels through the network, and each element in the vector corresponds to the weight of each channel of the feature map, which in essence tells the network the parts of the pedestrian to be attended to. An attention mechanism algorithm pseudo-code is shown in The network structure is shown in First, the input feature F is subjected to both maximum pooling and average pooling operations, and the null of the aggregated feature mapping The interval information is then input to a shared network, and the spatial dimension of the input feature map is compressed to sum the elements in the feature map one by one and generate the channel attention weights. The calculation formula is shown in equation below.Another attention mechanism cited in this paper is the spatial attention mechanism, which is essentially a network structure that generates a mask of the same size as the original image features, where the value of each element in the mask represents the feature map weight corresponding to the pixel at that location, and the weights change as they are continuously learned and adjusted, which in essence tells the network which regions to focus on. As shown in The feature map is first convolved by four 3 × 3-sized convolutional checks and return branches with both 256 channels, and then compressed into a single mask with 3 × 3 convolution and 1 channels. the original feature map is multiplied by EXP (mask parameter) multiplied to retain the original background information, thus adjusting the weights of each position of the original feature map. To guide the learning of the spatial attention mechanism, this paper uses the supervised information of the pedestrian as the label of the spatial attention mechanism to generate a pixel-level target mask: the pixel values of the pedestrian's full-body bounding box and visible bounding box regions are set to 0.8 and 1, respectively, and the pixel values of the remaining background regions are set to 0. Such a labeling will guide the spatial attention mechanism to focus its attention on the road regions in the frame at the The spatial attention mechanism is guided to pay more attention to the road visible area while focusing on the road area in the picture.The loss function is an important part of the deep learning process, and the main purpose of using the loss function in this paper is to evaluate the prediction accuracy of the model and adjust the weight turnover, the loss function can make the convergence speed of the neural network become faster and make the prediction accuracy of the traffic scene more accurate. The most obvious difference between the two types of problems is that the result of regression prediction is continuous , while the result of classification prediction is discrete . For example, semantic segmentation can be thought of as classifying each pixel in an image, and target detection can be thought of as regression on the position and size of a Bounding Box in an image.In this paper, the parameters of each component are tuned jointly by a multi-task loss function, which consists of 3 components.Mc is the number of all predicted frames; Mr is the number of all predicted frames, considering only the part judged as foreground; L is the loss function of the spatial attention mechanism subnetwork, which is actually a cross−entropy loss function based on each pixel of the mask; p and p denote the category probability of the nth predicted traffic frame and the corresponding actual category, respectively; m* are the masks generated by the spatial attention mechanism and their corresponding mask labels, respectively; λm, m1 are the masks generated by the spatial attention mechanism and their corresponding mask labels; λ and λ2 are the parameters used to balance the sub−loss functions, and their values are both 1 in this paper.where: In generic target detection, the classical regression loss function is the smooth L1 function, which takes the form:The expected weight is between 0 and 1, and even for a perfectly correct prediction frame, its IOU with the visible area may be a smaller value. The overlap between the visible area of the road and the prediction frame is used to improve the occlusion problem, and the practice is to judge this prediction frame as a positive sample only when the IOU of the prediction frame with both the road boundary frame and the visible area boundary frame is greater than a fixed threshold.The AI-TOD aerial image dataset was selected for training. 700,621 object instances in 8 categories from 28,036 aerial images were included in AI-TOD. In addition, 1000 instances of data from the Inria aerial image dataset were used as the validation set for model validation, As shown in This paper uses Pytorch 1.10.0 deep learning framework, the operating system environment is Windows 10, the GPU is NVIDIA GeForce GTX 1650, the system memory size is 24G, and the programming language is Python 3.8.0. The initial learning rate is 0.001, which decreases to 0.0001 at 200 rounds and decays to 0.00001 at 260 rounds. 260 rounds of network training are conducted, and the batchsize of each GPU is 32. The images are modified by image preprocessing. In this paper, we crop the images in the COCO dataset to 256 × 192 size, and then achieve the data enhancement effect by random flipping and random scaling.Remote sensing images are widely used in various fields such as urban planning, traffic management, and environmental monitoring. Road extraction from remote sensing images is an essential task that enables the identification and mapping of transportation networks. It helps in building precise and accurate geographic information systems and enhancing the performance of autonomous vehicles. However, the automatic road extraction methods face significant challenges due to the complex and diverse environments of remote sensing images. Therefore, it is necessary to evaluate the quality of the road extraction methods using appropriate metrics. In this context, recall rate and cross-merge ratio are universal evaluation metrics that are commonly used to assess the performance of remote sensing image road extraction methods. The recall rate is defined as the ratio of true positive predictions to the total number of actual positive samples, which represents the ability of the method to detect all the road pixels. It is given by the following formula:Where TP is the number of true positives, and FN is the number of false negatives.On the other hand, the cross-merge ratio represents the accuracy of the method in identifying the actual road pixels. It is defined as the ratio of true positive predictions to the total number of predicted road pixels, including both true positives and false positives. It is given by the following formula:Where FP is the number of false positives.Both recall rate and cross-merge ratio are important metrics for evaluating the performance of road extraction methods. However, they have their limitations. For example, the recall rate only considers the ability of the method to detect road pixels, but it does not measure the accuracy of the detection. Therefore, a method with a high recall rate may produce a large number of false positives. On the other hand, the cross-merge ratio only measures the accuracy of the predicted road pixels, but it does not consider the ability of the method to detect all the road pixels. Therefore, a method with a high cross-merge ratio may miss some road pixels.To overcome these limitations, other evaluation metrics have been proposed, such as F1-score, precision, and accuracy. The F1-score is the harmonic mean of precision and recall, and it is given by the following formula:Where P is the precision, which is the ratio of true positive predictions to the total number of predicted positive samples, and it is given by the following formula:The accuracy is the ratio of the total number of correct predictions to the total number of predictions, and it is given by the following formula:In order to verify the feasibility of the road segmentation model, the U-Net model based on the U-Net model and the improved model in this paper to implement the road-road extraction task for high-resolution remote sensing images, As shown in the In order to compare the performance of different attention modules, this paper introduces several attention modules into GNN. The results are shown in In response to the challenges faced in deep learning-based high-resolution remote sensing image information extraction, there are several issues that need to be addressed, such as the lack of global contextual information, over-segmentation problems, and difficulties in fusion of multimodal data. Additionally, the computational complexity, long training periods, and limited information transfer in both vertical and horizontal directions pose further challenges. To overcome these issues and improve the accuracy of remote sensing image information extraction, our proposed approach that integrates global attention mechanisms and robot-assisted techniques holds great promise in addressing the challenges of deep learning-based high-resolution remote sensing image information extraction. By incorporating global attention, we enhance the perception of scenes, improve object classification accuracy, and overcome the lack of global contextual information. Furthermore, the spatial attention mechanism helps reduce over-segmentation problems and facilitates the fusion of multimodal data, resulting in more accurate and detailed feature extraction. Robots equipped with advanced sensors and cameras are invaluable in the remote sensing domain. Their capabilities in data acquisition, preprocessing, and feature extraction contribute significantly to improving the efficiency and accuracy of remote sensing image analysis. Moreover, robot assistance in data fusion and annotation reduces manual efforts and enhances overall extraction processes.The interdisciplinary nature of our approach bridges the gap between remote sensing, robotics, and transportation, opening new possibilities for research and applications in traffic management, road safety, and urban planning. By leveraging the strengths of attention mechanisms and robot-assisted techniques, we can achieve enhanced information extraction, leading to more effective traffic management, safer roads, and intelligent transportation systems. This advancement in the field of high-resolution remote sensing image analysis and robotics will undoubtedly have important economic value and far-reaching research significance in various applications and industries.In the context of robotics, the proposed approach holds significant potential to augment the capabilities of robotic systems in road and transportation management. By integrating remote sensing and deep learning technologies into robots, they can actively contribute to various tasks, including road monitoring, traffic flow analysis, and autonomous navigation. With the ability to extract road and transportation features from remote sensing images, robots can efficiently carry out tasks such as road inspection, traffic surveillance, and swift response to accidents. The enhanced retrieval of traffic scenes from remotely sensed images, facilitated by the attentional mechanism fusion and graph neural algorithms, provides robots with precise and up-to-date information for effective decision-making in real-time traffic management and planning.This integration of robotics with remote sensing and deep learning not only improves the efficiency of traffic-related tasks but also enhances road safety and overall transportation systems. With robots capable of autonomously navigating urban environments and capturing high-resolution remotely sensed images, comprehensive traffic databases can be created, allowing for more accurate and informed traffic management strategies.As robotics technology continues to advance, the potential for further advancements in the field of intelligent transportation becomes even more promising. The ongoing evolution of deep learning techniques and visual attention mechanisms will continue to shape the future of remote sensing information extraction, enabling robots to play an even more significant role in traffic management, road safety, and urban planning. However, it is essential to acknowledge that current remote sensing information extraction methods based on deep learning still heavily rely on large training datasets, which can be resource-intensive to produce. Future research should focus on addressing this challenge and finding innovative ways to integrate domain knowledge and manual expertise with deep learning models to reduce dependence on extensive sample sets. By combining human expertise with cutting-edge technologies, the potential for advancing intelligent transportation systems becomes even greater.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.HP: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing—original draft, Writing—review and editing. NS: Conceptualization, Data curation, Formal analysis, Resources, Software, Supervision, Validation, Writing—original draft. GW: Investigation, Supervision, Validation, Visualization, Writing—original draft.


The temporomandibular joint can be affected by various conditions, such as jointdysfunction, degenerative changes, inflammatory processes, infections, tumors, and trauma.The aim of this pictorial essay is to help radiologists identify and describe the mainfindings on magnetic resonance imaging evaluation of the temporomandibular joint, giventhat the correct diagnosis is essential for the appropriate treatment of patients withtemporomandibular joint disorders. The temporomandibular joint (TMJ) is a complex synovial joint that can be affected byvarious conditions, such as joint dysfunction, degenerative changes, inflammatory processes,infections, tumors, and trauma. The aim of this pictorial essay was to review the anatomy ofthe TMJ, as well as derangement and degenerative changes within the joint, discussing themain findings on magnetic resonance imaging (MRI).,2.The bony portion of the TMJ is composed of a temporal component and a mandibular component(the mandibular condyle). Each component is surrounded by a synovial capsule. The jointcavity is composed of the squamous portion of the temporal bone, whose anterior border isthe articular eminence. The mandibular condyle projects into the joint cavity and typicallyhas an oval or rounded morphology,4,5.The TMJ disc is a biconcave, fibrocartilaginous structure that interposes between themandibular condyle and the articular surface of the temporal bone. This interposition of thedisc between the two bony portions of the joint has the important function of preventingjoint damage. In addition, the disc facilitates the sliding of the mandibular condyle inrelation to the temporal bone during the opening and closing of the mouth. Because of thebiconcave shape of the disc, its peripheral zones (the anterior and posterior bands) arethicker than its central portion (the intermediate zone). The disc is attached to the innersurface of the TMJ capsule, creating two joint cavities—an upper cavity and a lower cavity.Under normal conditions , there i,4,5.The TMJ disc is stabilized posteriorly by the bilaminar zone, which inserts into theposterior portion of the posterior band of the disc. The bilaminar zone, also known as theretrodiscal tissue, has two layers of fibers, an upper layer , whichinserts into the anterior contour of the external auditory canal, and a lower layer, whichinserts into the posterior contour of the mandibular condyle. The layers are separated byelastic tissue containing vessels and nerves. The bilaminar zone has the function ofpreventing slippage or excessive rotation of the disc during mouth opening. There is also asmall lamina anterior to the articular eminence and mandibular head,4,5.The lateral pterygoid muscle has two bellies. The superior belly inserts on theanteromedial surface of the neck of the mandible, in the pterygoid fovea region, and, insome patients, part of the superior belly inserts on the surface of the TMJ capsule. Theinferior belly also inserts into the pterygoid fovea.Conventional X-ray of the TMJ, with oblique transcranial projection performed in profilein the open- and closed-mouth positions (Schuller’s view), provides an overview of thebones that make up the joint and joint space. However, it does not allow an assessment ofthe cartilage or adjacent soft parts, in addition to being impaired by the overlap withother cranial bone structures,7.Computed tomography presents excellent image resolution, mainly for the evaluation ofbone structures, making it the method of choice when evaluating fractures involving theTMJ or the involvement of the cortical bone by tumors or inflammatory processes. Computedtomography also allows three-dimensional reconstructions, which are very useful infracture assessment and surgical planning,7,8.For the investigation of TMJ disorders, the modality of choice is MRI. Images ≤ 3 mmin thickness should be acquired in proton density (PD)-weighted, T1-weighted, andT2-weighted sequences. As detailed in ,7.The TMJ disc usually has a biconcave shape and homogeneously low or intermediate signalintensity on T1- and T2-weighted images. In some cases, there can be an area of discretehigh signal intensity in the central portion of the disc on T2- and PD-weighted images. Theposterior insertion of the disc has a signal that is hyperintense to the muscle on PD- andT1-weighted images, due to the presence of fatty tissue, whereas the bilaminar zone shows asignal isointense to muscle in all sequences. The posterior band and the bilaminar zone arebest evaluated in the open-mouth position,3,4,5.In the closed-mouth position, the junction between the posterior band of the disc and thebilaminar zone is located near the 11 or 12 o’clock position . When th.Temporomandibular disorder is a common condition that affects up to 28% of the generalpopulation; the term encompasses a wide range of conditions involving the TMJ.The main cause of TMJ dysfunction is the internal derangement of the joint, defined as anabnormal relationship between the articular disc and the mandibular condyle that interfereswith the normal biomechanics of the TMJ and can manifest clinically as joint pain, jawdeviation while opening the mouth, or sounds  emanating from thejoint,9,10.The most common cause of internal derangement of the TMJ is disc displacement. Theposition of the TMJ disc is evaluated according to its relationship to the condyle. Discdisplacement can thus be classified as anterior, posterior, medial, or lateral. There canalso be multidirectional displacement, which is classified as anterolateral oranteromedial. Although the most common type of displacement is anterior , anterom,11.Anterior displacement of the TMJ disc is defined as a condition in which the junction ofthe posterior band and the bilaminar zone is more anterior than the 11 o’clock position orin which the angle between the posterior band of the disc and a vertical line runningthrough the condylar head is greater than 10°. However, to make a definitive diagnosisbased on that criterion, it is necessary to consider the clinical correlation, given thatsmall anterior disc displacements can be seen in up to 33% of asymptomatic individuals.Other authors have proposed that the intermediate zone be the reference point forassessing disc position, which is considered normal when the disc is interposed betweenthe anterior eminence of the condyle and the posterior aspect of the articular eminence ofthe temporal bone. Medial and lateral displacements are more easily characterized in thecoronal plane, in which the disc should not extend beyond the lateral or medial edge ofthe condyle. This is also evaluated in sagittal acquisitions, in which the disc should notappear medial or lateral to the condyle.An investigation of disc displacement should include the evaluation of recapture, whichoccurs when the disc returns to its normal position after mouth opening. Anteriordisplacement with recapture can be seen in asymptomatic individuals who do not requiretreatment. Anterior displacement without recapture is commonly associated with joint discdeformity, joint effusion, or disc perforation, as well as with bone changes such asedema, erosion, or necrosis of the condyle and temporal eminenceIn the early stages of TMJ dysfunction, the disc retains its usual shape and signalcharacteristics. As the disease progresses, the disc undergoes degenerative changes, withthickening of the posterior band and thinning of the anterior band, and can acquire abiconvex or rounded shape  and 7.,12: anterolateral displacement is associated with perforationof the medial aspect of the disc, whereas anteromedial displacement is associated withperforation of the lateral aspect  position. This condition, known as astuck disc, significantly limits condylar translation. In such patients, there can be thickening atthe insertion of the inferior belly of the lateral pterygoid, forming the double-disc sign(Electroneuromyography studies have identified hyperactivity of the inferior belly of thelateral pterygoid muscle in patients with internal derangement of the TMJisc sign.,11. If osteoarthritisis suspected, contrast-enhanced T1-weighted sequences help differentiate between synovitisand joint effusion. On T2-weighted sequences, a thickened synovium typically shows ahyperintense signal and contrast enhancement (.A small amount of fluid can be seen in normal joints. Small joint effusions are commonlyseen surrounding the anterior band of the articular disc . Moderatancement ; synovit.In general, degenerative changes represent an advanced stage of TMJ dysfunction after along process of disc displacement without recapture, which results in deterioration andabrasion of the articular cartilage, with thickening and remodeling of the underlying bone,and, in some cases, the presence of synovitis signs, bone edema, reduced joint space,irregular condylar margins, osteophytes, and bone sclerosis. Paradoxically, symptoms tend todecrease with age, becoming self-limiting and remittingInternal derangement, together with its possible complications and sequelae, is thepathological condition most commonly seen in the TMJ . Symptom


Meibomian gland dysfunction (MGD) is a functional and morphological disorder of the meibomian glands which results in qualitative or quantitative alteration in meibum secretion and is the major cause of evaporative dry eye (EDE). EDE is often characterized by tear film instability, increased evaporation, hyperosmolarity, inflammation, and ocular surface disorder. The precise pathogenesis of MGD remains elusive. It has been widely considered that MGD develops as a result of ductal epithelial hyperkeratinization, which obstructs the meibomian orifice, halts meibum secretion, and causes secondary acinar atrophy and gland dropout. Abnormal self-renewal and differentiation of the acinar cells also play a significant role in MGD. This review summarizes the latest research findings regarding the possible pathogenesis of MGD and provides further treatment strategies for MGD-EDE patients. Meibomian glands (MGs) were first described by Heinrich Meibom in 1666 . Twenty Meibomian gland dysfunction (MGD) is a chronic, diffuse disorder of the MGs, typically presented with terminal duct blockage and/or qualitatively/quantitatively altered gland production . The preIn this review, we will provide our current insights about the pathogenesis of MGD and therapeutic methods based on these new concepts.The MG is a compound duct–acini structure, which consists of linear arrays of sebaceous glands in the tarsal plates of the eyelids . The secMGs share a similar biology and cell dynamics with sebaceous glands. In the MGs, cell replacement is continuous due to holocrine secretion, which relies on the MG stem cell dynamic activity . There hMGs consist of three main kinds of epithelial cells, including progenitor, differentiated, and ductal epithelial cells . The aciBased on our recent finding, DNase2 is a differentiation marker expressed in the central differentiated epithelial cells of the MGs but not in the peripheral undifferentiated cells . It has As acinar cells degenerate, new cells are continuously produced, causing a turnover and differentiation of cells within the acinus. The consensus was previously made that MG atrophy was permanent . Yang etThe diagnosis of MGD relies on morphological and functional assessments. Currently, a variety of morphological methods have become available, including meibography by transillumination of an everted lid at the slit lamp, in vivo microscopy (IVCM), and noncontact infrared photography. The slit lamp examination is an economical and effective way to observe the tear film break time (BUT), the secretion of meibum, the closure of the MG orifice, and the hyperkeratinization of the lid margin . IVCM coMGD results from abnormal MG function and/or morphology, as commonly shown by alternations in the quality and quantity of the meibum . Both prThere are two main hypotheses for pathological mechanisms, namely the ‘ductal centric’ and ‘meibocyte centric’ ,11,61,62Korb and Henriquez first described obstructive MGD as a result of hyperkeratinization in patients . A notewContinual secretion may cause the atrophic degeneration of the MGs and the gradual loss of lid tissues. With increasing age, the Ki67 labeling of the basal meibocyte layer demonstrated a substantial decrease in mitosis in proliferating cells . This acS. aureus [malassezia species [Age, sex, hormonal disturbance, and environmental factors play a role in the regulation of the process of ductal hyperkeratinization and meibocyte renewal and differentiation. Age-related MGD is associated with decreased meibocyte cell renewal and lipid synthesis via the SRC (a nonreceptor tyrosine kinase protein that is encoded by the SRC gene in humans) pathway . Accordi. aureus  and mala species  might al species . During  species . MGs can species  and some species .S. aureus could lead to the hyperkeratinization of the entire MGs, both in the acinar and ductal systems. It decreased the dynamic activity of meibomian glandular tissue, promoted cell death, inhibited cell proliferation, and destructed the morphology of the acinar and duct. In addition, it downregulated the expression of PPARγ, which is crucial for lipid synthesis and inflammation inhibition [Based on previous findings, we propose that both the hyperkeratinization of the ductal system and the abnormal self-renewal of acinar cells contribute to the development of MGD, which can be regulated by intrinsic and extrinsic factors ,16,65,74hibition .Figure 4The pathological features of MGD are critical in the determination of optimal treatments. Recently, various therapies have been proposed, including eyelid hygiene, MG warming and massage, anti-inflammatory and antibiotic medications ,76. EyelIn summary, MGD is a major cause of ocular surface diseases such as EDE. Previous evidence has confirmed that the pathogenesis of MGD is linked to the alternation of meibum lipids and meibum proteins. Nowadays, more and more researchers are paying attention to protein production, deposition, and aggregation. So far, there has not been a consistent and thorough discussion of the pathophysiology of MGD. It remains unknown whether changes in the MG structure result in alterations in the lipid secretions and whether those changes can be reversed or halted. In the pathogenesis of MGD, the localization and function of MG stem cells need further study. Understanding meibocyte renewal and the maintenance of meibocyte stem cells may enable the treatment of MGD in patients with meibocyte atrophy. A better understanding of the pathological mechanisms helps ophthalmologists and optometrists in effective decision making.