The phenomenon of quasi-periodic pulsations (QPPs) in solar and stellar flares has been known for over 50 years and significant progress has been made in this research area. It has become clear that ...QPPs are not rare—they are found in many flares and, therefore, robust flare models should reproduce their properties in a natural way. At least fifteen mechanisms/models have been developed to explain QPPs in solar flares, which mainly assume the presence of magnetohydrodynamic (MHD) oscillations in coronal structures (magnetic loops and current sheets) or quasi-periodic regimes of magnetic reconnection. We review the most important and interesting results on flare QPPs, with an emphasis on the results of recent years, and we present the predicted and prominent observational signatures of each of the fifteen mechanisms. However, it is not yet possible to draw an unambiguous conclusion as to the correct underlying QPP mechanism because of the qualitative, rather than quantitative, nature of most of the models and also due to insufficient observational information on the physical properties of the flare region, in particular the spatial structure of the QPP source. We also review QPPs in stellar flares, where progress is largely based on solar-stellar analogies, suggesting similarities in the physical processes in flare regions on the Sun and magnetoactive stars. The presence of QPPs with similar properties in solar and stellar flares is, in itself, a strong additional argument in favor of the likelihood of solar-stellar analogies. Hence, advancing our understanding of QPPs in solar flares provides an important additional channel of information about stellar flares. However, further work in both theory/simulations and in observations is needed.
Expansion microscopy CHO, I.; SEO, J.Y.; CHANG, J.
Journal of microscopy (Oxford),
August 2018, Letnik:
271, Številka:
2
Journal Article
Recenzirano
Summary
Super‐resolution optical microscopy techniques have revolutionized how we see and understand biology. In recent past, a new super‐resolution optical microscopy technique called expansion ...microscopy (ExM) was developed. Unlike other pre‐existing super‐resolution imaging techniques, this technique achieves super‐resolution by physically expanding biological specimens via a swellable hydrogel. After the development of ExM, various techniques based on ExM but with improved performance in various aspects, have been developed. In this review, we introduce the basic principles of ExM and its variants. and compare the advantages and disadvantages of these techniques. In addition, we present the applications of ExM techniques in various fields.
Lay Description
In this review, we describe a recently developed optical microscopy technique, i.e., expansion microscopy (ExM). In the last decades, many attempts have been made to develop better microscopy systems, especially with better resolution, to visualize much smaller biological structures or even biomolecules more clearly. Recently, multiple super‐resolution optical microscopy techniques have been developed and widely adopted to study micro‐organisms and cells. However, the use of those techniques to image thick tissue slices remains limited, as light cannot penetrate deep into thick tissue slices. In 2015, a new super‐resolution optical microscopy technique, i.e., expansion microscopy (ExM), was demonstrated. This technique clearly visualized tiny biological structures by expanding the structures via a swellable hydrogel. Unlike conventional optical microscopy techniques, in which a target structure is optically magnified, in ExM the structure itself is physically magnified and imaged with conventional microscopy. Since the first demonstration of ExM, several variants of ExM, such as a simpler ExM or ExM with better resolution, have been reported. In this review article, we describe the basic principle of ExM and its variants. We also discuss the advantages and disadvantages of these techniques and their possible applications.
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The ...psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.
Psoriasis is a skin disease that causes lesions of various sizes across the body and can persist for years with cyclic deterioration and improvement. During treatment, and a multiple-severity ...disease, with irregular severity within the observation area may be found. The current psoriasis evaluation is based on the subjective evaluation criteria of the clinician using the psoriasis area and severity index (PASI). We proposed a novel psoriasis evaluation method that detects representative regions as evaluation criteria, and extracts severity features to improve the evaluation performance of various types of psoriasis, including multiple-severity diseases. We generated multiple-severity disease images using CutMix and proposed a hierarchical multi-scale deformable attention module (MS-DAM) that can adaptively detect representative regions of irregular and complex patterns in multiple-severity disease analyses. EfficientNet B1 with MS-DAM exhibited the best classification performance with an F1-score of 0.93. Compared with the performance of the six existing self-attention methods, the proposed MS-DAM showed more than 5% higher accuracy than that of multiscale channel attention module (MS-CAM). Using the gradient-weighted activation mapping method, we confirmed that the proposed method works at par with human visual perception. We performed a more objective, effective, and accurate analysis of psoriasis severity using the proposed method.
With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to ...enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images.
For 478 centrally located sunspots observed in the optical continuum with Solar Dynamics Observatory/Helioseismic Magnetic Imager, we perform seismological diagnostics of the physical parameters of ...umbral photospheres. The new technique is based on the theory of slow magnetoacoustic waves in a non-isothermally stratified photosphere with a uniform vertical magnetic field. We construct a map of the weighted frequency of three-minute oscillations inside the umbra and use it for the estimation of the Alfvén speed, plasma-beta, and mass density within the umbra. We find the umbral mean Alfvén speed ranges between 10.5 and 7.5 km s−1 and is negatively correlated with magnetic field strength. The umbral mean plasma-beta is found to range approximately between 0.65 and 1.15 and does not vary significantly from pores to mature sunspots. The mean density ranges between (1-6) × 10−4 kg m−3 and shows a strong positive correlation with magnetic field strength.
Instagram is used as an effective and visual marketing channel for building brand equity in the minds of consumers. Therefore, this study aims to classify Instagram marketing activities and analyze ...the associated effects on customer-based brand equity (brand awareness, brand image, perceived quality, brand love, and Instagram re-usage intention) formation through Instagram marketing activities. To this end, data were collected from 358 coffee consumers who had visited any of the five coffee brand Instagram accounts used in this study and analyzed using SPSS and AMOS. The results showed that four sub-dimensions (interaction, entertainment, customization, and trendiness) of Instagram marketing activities affect brand equity (brand awareness, brand image, and perceived quality), which in turn led to attitudinal loyalty (brand love) and behavioral loyalty (Instagram re-usage intention) towards the brand. This research comprehensively illustrates the influences of Instagram marketing activities on customer-based brand equity. The findings of this study will enable coffee brands to more accurately forecast the future purchasing behaviors of their customers through Instagram marketing activities and provide a guide to managing brand equity as well.
Background
The skin surface becomes wrinkled and rough due to various internal and external factors. A three‐dimensional (3D) analysis of the skin is required to improve skin conditions. ...Stereophotogrammetry, a noninvasive 3D analysis method, is easy to install and use, but most stereo systems have a fixed baseline and scale. Previous stereo systems are not suitable for observing micro‐range skin features. Therefore, we suggest the optimal conditions and methods for the 3D analysis of skin microrelief using a multi‐conditioned stereo system.
Methods
We constructed a nonconvergence model using a mobile device and acquired stereo images under multiscale and multi‐baseline conditions. We extracted 3D information of the skin through our process: preprocessing, skin feature extraction, feature matching, and actual depth mapping. We improved the accuracy of the 3D analysis of the skin by using disparity values instead of disparity maps. We compared and analyzed the performances of six local feature detector and descriptor algorithms. In addition, we suggested depth‐mapping formulas to estimate the actual depth of the skin microrelief.
Results
We confirmed that stereo images with a working distance of 70–75 mm and a baseline of 4–8 mm are effective for the 3D analysis of skin microrelief. In addition, accelerated KAZE exhibited the best performance for features extraction and stereo matching. Finally, the extracted 3D information was converted to the actual depth, and the performance of the 3D analysis was verified.
Conclusion
The proposed system and method that provide texture information are effective for 3D skin disease analysis and evaluation.
The skin surface is composed of a network-like microstructure comprising wrinkles. Observing and analyzing the microstructure of the skin that changes with the skin condition and aging are simple, ...stable, and accurate evaluation methods for skin diagnosis. However, the skin surface includes various morphological and topological changes, depending on the individual or the degree of aging. It is difficult to accurately extract and analyze a skin microstructure including these changes. Therefore, we perform skin microstructure segmentation and aging analysis by using convolutional neural network (CNN) models. First, we propose a fusion UNet model to extract the skin microstructure. We compare and evaluate the segmentation performance by using an image processing method and deep learning models. Next, we classify skin aging based on the skin microstructure. For the classification, we use four mobile CNN models: NASNet-Mobile, MobileNetV2, MobileNetV3-Small, and EfficientNet-B0. Subsequently, we compare and evaluate their classification performances. Results show that the segmentation images of the fusion U-Net are most similar to the ground truth, and the fusion U-Net model can detect fine wrinkles that are difficult to identify by the naked eye. In the microstructure-based classification of skin aging, MobileNetV3-Small exhibits the best performance with an accuracy of 94%. The proposed method facilitates an objective and quantitative analysis of the skin surface with more diverse aging characteristics. Consequently, the association between skin aging and skin microstructure changes is confirmed. Our study can be utilized in the diagnostic studies on various skin characteristics, including skin texture, anisotropy, and roughness. The proposed method can also be applied to a mobile-based self-diagnosis system.
IntroductionAlthough dysfunctional breathing is a common symptom in general population and affects qualities of life, it is still underdiagnosed. There are some studies of prevalence of it in asthma, ...but few studies in mental illness.ObjectivesThe purposes of this study were to explore the prevalence of it in anxiety related disorders, and to investigate whether anxiety influence it.Methods150 patients diagnosed with anxiety or depressive disorders, and 135 controls were recruited. Nijmegen questionnaire was used to assess dysfunctional breathing, and Hospital anxiety depression scale was used.ResultsThe prevalence of dysfunctional breathing in anxiety related disorders was higher than that in control.In the linear regression model, anxiety accounted for 61.2 % of dysfunctional breathing, but depressed mood. With covariate adjusted for anxiety, scores of dysfunctional breathing in anxiety or depressive disorders were higher than in controls.ConclusionsDysfunctional breathing in anxiety related disorders is higher than that in control. Adjusting anxiety, its difference is still. Anxiety affects dysfunctional breathing, but depressed mood does not.Disclosure of InterestNone Declared
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