Macular microvascular and structural changes on optical coherence tomography angiography in atypical optic neuritis

BACKGROUND Atypical optic neuritis,consisting of neuromyelitis optica spectrum disorders(NMOSD)or myelin oligodendrocyte glycoprotein antibody disease(MOGAD),has a very similar presentation but different prognostic implications and longterm management strategies.Vascular and metabolic factors are being thought to play a role in such autoimmune neuro-inflammatory disorders,apart from the obvious immune mediated damage.With the advent of optical coherence tomography angiography(OCTA),it is easy to pick up on these subclinical macular microvascular and structural changes.AIM To study the macular microvascular and structural changes on OCTA in atypical optic neuritis.METHODS This observational cross-sectional study involved 8 NMOSD and 17 MOGAD patients,diagnosed serologically,as well as 10 healthy controls.Macular vascular density(MVD)and ganglion cell+inner plexiform layer thickness(GCIPL)were studied using OCTA.RESULTS There was a significant reduction in MVD in NMOSD and MOGAD affected as well as unaffected eyes when compared with healthy controls.NMOSD and MOGAD affected eyes had significant GCIPL thinning compared with healthy controls.NMOSD unaffected eyes did not show significant GCIPL thinning compared to healthy controls in contrast to MOGAD unaffected eyes.On comparing NMOSD with MOGAD,there was no significant difference in terms of MVD or GCIPL in the affected or unaffected eyes.CONCLUSION Although significant microvascular and structural changes are present on OCTA between atypical optic neuritis and normal patients,they could not help in differentiating between NMOSD and MOGAD cases.

A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Glaucoma surgery experiments using digital microscope-integrated optical coherence tomography and OCT-compatible instruments

There is a certain failure rate in traditional glaucoma surgery because of the lack of depth information in microscope images.In this work,we present a digital microscope-integrated optical coherence tomography(MIOCT)system and several custom-made OCT-compatible instruments for glaucoma surgery.Sixteen ophthalmologists were asked to perform trabeculectomy and canaloplasty on live porcine eyes using the system and instruments.After surgery,a subjective feedback survey about the user experience was taken.The experiment results showed that our system can help surgeons easily locate important tissue structures during surgery.The custom-made instruments also solved the shadowing problem in OCT imaging.Surgeons preferred to use the system in their future practice.

Evaluation of retinal and choroidal thickness changes in overweight and obese adults without ocular symptoms by swept-source optical coherence tomography

AIM:To evaluate the relationship of overweight and obesity with retinal and choroidal thickness in adults without ocular symptoms by swept-source optical coherence tomography(SS-OCT).METHODS:According to the body mass index(BMI)results,the adults enrolled in the cross-sectional study were divided into the normal group(18.50≤BMI<25.00 kg/m^(2)),the overweight group(25.00≤BMI<30.00 kg/m^(2)),and the obesity group(BMI≥30.00 kg/m^(2)).The one-way ANOVA and the Chi-square test were used for comparisons.Pearson’s correlation analysis was used to evaluate the relationships between the measured variables.RESULTS:This research covered the left eyes of 3 groups of 434 age-and sex-matched subjects each:normal,overweight,and obesity.The mean BMI was 22.20±1.67,26.82±1.38,and 32.21±2.35 kg/m^(2) in normal,overweight and obesity groups,respectively.The choroid was significantly thinner in both the overweight and obesity groups compared to the normal group(P<0.05 for all),while the retinal thickness of the three groups did not differ significantly.Pearson’s correlation analysis showed that BMI was significantly negatively correlated with choroidal thickness,but no significant correlation was observed between BMI and retinal thickness.CONCLUSION:Choroidal thickness is decreased in people with overweight or obesity.Research on changes in choroidal thickness contributes to the understanding of the mechanisms of certain ocular disorders in overweight and obese adults.

Three-dimensional choroidal vascularity index and choroidal thickness in fellow eyes of acute and chronic primary angle-closure using swept-source optical coherence tomography

AIM:To compare the three-dimensional choroidal vascularity index(CVI)and choroidal thickness between fellow eyes of acute primary angle-closure(F-APAC)and chronic primary angle-closure glaucoma(F-CPACG)and the eyes of normal controls.METHODS:This study included 37 patients with unilateral APAC,37 with asymmetric CPACG without prior treatment,and 36 healthy participants.Using swept-source optical coherence tomography(SS-OCT),the macular and peripapillary choroidal thickness and three-dimensional CVI were measured and compared globally and sectorally.Pearson’s correlation analysis and multivariate regression models were used to evaluate choroidal thickness or CVI with related factors.RESULTS:The mean subfoveal CVIs were 0.35±0.10,0.33±0.09,and 0.29±0.04,and the mean subfoveal choroidal thickness were 315.62±52.92,306.22±59.29,and 262.69±45.55μm in the F-APAC,F-CPACG,and normal groups,respectively.All macular sectors showed significantly higher CVIs and choroidal thickness in the F-APAC and F-CPACG eyes than in the normal eyes(P<0.05),while there were no significant differences between the F-APAC and F-CPACG eyes.In the peripapillary region,the mean overall CVIs were 0.21±0.08,0.20±0.08,and 0.19±0.05,and the mean overall choroidal thickness were 180.45±54.18,174.82±50.67,and 176.18±37.94μm in the F-APAC,F-CPACG,and normal groups,respectively.There were no significant differences between any of the two groups in all peripapillary sectors.Younger age,shorter axial length,and the F-APAC or F-CPACG diagnosis were significantly associated with higher subfoveal CVI and thicker subfoveal choroidal thickness(P<0.05).CONCLUSION:The fellow eyes of unilateral APAC or asymmetric CPACG have higher macular CVI and choroidal thickness than those of the normal controls.Neither CVI nor choroidal thickness can distinguish between eyes predisposed to APAC or CPACG.A thicker choroid with a higher vascular volume may play a role in the pathogenesis of primary angle-closure glaucoma.

Intravascular photoacoustic and optical coherence tomography imaging dual-mode system for detecting spontaneous coronary artery dissection: A feasibility study

In this work,we present an intravascular dual-mode endoscopic system capable of both intravascular photoacoustic imaging(IVPAI)and intravascular optical coherence tomography(IVOCT)for recognizing spontaneous coronary artery dissection(SCAD)phantoms.IVPAI provides high-resolution and high-penetration images of intramural hematoma(IMH)at different depths,so it is especially useful for imaging deep blood clots associated with imaging phantoms.IVOCT can readily visualize the double-lumen morphology of blood vessel walls to identify intimal tears.We also demonstrate the capability of this dual-mode endoscopic system using mimicking phantoms and biological samples of blood clots in ex vivo porcine arteries.The results of the experiments indicate that the combined IVPAI and IVOCT technique has the potential to provide a more accurate SCAD assessment method for clinical applications.

Quantum block coherence with respect to projective measurements

Quantum coherence serves as a defining characteristic of quantum mechanics,finding extensive applications in quantum computing and quantum communication processing.This study explores quantum block coherence in the context of projective measurements,focusing on the quantification of such coherence.Firstly,we define the correlation function between the two general projective measurements P and Q,and analyze the connection between sets of block incoherent states related to two compatible projective measurements P and Q.Secondly,we discuss the measure of quantum block coherence with respect to projective measurements.Based on a given measure of quantum block coherence,we characterize the existence of maximal block coherent states through projective measurements.This research integrates the compatibility of projective measurements with the framework of quantum block coherence,contributing to the advancement of block coherence measurement theory.

Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Aflibercept combined with triamcinolone acetonide in the treatment of diabetic macular edema:optical coherence tomography and optical coherence tomography angiography

AIM:To analyze the relationship between optical coherence tomography(OCT)and OCT angiography(OCTA)imaging in patients with diabetic macular edema(DME)who are treated with a combination of aflibercept and triamcinolone acetonide(TA).METHODS:A total of 76 eyes newly diagnosed DME were included in this study.They were randomly assigned to receive either aflibercept or a combination of aflibercept and TA.Injections once a month for a total of three injections.Central macular thickness(CMT),number of hyperreflective foci(HRF),height of subretinal fluid(SRF),and area of foveal avascular zone(FAZ)were evaluated using OCT and OCTA at baseline and after each monthly treatment.RESULTS:Both groups showed improvement in best corrected visual acuity(BCVA)and reduction in macular edema after treatment,and the difference in BCVA between the two groups was statistically significant after each treatment(P<0.05).The difference in CMT between the two groups was statistically significant after the first two injections(P<0.01),but not after the third injection(P=0.875).The number of HRF(1mo:7.41±8.25 vs 10.86±7.22,P=0.027;2mo:5.33±6.13 vs 9.12±8.61,P=0.034;3mo:3.58±3.00 vs 6.37±5.97,P=0.007)and height of SRF(1mo:82.39±39.12 vs 105.77±42.26μm,P=0.011;2mo:36.84±10.02 vs 83.59±37.78μm,P<0.01;3mo:11.57±3.29 vs 45.43±12.60μm,P<0.01)in combined group were statistically significant less than aflibercept group after each injection,while the area of FAZ showed no significant change before and after treatment in both groups.CONCLUSION:The combination therapy of aflibercept and TA shows more significant effects on DME eyes with decreased HRF and SRF.However,both aflibercept and combination therapy show no significant change in the area of FAZ.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

MULTI-SCALE COHERENT STRUCTURES IN TURBULENT BOUNDARY LAYER DETECTED BY LOCALLY AVERAGED VELOCITY STRUCTURE FUNCTIONS

The time sequence of longitudinal velocity component at different vertical locations in turbulent boundary layer was finely measured in a wind tunnel. The concept of coarse_grained velocity structure functions, which describes the relative motions of straining and compressing for multi_scale eddy structures in turbulent flows, was put forward based on the theory of locally multi_scale average. Based on the consistency between coarse_grained velocity structure function and Harr wavelet transformation,detecting method was presented, by which the coherent structures and their intermittency was identified by multi_scale flatness factor calculated by locally average structure function. Phase_averaged evolution course for multi_scale coherent eddy structures in wall turbulence were extracted by this conditional sampling to educe scheme. The dynamics course of multi_scale coherent eddy structures and their effects on statistics of turbulent flows were studied.

Multi-scale Modeling and Finite Element Analyses of Thermal Conductivity of 3D C/SiC Composites Fabricating by Flexible-Oriented Woven Process

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Distal-scanning common path probe for optical coherence tomography

In this paper,we present a distal-scanning common path probe for optical coherence tomography(OCT)equipped with a hollow ultrasonic motor and a simple and specially designed beam-splitter.This novel probe proves to be able to effectively circumvent polarization and dispersion mismatch caused by fiber motion and is more robust to a variety of interfering factors during the imaging process,experimentally compared to a conventional noncommon path probe.Furthermore,our design counteracts the attenuation of backscattering with depth and the fall-off of the signal,resulting in a more balanced signal range and greater imaging depth.Spectral-domain OCT imaging of phantom and biological tissue is also demonstrated with a sensitivity of∼100dB and a lateral resolution of∼3μm.This low-cost probe offers simplified system configuration and excellent robustness,and is therefore particularly suitable for clinical diagnosis as one-off medical apparatus.

Imaging through scattering layers using a near-infrared low-spatial-coherence fiber random laser

Optical memory effect-based speckle-correlated technology has been developed for reconstructing hidden objectsfrom disordered speckle patterns,achieving imaging through scattering layers.However,the lighting efficiency and fieldof view of existing speckle-correlated imaging systems are limited.Here,a near-infrared low spatial coherence fiberrandom laser illumination method is proposed to address the above limitations.Through the utilization of random Rayleighscattering within dispersion-shifted fibers to provide feedback,coupled with stimulated Raman scattering for amplification,a near-infrared fiber random laser exhibiting a high spectral density and extremely low spatial coherence is generated.Based on the designed fiber random laser,speckle-correlated imaging through scattering layers is achieved,with highlighting efficiency and a large imaging field of view.This work improves the performance of speckle-correlated imagingand enriches the research on imaging through scattering medium.

Comparison of ocular biometric parameters between two swept-source optical coherence tomography devices and Scheimpflug tomography in patients with cataract

AIM:To assess and compare the variations and agreements across different ocular biometric parameters using swept-source optical coherence tomography(SS-OCT)and Scheimpflug tomography in patients diagnosed with cataract.METHODS:This prospective case series was conducted at Tianjin Medical University Eye Hospital.In total,212 eyes from 212 patients scheduled for phacoemulsification were included.Eyes were evaluated preoperatively using two SSOCT devices(IOLMaster700 and CASIA2)and Scheimpflug tomography(Pentacam).Central corneal thickness(CCT),anterior chamber depth(ACD),aqueous depth(AQD),white-to-white distance(WTW),flat simulated keratometry(Kf),steep simulated keratometry(Ks),mean keratometry(Km),and total corneal keratometry(TKm)were measured.Intraclass correlation coefficient(ICC),95%confidence intervals(CI)and limits of agreement(LoA)widths were conducted to assess differences and correlations between devices.RESULTS:All parameters,except for Ks,were significantly different.Pairwise comparison revealed no significant differences between keratometry obtained by IOLMaster 700 and Pentacam.LoA widths of all paired comparisons for Ks were>0.80 D.Except for WTW between IOLMaster 700 and CASIA2 and between CASIA2 and Pentacam,other Pearson’s coefficients between devices showed a strong correlation(all r>0.95).The ICC of WTW(ICC=0.438,95%CI 0.167-0.625)showed poor reliability.The reliability of CCT,ACD,and AQD was excellent(all ICC>0.95),whereas that of TKm was good(ICC=0.827,95%CI 0.221-0.939).A significant linear correlation was also observed among devices.CONCLUSION:The ocular parameters derived from the use of IOLMaster700,CASIA2,and Pentacam exhibit significant discrepancies;as such,measurements from these devices should not be deemed as interchangeable.