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Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote sensing images has limited performance in global context information. Therefore, salient object detection in optical remote sensing images (ORSI-SOD) is challenging. Optical remote sensing images usually have large-scale variations. However, the vast majority of networks are based on Convolutional Neural Network (CNN) backbone networks such as VGG and ResNet, which can only extract local features. To address this problem, we designed a new model that employs a transformer-based backbone network capable of extracting global information and remote dependencies. A new framework is proposed for this question, named Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images (GMANet). In this framework, the Pyramid Vision Transformer (PVT) is an encoder to catch remote dependencies. A Multiscale Attention Module (MAM) is introduced for extracting multiscale information. Meanwhile, a Global Guiled Brach (GGB) is used to learn the global context information and obtain the complete structure. Four MAMs are densely connected to this GGB. The Aggregate Refinement Module (ARM) is used to enrich the details of edge and low-level features. The ARM fuses global context information and encoder multilevel features to complement the details while the structure is complete. Extensive experiments on two public datasets show that our proposed framework GMANet outperforms 28 state-of-the-art methods on six evaluation metrics, especially E-measure and F-measure. It is because we apply a coarse-to-fine strategy to merge global context information and multiscale information. [ABSTRACT FROM AUTHOR]

Intrinsic actuation magnetic resonance elastography (MRE) is a phase‐contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high‐quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior–posterior and left–right directions, and 0.7 mm/2π for feet–head displacements). The viscoelastic properties of the brain were estimated using a subzone based non‐linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole‐brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole‐brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole‐brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data‐model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes. [ABSTRACT FROM AUTHOR]

Forensic audio analysis is a foundation stone of many crime investigations. In forensic evidence; the audio file of the human voice is analyzed to extract much information in addition to the content of the speech, such as the speaker's identity, emotions, gender, origin, etc. The accurate determination of individuals into groups based on their age development stage and their gender are often used as early investigations to differentiate them and determine the legal rights and responsibilities associated with them. This work introduces a light CNN model with a new architecture to detect the human age-evolution being's stage (kids or adults) at the same time the gender of the adult one (male or female) based on the individual's voice characteristics, which offers a balance between computational efficiency and model accuracy. The temporal information in the audio file is prepared by scaled and normalized. Then this information is exploited to extract and track the unique and salient audio features that make up the pattern of the feature map for each target class through some convolutional layers followed by maxpooling layers. Finally, The decision is made based on these feature maps by some fully connected layers. Successful and promising results are accomplished in terms of accuracy and loss functions which realize 0.99 and 0.017 respectively over the riched Voxceleb2 dataset. The proposed model underscores the importance of leveraging Light DNNs for gender and age-evolution detection, offering a robust and ethically sound solution for real-world applications in the field of audio forensics such as span speaker identification, victim profiling, deception detection, and more, contributing to the advancement of audio forensic analysis. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a common malignant tumor in the gastrointestinal tract, which usually evolves from adenomatous polyps. However, due to the similarity in color between polyps and their surrounding tissues in colonoscopy images, and their diversity in size, shape, and texture, intelligent diagnosis still remains great challenges. For this reason, we present a novel dense residual-inception network (DRI-Net) which utilizes U-Net as the backbone. Firstly, in order to increase the width of the network, a modified residual-inception block is designed to replace the traditional convolutional, thereby improving its capacity and expressiveness. Moreover, the dense connection scheme is adopted to increase the network depth so that more complex feature inputs can be fitted. Finally, an improved down-sampling module is built to reduce the loss of image feature information. For fair comparison, we validated all method on the Kvasir-SEG dataset using three popular evaluation metrics. Experimental results consistently illustrates that the values of DRI-Net on IoU, Mcc and Dice attain 77.72%, 85.94% and 86.51%, which were 1.41%, 0.66% and 0.75% higher than the suboptimal model. Similarly, through ablation studies, it also demonstrated the effectiveness of our approach in colorectal semantic segmentation. [ABSTRACT FROM AUTHOR]

From the high perspective of topology, this paper discusses the essential properties of some important concepts and principles in mathematical analysis, and studies their particularity. In mathematical analysis, the uniqueness of sequence limit, the characterization of continuous function by Heine's theorem, the intermediate value theorem, and the equivalence of several important theorems describing the completeness of real numbers are all special results on the space of special real numbers. [ABSTRACT FROM AUTHOR]

Soil erosion monitoring in coastal mountainous areas is very important during the construction of Electric-Transmission-Line (ETL) because of the impact this disturbance has on the sensitive environment. In this study, high-resolution remote sensing data and deep learning models including Dense and Long Short-Term Memory (LSTM) were used to fit the popular soil erosion equation, which is called the Revised Universal Soil Loss Equation (RUSLE), for the Min-Yue ETL (in Fujian). The accuracy of soil erosion regression was then evaluated in the transmission line buffer area and sampling spots at two spatial scales in order to obtain the optimized parameters and a suitable model. The results show that the Dense and LSTM models can meet the accuracy requirements by using 10 characteristic values, including soil erodibility, annual rainfall, mountain vegetation index (NDMVI), DEM, slope, four bands gray values of high-spectral image, construction attributes. The optimized parameters for the priority machine-learning model LSTM are as follows: the layer depth is 3, the layer capacity is 512, the dropout ratio is 0.1, and the epoch of the LSTM model is 7060. The regression accuracy of the LSTM model decreases with an increase in soil erosion levels, and the average regression accuracy is greater than 0.98 for the slight level of soil erosion. Therefore, the machine-learning model of LSTM can be applied for quickly monitoring the soil erosion using high resolution remote sensing data. [ABSTRACT FROM AUTHOR]

Background: In this review, we introduce the displacement encoding with stimulated echoes (DENSE) method for measuring myocardial dyssynchrony using cardiovascular magnetic resonance (CMR) imaging. We provide an overview of research findings related to DENSE from the past two decades and discuss other techniques used for dyssynchrony evaluation. Additionally, the review discusses the potential uses of DENSE in clinical practice. Methods: A search was conducted to identify relevant articles published from January 2000 through January 2023 using the Scopus, Web of Science, PubMed and Cochrane databases. The following search term was used: (DENSE OR 'displacement encoding with stimulated echoes' OR CURE) AND (dyssynchrony* OR asynchron* OR synchron*) AND (MRI OR 'magnetic resonance' OR CMR). Results: After removing duplicates, researchers screened a total of 174 papers. Papers that were not related to the topic, reviews, general overview articles and case reports were excluded, leaving 35 articles for further analysis. Of these, 14 studies focused on cardiac dyssynchrony estimation with DENSE, while the remaining 21 studies served as background material. The studies used various methods for presenting synchronicity, such as circumferential uniformity ratio estimate (CURE), CURE-singular value decomposition (SVD), radial uniformity ratio estimate (RURE), longitudinal uniformity ratio estimate (LURE), time to onset of shortening (TOS) and dyssynchrony index (DI). Most of the dyssynchrony studies concentrated on human heart failure, but congenital heart diseases and obesity were also evaluated. The researchers found that DENSE demonstrated high reproducibility and was found useful for detecting cardiac resynchronisation therapy (CRT) responders, optimising CRT device settings and assessing right ventricle synchronicity. In addition, studies showed a correlation between cardiac fibrosis and mechanical dyssynchrony in humans, as well as a decrease in the synchrony of contraction in the left ventricle in obese mice. Conclusions: DENSE shows promise as a tool for quantifying myocardial function and dyssynchrony, with advantages over other cardiac dyssynchrony evaluation methods. However, there remain challenges related to DENSE due to the relatively time-consuming imaging and analysis process. Improvements in imaging and analysing technology, as well as possible artificial intelligence solutions, may help overcome these challenges and lead to more widespread clinical use of DENSE. [ABSTRACT FROM AUTHOR]

Latest advancements in transparent ceramics development have ensured a mainstream research interest in this family of materials. Crystallization of glass into transparent ceramics has emerged recently as an alternative but complementary trajectory for obtaining transparent ceramics, which circumvents some of the long-standing technical difficulties associated with traditional transparent ceramics processing. The full bulk glass crystallization allows for the synthesis of high-density/low-porosity transparent ceramics of stable and metastable phases or even non-cubic structures, which are difficult to obtain using the traditional processing methods. This article presents a brief survey of the science of the transparency of ceramics and a detailed overview of the materials systems and techniques used for the preparation of transparent ceramics through the glass crystallization route. Finally, the review provides authors' insights into the future trends and research directions aimed to encourage a widespread application of glass crystallization into transparent ceramics in the fabrication of next-generation materials. [ABSTRACT FROM AUTHOR]

Abstract Background Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. Methods 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. Results Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. Conclusion Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote sensing images has limited performance in global context information. Therefore, salient object detection in optical remote sensing images (ORSI-SOD) is challenging. Optical remote sensing images usually have large-scale variations. However, the vast majority of networks are based on Convolutional Neural Network (CNN) backbone networks such as VGG and ResNet, which can only extract local features. To address this problem, we designed a new model that employs a transformer-based backbone network capable of extracting global information and remote dependencies. A new framework is proposed for this question, named Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images (GMANet). In this framework, the Pyramid Vision Transformer (PVT) is an encoder to catch remote dependencies. A Multiscale Attention Module (MAM) is introduced for extracting multiscale information. Meanwhile, a Global Guiled Brach (GGB) is used to learn the global context information and obtain the complete structure. Four MAMs are densely connected to this GGB. The Aggregate Refinement Module (ARM) is used to enrich the details of edge and low-level features. The ARM fuses global context information and encoder multilevel features to complement the details while the structure is complete. Extensive experiments on two public datasets show that our proposed framework GMANet outperforms 28 state-of-the-art methods on six evaluation metrics, especially E-measure and F-measure. It is because we apply a coarse-to-fine strategy to merge global context information and multiscale information.

The aim of this multicentric study was to assess the impacts of oxidative stress, inflammation, and the presence of small, dense, low-density lipoproteins (sdLDL) on the antioxidative function of high-density lipoprotein (HDL) subclasses and the distribution of paraoxonase-1 (PON1) activity within HDL in patients with ST-segment elevation acute myocardial infarction (STEMI). In 69 STEMI patients and 67 healthy control subjects, the lipoproteins' subclasses were separated using polyacrylamide gradient (3–31%) gel electrophoresis. The relative proportion of sdLDL and each HDL subclass was evaluated by measuring the areas under the peaks of densitometric scans. The distribution of the relative proportion of PON1 activity within the HDL subclasses (pPON1 within HDL) was estimated using the zymogram method. The STEMI patients had significantly lower proportions of HDL2a and HDL3a subclasses (p = 0.001 and p < 0.001, respectively) and lower pPON1 within HDL3b (p = 0.006), as well as higher proportions of HDL3b and HDL3c subclasses (p = 0.013 and p < 0.001, respectively) and higher pPON1 within HDL2 than the controls. Independent positive associations between sdLDL and pPON1 within HDL3a and between malondialdehyde (MDA) and pPON1 within HDL2b were shown in the STEMI group. The increased oxidative stress and increased proportion of sdLDL in STEMI are closely related to the compromised antioxidative function of small HDL3 particles and the altered pPON1 within HDL. [ABSTRACT FROM AUTHOR]

The bamboo-shaped SiC nanowires reinforced SiC dense coating (dense SiCNWbs/SiC coating) was successfully designed on the graphite by polymer pyrolysis chemical vapor deposition (PPCVD) combined with chemical vapor deposition (CVD) method. By analyzing the thermal decomposition of the mixed powder of PCS and activated carbon, it was determined that the porous bamboo-shaped SiC nanowires layer could be grown by the PPCVD technology based on a dual-temperature zone whose optimal temperatures were determined to be 800 ℃ + 1200 ℃. SEM results showed that the porous layer prepared by the catalyst solution of 0.1 mol/L and holding time of 2 h was easier for the subsequent SiC densification. As expected, the dense SiCNWbs/SiC coating (SS1 coating) was successfully fabricated. Compared with the loose coating, the SS1 coating showed better performance. SS1 coating possessed better crystallinity. The bonding strength between the coating and the substrate was 11.96 MPa, and the weight loss was just 0.79% after oxidation at 1500 ℃ for 60 h, which showed the potential of excellent anti-oxidation protection. Finally, the forming mechanism of the dense coating was revealed, which provided a theoretical basis for the forming and application of the dense anti-oxidation coating of C/C composites. [ABSTRACT FROM AUTHOR]

Background: Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. Methods: 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. Results: Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. Conclusion: Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine. [ABSTRACT FROM AUTHOR]

Behind the science of breast cancer in Canada, as well as globally, are the stories of thousands of women, their families, and their communities. These include stories from those who have died or those suffering from the realities of stage III and stage IV breast cancer due to late detection, misinformation, and dismissal. The reality for these women is that, whilst grateful for the latest developments in cancer research, much of this knowledge is not reflected in policy and practice. Canadian guidelines do not reflect the recommended screening by experts within the field and inequities in screening practices and practitioner knowledge exist in different areas within Canada. Told through the stories of women with lived experiences of late-stage breast cancer and supported by scientific evidence, this paper explores the impact of outdated breast cancer screening practices on the lives of women. Recent patient advocacy is driving changes, such as notifying women of their breast density in a few jurisdictions in Canada, but we call for the whole medical community to take responsibility and ensure breast screening is optimised to save more lives.

Abstract Background While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers. Methods Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland–Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6

IntroductionDisplacement encoding with stimulated echoes (DENSE) is a phase contrast technique that encodes tissue displacement into phase images, which are typically processed into measures of cardiac function such as strains. For improved signal to noise ratio and spatiotemporal resolution, DENSE is often acquired with a spiral readout using an 11.1 ms readout duration. However, long spiral readout durations are prone to blurring due to common phenomena such as off-resonance and T2* decay, which may alter the resulting quantifications of strain. We hypothesized that longer readout durations would reduce image quality and underestimate cardiac strains at both 3.0 T and 1.5 T and that using short readout durations could overcome these limitations.Material and methodsComputational simulations were performed to investigate the relationship between off-resonance and T2* decay, the spiral cine DENSE readout duration, and measured radial and circumferential strain. Five healthy participants subsequently underwent 2D spiral cine DENSE at both 3.0 T and 1.5 T with several different readout durations 11.1 ms and shorter. Pearson correlations were used to assess the relationship between cardiac strains and the spiral readout duration.ResultsSimulations demonstrated that long readout durations combined with off-resonance and T2* decay yield blurred images and underestimate strains. With the typical 11.1 ms DENSE readout, blurring was present in the anterior and lateral left ventricular segments of participants and was markedly improved with shorter readout durations. Radial and circumferential strains from those segments were significantly correlated with the readout duration. Compared to the 1.9 ms readout, the 11.1 ms readout underestimated radial and circumferential strains in those segments at both field strengths by up to 19.6% and 1.5% (absolute), or 42% and 7% (relative), respectively.ConclusionsBlurring is present in spiral cine DENSE images acquired at both 3.0 T and 1.5 T using the typical 11.1 ms readout duration, which yielded substantially reduced radial strains and mildly reduced circumferential strains. Clinical studies using spiral cine DENSE should consider these limitations, while future technical advances may need to leverage accelerated techniques to improve the robustness and accuracy of the DENSE acquisition rather than focusing solely on reduced acquisition time.

IntroductionIn displacement encoding with stimulated echoes (DENSE), tissue displacement is encoded in the signal phase such that the phase of each pixel in space and time provides an independent measurement of absolute tissue displacement. Previously for DENSE, estimation of Lagrangian displacement used two steps: first a spatial interpolation and, second, least squares fitting through time to a Fourier or polynomial model. However, there is no strong rationale for such a through-time model,MethodsTo compute the Lagrangian displacement field from DENSE phase data, a minimization problem is introduced to enforce fidelity with the acquired Eulerian displacement data while simultaneously providing model-independent regularization in space and time, enforcing only spatiotemporal smoothness. A regularized spatiotemporal least squares (RSTLS) method is used to solve the minimization problem, and RSTLS was tested using two-dimensional DENSE data from 71 healthy volunteers.ResultsThe mean absolute percent error (MAPE) between the Lagrangian displacements and the corresponding Eulerian displacements was significantly lower for the RSTLS method vs. the two-step method for both x- and y-directions (0.73±0.59 vs 0.83 ±0.1, p

This paper proposed LONTAR_DETC, a method to detect handwritten Balinese characters in Lontar manuscripts. LONTAR_DETC is a deep learning architecture based on YOLO. The detection of Balinese characters in Lontar manuscripts is challenging due to the characteristics of Balinese characters in Lontar manuscripts. Balinese characters in Lontar manuscripts are dense, overlapping, have high variance, contain noise, and classes of these characters are imbalanced. The proposed method consists of three steps, namely data generation, Lontar manuscript annotation, and Balinese character detection. The first step is data generation, in which synthetic images of original Lontar manuscript images are generated with enhanced image quality. The second step is data annotation to build a new Lontar manuscript dataset. As a result, we also propose the Handwritten Balinese Character of Lontar manuscript (HBCL_DETC) dataset, a novel Balinese character in Lontar manuscripts dataset. HBCL_DETC contains 600 images that consists of more than 100,000 Balinese characters annotated by experts. Finally, the third step is training the YOLOv4 detection model using the HBCL_DETC dataset. We created this dataset specifically for the task of detecting Balinese characters in Lontar manuscripts. To evaluate the reliability of the dataset, we experimented with three scenarios. In the first scenario, the detection model was trained using original images of Lontar manuscripts, in the second scenario the detection model was trained with the addition of augmented grayscale images, and in the third scenario the detection model was trained using HBCL_DETC. Based on the experimental results, LONTAR_DETC can detect Balinese characters with high detection rate with mAP of 99.55%.

Background: In this review, we introduce the displacement encoding with stimulated echoes (DENSE) method for measuring myocardial dyssynchrony using cardiovascular magnetic resonance (CMR) imaging. We provide an overview of research findings related to DENSE from the past two decades and discuss other techniques used for dyssynchrony evaluation. Additionally, the review discusses the potential uses of DENSE in clinical practice. Methods: A search was conducted to identify relevant articles published from January 2000 through January 2023 using the Scopus, Web of Science, PubMed and Cochrane databases. The following search term was used: (DENSE OR ‘displacement encoding with stimulated echoes’ OR CURE) AND (dyssynchrony* OR asynchron* OR synchron*) AND (MRI OR ‘magnetic resonance’ OR CMR). Results: After removing duplicates, researchers screened a total of 174 papers. Papers that were not related to the topic, reviews, general overview articles and case reports were excluded, leaving 35 articles for further analysis. Of these, 14 studies focused on cardiac dyssynchrony estimation with DENSE, while the remaining 21 studies served as background material. The studies used various methods for presenting synchronicity, such as circumferential uniformity ratio estimate (CURE), CURE-singular value decomposition (SVD), radial uniformity ratio estimate (RURE), longitudinal uniformity ratio estimate (LURE), time to onset of shortening (TOS) and dyssynchrony index (DI). Most of the dyssynchrony studies concentrated on human heart failure, but congenital heart diseases and obesity were also evaluated. The researchers found that DENSE demonstrated high reproducibility and was found useful for detecting cardiac resynchronisation therapy (CRT) responders, optimising CRT device settings and assessing right ventricle synchronicity. In addition, studies showed a correlation between cardiac fibrosis and mechanical dyssynchrony in humans, as well as a decrease in the synchrony of contraction in the left ventricle in obese mice. Conclusions: DENSE shows promise as a tool for quantifying myocardial function and dyssynchrony, with advantages over other cardiac dyssynchrony evaluation methods. However, there remain challenges related to DENSE due to the relatively time-consuming imaging and analysis process. Improvements in imaging and analysing technology, as well as possible artificial intelligence solutions, may help overcome these challenges and lead to more widespread clinical use of DENSE.

A dense environmental barrier coating (EBC) was designed via aluminum infiltration into open pores or cracks to eliminate premature coating failure induced by the rapid permeation of oxidants through these pores. The water vapor corrosion performance of the EBC with and without aluminum infiltration was investigated at 1350 °C for 300 h under flowing 50%H 2 O–50%O 2 gas. The infiltrated coating exhibited decrease silicon loss by more than 80%, almost no phase decomposition, and no continuous thermally grown oxide (TGO), furthermore, TGO thickness at the crack-root decreased by ∼90%. The excellent resistance is primarily attributed to the channel pores being filled with aluminum, which eliminates the rapid permeation of oxidants. In addition, a continuous refractory ytterbium aluminum garnet (YbAG) layer was formed in situ on the EBC surface, this layer exhibited sufficient stability in steam, rendering the EBC less permeable to oxidants. [Display omitted] [ABSTRACT FROM AUTHOR]

Drones are currently being used in a wide range of useful tasks that are too dangerous or/and expensive to be performed by humans. However, this is increasingly developing security breaching issues due to the possibility of misuse of unmanned aircraft in illegal activities such as drug smuggling, terrorism, etc. Thus, the detection and tracking of drones are becoming a crucial topic. Unfortunately, due to the drone's small size, its detection methods are generally unreliable: high false alarm rate, low accuracy rate, and low detection speed are well-known aspects of this detection. The new emerging real-time algorithm based on the improved "You Only Look Once" (YOLO-V3) algorithm is proposed here for drone detection. This newly designed algorithm comprises multiple phases and has shown the potential to outperform the traditional detection approaches. The proposed algorithm enhances the performance of YOLO-V3 by designing and building a CNN to solve the problem of a large number of YOLO-V3 parameters, using densely connected modules to enhance the interlayer connection of CNNs and further strengthen the connection between dense neural network blocks, and finally improving the YOLO-V3 multiple-scale detection by expanding the three-scale to four-scale detection to increase the accuracy of detecting small objects like drones. The evaluation results of our algorithm obtain 96% on average precision and 95.60% accuracy. [ABSTRACT FROM AUTHOR]

Objectives: A novel architecture-based grading system for pancreatic ductal adenocarcinoma (PDAC) is tested against traditional grading., Methods: A total of 103 PDAC resections were graded by College of American Pathologists/American Joint Committee on Cancer (CAP/AJCC) guidelines and by a system using an architectural pattern (dispersed larger duct = low grade vs dense smaller duct = high grade). Survival analyses and interobserver variability were assessed. In total, 114 cases from a public data set were used for validation., Results: Median overall survivals were 15 and 36 months for architectural high-grade and low-grade cases, respectively (P < .001). Conversely, CAP/AJCC grading showed no survival difference between well-differentiated and moderately differentiated tumors (P = .545). Architecture-based grading remained prognostically significant for recurrence-free survival (P = .004), but CAP/AJCC grading was not (P = .226). Adjusted for stage and margin status, architectural high-grade PDACs showed a hazard ratio of 2.69 relative to low grade (P < .001) for survival. The validation cohort confirmed prognostic differences in overall (P < .001) and recurrence-free survival (P = .027) for the architecture-based system, outperforming CAP/AJCC grading. Architecture-based grading exhibited a Cohen's ĸ value of 0.710 (substantial agreement), superior to traditional grading (0.488, moderate agreement)., Conclusions: Grading PDAC based on architectural pattern results in superior prognostication and reproducibility vs CAP/AJCC grading., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective To compare the surgical efficiency and postoperative evaluation indicators between active-fluidic pressure control phacoemulsification system and gravity-fluidics in dense nuclear cataract surgery. Methods A total of 60 patients (60 eyes) with age-related cataract admitted in the First Affiliated Hospital of Army Medical University from January 2020 to December 2020 were enrolled in this prospective study, and all of them were classified as NⅣ grade according to the Lens Opacities Classification System Ⅱ (LOCS Ⅱ). They were randomized into the active-fluidics group (n=30, phacoemulsification by active-fluidic pressure control system) or the gravity-fluidics group (n=30, phacoemulsification by gravity-fluidic system). Intraoperative parameters, such as cumulative dissipated energy (CDE), ultrasound total time (UTT), torsional amplitude (TA), torsion usage time (TUT), aspiration time (AT), and fluid used (FU) were recorded and compared between the 2 groups. Postoperative evaluation indicators, like central corneal thickness (CCT), central corneal edema and thickness (CCT) at 1 d after surgery were also collected and compared. Results There were no significant differences in preoperative best corrected distance visual acuity, CCT, and corneal endothelial cell count between the 2 groups. Intraoperatively, the active-fluidics group had significantly lower CDE, UTT, TUT, AT and FU than the gravity-fluidics group (P < 0.05), though TA did not differ. Moreover, the active-fluidics group also presented better UDVA (P=0.005), less increase of CCT (P=0.006), slighter central corneal edema (P < 0.001), and smaller proportion of patients with moderate to severe central corneal edema (P < 0.001) than the gravity-fluidics group in 1 d after surgery. Conclusion The active-fluidics configuration achieves a higher surgical efficiency than the traditional gravity-fluidics system in phacoemulsification of dense nuclear cataract, with slighter postoperative corneal edema, less change of CCT and faster recovery of UDVA.

The aim of this multicentric study was to assess the impacts of oxidative stress, inflammation, and the presence of small, dense, low-density lipoproteins (sdLDL) on the antioxidative function of high-density lipoprotein (HDL) subclasses and the distribution of paraoxonase-1 (PON1) activity within HDL in patients with ST-segment elevation acute myocardial infarction (STEMI). In 69 STEMI patients and 67 healthy control subjects, the lipoproteins’ subclasses were separated using polyacrylamide gradient (3–31%) gel electrophoresis. The relative proportion of sdLDL and each HDL subclass was evaluated by measuring the areas under the peaks of densitometric scans. The distribution of the relative proportion of PON1 activity within the HDL subclasses (pPON1 within HDL) was estimated using the zymogram method. The STEMI patients had significantly lower proportions of HDL2a and HDL3a subclasses (p = 0.001 and p < 0.001, respectively) and lower pPON1 within HDL3b (p = 0.006), as well as higher proportions of HDL3b and HDL3c subclasses (p = 0.013 and p < 0.001, respectively) and higher pPON1 within HDL2 than the controls. Independent positive associations between sdLDL and pPON1 within HDL3a and between malondialdehyde (MDA) and pPON1 within HDL2b were shown in the STEMI group. The increased oxidative stress and increased proportion of sdLDL in STEMI are closely related to the compromised antioxidative function of small HDL3 particles and the altered pPON1 within HDL.

While establishing the bijection between near-ring congruences and various types of ideals of seminearrings, different kinds of restrictions were imposed either on the seminearrings under consideration or on the near-ring congruences. In this paper we consider a seminearring S without any restriction and establish that there exists an inclusion preserving bijective correspondence between the set { I ⊆ S : I is a strong, dense, reflexive and closed additive subsemigroup of S with I S ⊆ I } and the set of all near-ring congruences on S. We also show that in any seminearring S, there exists an inclusion preserving bijective correspondence between the set { I ⊆ S : I is a strong, dense, reflexive and closed additive subsemigroup of S with I S , S I ⊆ I } and the set of all zero-symmetric near-ring congruences on S. [ABSTRACT FROM AUTHOR]

The increased usage of online social networks in recent days attracts the attention of researchers. That makes analysis of social networks a significant concern. Link prediction is one of the problem of social networks, where links are predicted between users by analyzing the relationships between the nodes. As the size and usability of the social network grows, the accurate prediction of connections with limited public information is a challenge. This article introduces a novel approach for predicting missing links using a nature‐inspired method, the gravitational search algorithm. We selected seven real‐world networks and five widely used algorithms and also considered the sparsity of networks for the experimental evaluation of the proposed approach. The proposed GSA‐driven algorithm has shown a better or comparable AUC result than the other standard algorithms on these networks. [ABSTRACT FROM AUTHOR]

Behind the science of breast cancer in Canada, as well as globally, are the stories of thousands of women, their families, and their communities. These include stories from those who have died or those suffering from the realities of stage III and stage IV breast cancer due to late detection, misinformation, and dismissal. The reality for these women is that, whilst grateful for the latest developments in cancer research, much of this knowledge is not reflected in policy and practice. Canadian guidelines do not reflect the recommended screening by experts within the field and inequities in screening practices and practitioner knowledge exist in different areas within Canada. Told through the stories of women with lived experiences of late-stage breast cancer and supported by scientific evidence, this paper explores the impact of outdated breast cancer screening practices on the lives of women. Recent patient advocacy is driving changes, such as notifying women of their breast density in a few jurisdictions in Canada, but we call for the whole medical community to take responsibility and ensure breast screening is optimised to save more lives. [ABSTRACT FROM AUTHOR]

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Background: While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers. Methods: Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland–Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4. Results: Based on CV and ICC, segmental Ecc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good–excellent inter-scan reproducibility. Whole-slice Ecc and global Ell provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good–excellent for all comparisons. For whole-slice Err, CV was in the fair-good range, and ICC was in the good–excellent range. Conclusions: Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental Ecc, global Ell, and torsion measurements in healthy subjects and heart disease patients. [ABSTRACT FROM AUTHOR]

Abstract Background Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. Methods Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. Results LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p

Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image. Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods. However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map. We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning. We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning. We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones. We show that our method obtains comparable or better results to previous deep learning models, which are texture specific. It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures. Our implementation runs in real-time, with a low-cost GPU and CPU.

Abstract Background Several cardiovascular magnetic resonance (CMR) techniques can measure myocardial strain and torsion with high accuracy. The purpose of this study was to compare displacement encoding with stimulated echoes (DENSE), tagging and feature tracking (FT) for measuring circumferential and radial myocardial strain and myocardial torsion in order to assess myocardial function and infarct scar burden both at a global and at a segmental level. Method 116 patients with a high likelihood of coronary artery disease (European SCORE > 15%) underwent CMR examination including cine images, tagging, DENSE and late gadolinium enhancement (LGE) in the short axis direction. In total, 97 patients had signs of myocardial disease and 19 had no abnormalities in terms of left ventricular (LV) wall mass index, LV ejection fraction, wall motion, LGE or a history of myocardial infarction. Thirty-four patients had myocardial infarct scar with a transmural LGE extent (transmurality) that exceeded 50% of the wall thickness in at least one segment. Global circumferential strain (GCS) and global radial strain (GRS) was analyzed using FT of cine loops, deformation of tag lines or DENSE displacement. Results DENSE and tagging both showed high sensitivity (82% and 71%) at a specificity of 80% for the detection of segments with > 50% LGE transmurality, and receiver operating characteristics (ROC) analysis showed significantly higher area under the curve-values (AUC) for DENSE (0.87) than for tagging (0.83, p 50% scar with a higher AUC than strain determined from tagging and FT at a segmental level. GCS and torsion computed from DENSE and tagging showed similar correlation with global scar size, while when computed from FT, the correlation was lower.

Research purpose: The research aims to identify the most important visionary pillars that distinguish the experience of the poet Abdul-Malik Boumengel from other poetic experiences in their subjective and collective dimensions, and the attitudes that result from that towards current events, and the functions and effects of this experience. Such dimensions are aesthetically and intellectually concentrated in the dynamism of reality, and they establish a conscious presence in the profound variables of the human process. Study methodology: The research depends on the mechanisms of description and analysis and approaches texts intellectually and aesthetically through the data of systematic approaches such as stylistic and semiotic approaches. It is also open to the social approach and the data of cultural criticism to approach the relationship between poetry and reality. Findings: The researcher was able to determine the peculiarity of Abdul Malik Bomingle's poetry, and its relationship with reality, and the nature of the specific feedback of the relationship, and hence it was possible to realize the visual value of the poetic experience.Research originality: The research explores a field that researchers did not pay attention to in studying the experience of Abdul Malik Boumengel, being the levels of realistic return and their effect on determining the visual specificity of his poetic experience.

Annotation: In article the incorrect task for abstract polycaloric the equation is studied and a stability assessment according to Tikhonov is given. [ABSTRACT FROM AUTHOR]

Purpose: Right ventricular (RV) function is increasingly recognized for its prognostic value in many disease states. As with the left ventricle (LV), strain-based measurements may have better prognostic value than typical chamber volumes or ejection fraction. Complete functional characterization of the RV requires high-resolution, 3D displacement tracking methods, which have been prohibitively challenging to implement. Zonal excitation during Displacement ENcoding with Stimulated Echoes (DENSE) magnetic resonance imaging (MRI) has helped reduce scan time for 2D LV strain quantification. We hypothesized that zonal excitation could alternatively be used to reproducibly acquire higher resolution, 3D-encoded DENSE images for quantification of bi-ventricular strain within a single breath-hold. Methods: We modified sequence parameters for a 3D zonal excitation DENSE sequence to achieve in-plane resolution < 2 mm and acquired two sets of images in eight healthy adult male volunteers with median (IQR) age 32.5 (32.0–33.8) years. We assessed the inter-test reproducibility of this technique, and compared computed strains and torsion with previously published data. Results: Data for one subject was excluded based on image artifacts. Reproducibility for LV (CoV: 6.1–9.0%) and RV normal strains (CoV: 6.3–8.2%) and LV torsion (CoV = 7.1%) were all very good. Reproducibility of RV torsion was lower (CoV = 16.7%), but still within acceptable limits. Computed global strains and torsion were within reasonable agreement with published data, but further studies in larger cohorts are needed to confirm. Conclusion: Reproducible acquisition of 3D-encoded biventricular myocardial strain data in a breath-hold is feasible using DENSE with zonal excitation. [ABSTRACT FROM AUTHOR]

Approximations of nonhomogeneous discrete Markov chains (NDMC) play an essential role in both probability and statistics. In all these settings, it is crucial to consider random variables in appropriate spaces. Therefore, the abstract considerations of such spaces lead to investigating the approximations in ordered Banach space scheme. In this paper, we consider two topologies on the set of NDMC of abstract state spaces. We establish that the set of all uniformly P-ergodic NDMC is norm residual in NDMC. The set of point-wise weak P-ergodic NDMC is also considered and such sets are shown to be a Gd-subset (in strong topology) of NDMC. We point out that all the deduced results are new in the classical and non-commutative probabilities, respectively, since in most of earlier results the limiting projection is taken as a rank one projection. Indeed, the obtained results give new insight into data-analysis and statistics. [ABSTRACT FROM AUTHOR]

Purpose: We propose a new method, displacement spectrum (DiSpect) imaging, for probing in vivo complex tissue dynamics such as motion, flow, diffusion, and perfusion. Based on stimulated echoes and image phase, our flexible approach enables observations of the spin dynamics over short (milliseconds) to long (seconds) evolution times. Methods: The DiSpect method is a Fourier‐encoded variant of displacement encoding with stimulated echoes, which encodes bulk displacement of spins that occurs between tagging and imaging in the image phase. However, this method fails to capture partial volume effects as well as blood flow. The DiSpect variant mitigates this by performing multiple scans with increasing displacement‐encoding steps. Fourier analysis can then resolve the multidimensional spectrum of displacements that spins exhibit over the mixing time. In addition, repeated imaging following tagging can capture dynamic displacement spectra with increasing mixing times. Results: We demonstrate properties of DiSpect MRI using flow phantom experiments as well as in vivo brain scans. Specifically, the ability of DiSpect to perform retrospective vessel‐selective perfusion imaging at multiple mixing times is highlighted. Conclusion: The DiSpect variant is a new tool in the arsenal of MRI techniques for probing complex tissue dynamics. The flexibility and the rich information it provides open the possibility of alternative ways to quantitatively measure numerous complex spin dynamics, such as flow and perfusion within a single exam. [ABSTRACT FROM AUTHOR]

Purpose: To use deep learning for suppression of the artifact‐generating T1‐relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time. Methods: A U‐Net was trained to suppress the artifact‐generating T1‐relaxation echo using complementary phase‐cycled data as the ground truth. A data‐augmentation method was developed that generates synthetic DENSE images with arbitrary displacement‐encoding frequencies to suppress the T1‐relaxation echo modulated for a range of frequencies. The resulting U‐Net (DAS‐Net) was compared with k‐space zero‐filling as an alternative method. Non‐phase‐cycled DENSE images acquired in shorter breath‐holds were processed by DAS‐Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain. Results: The DAS‐Net method effectively suppressed the T1‐relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS‐Net method outperformed zero‐filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS‐Net vs zero‐filling, P <.01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS‐Net vs zero‐filling, P <.01). Strain data for non‐phase‐cycled DENSE images with DAS‐Net showed close agreement with strain from phase‐cycled DENSE. Conclusion: The DAS‐Net method provides an effective alternative approach for suppression of the artifact‐generating T1‐relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase‐cycling. [ABSTRACT FROM AUTHOR]