Threshold Method Research Articles

Noise Reduction Using Sparsity Constrained and Regularized Iterative Thresholding Algorithm and Dictionary

Dictionary-based methods are recognized for their ability to estimate clear speech from speech contaminated with noise. However, these techniques face a limitation in distinguishing between speech and noise components. The presented algorithm addresses this challenge by introducing an innovative solution through an iterative thresholding method. In this method, the thresholding is based on noise characteristics and remains independent of variations in noise power. To determine the stopping criteria for thresholding, the algorithm leverages the structure of the speech signal in time–frequency domains using the Gini Index. Remarkably, this technique adeptly extracts reliable signal components from a noisy time–frequency magnitude spectrum, performing well under non-varying and varying Signal-to-Noise Ratio (SNR) conditions. Importantly, it achieves this without the need for mask functions, voice activity detection techniques, noise or mixture dictionaries, or SNR information, which are essential components in other dictionary-based methods. Following the thresholding process, the clean speech is assessed using a dictionary-based approach to restore perceptual loss. In evaluating its performance, the algorithm is compared with traditional enhancement techniques concerning perceptual evaluation of speech quality and short-time objective intelligibility. The assessment is conducted under various background noise conditions, including babble, white, factory, and Volvo noises. The proposed algorithm exhibits superior performance, particularly in low and varying SNR scenarios.

Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground-background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT-BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis.

MDD brain network analysis based on EEG functional connectivity and graph theory

BackgroundExisting studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem. New methodThis paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction. ResultsIn theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases. Comparison with existing methodsThe results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability. ConclusionsMDD has brain dysfunction, particularly in the frontal and temporal lobes.

Upregulation of Serum miR-132 and miR-152 in Vietnamese Patients with Heart Failure.

MicroRNAs (miRs) are emerging targets for the diagnosis, prognosis and treatment of heart failure (HF). Accumulated evidence showed that microRNA-132 (miR-132) and microRNA-152 (miR-152) play critical roles in the development of multiple pathological processes of the heart. Although their upregulations have been detected in the failing hearts of humans and animal models, little is known about the circulating levels of miR-132 and miR-152 in patients with HF. Our study was conducted from January 2022 to August 2022 at the Cardiology Department of the University Medical Center in Ho Chi Minh City, Vietnam. During study period, 36 participants were consecutively enrolled, including 18 HF patients and 18 patients who age and sex matched the non-HF controls. Serum samples of study participants were collected on admission and the expression levels of miR-132 and miR-152 were measured by quantitative reverse transcription polymerase chain reaction (RT-PCR). The comparative cycle threshold method (ΔCt) was applied to calculate the relative expression of miRs. The miR concentration in HF group was significantly lower than that in the control group. In contrast, the serum levels of miR-132 and miR-152 were significantly higher in HF patients. Further analyses of receiver operating characteristic (ROC) curve showed that miR-132 and miR-152 individually had moderate diagnostic potential for HF (with area under curve [AUC] values of 0.713 and 0.698, respectively). A positive correlation between these miRs was also confirmed. Serum miR-132 and miR-152 were upregulated in Vietnamese patients with HF and may serve as candidate biomarkers for diagnostic purposes.

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks

Complex-valued convolutional neural networks (CVCNNs) have been demonstrated effectiveness in classifying complex signals and synthetic aperture radar (SAR) images. However, due to the introduction of complex-valued parameters, CVCNNs tend to become redundant with heavy floating-point operations. Model sparsity is emerged as an efficient method of removing the redundancy without much loss of performance. Currently, there are few studies on the sparsity problem of CVCNNs. Therefore, a complex-valued soft-log threshold reweighting (CV-SLTR) algorithm is proposed for the design of sparse CVCNN to reduce the number of weight parameters and simplify the structure of CVCNN. On one hand, considering the difference between complex and real numbers, we redefine and derive the complex-valued log-sum threshold method. On the other hand, by considering the distinctive characteristics of complex-valued convolutional (CConv) layers and complex-valued fully connected (CFC) layers of CVCNNs, the complex-valued soft and log-sum threshold methods are respectively developed to prune the weights of different layers during the forward propagation, and the sparsity thresholds are optimized during the backward propagation by inducing a sparsity budget. Furthermore, different optimizers can be integrated with CV-SLTR. When stochastic gradient descent (SGD) is used, the convergence of CV-SLTR is proved if Lipschitzian continuity is satisfied. Experiments on the RadioML 2016.10A and S1SLC-CVDL datasets show that the proposed algorithm is efficient for the sparsity of CVCNNs. It is worth noting that the proposed algorithm has fast sparsity speed while maintaining high classification accuracy. These demonstrate the feasibility and potential of the CV-SLTR algorithm.

A threshold method of evaluating the critical trimming stage of FAP surface based on interface friction

Trimming technology is crucial for fixed abrasive pads (FAP) to maintain peak performance and prolonging their lifespan. However, there is a lack of effective evaluation methods for trimming FAP, which leads to under-trimming and over-trimming of FAP. As a result, it is very important to recognize and evaluate the critical trimming stage of the FAP. Therefore, the discriminative method for trimming FAP based on interfacial friction is proposed to determine the optimal trimming stage of FAP. Firstly, according to lapping experiments and tribological experiments of the FAP, the mapping relationship between the characteristics of interfacial friction and the parameters related to the FAP surface has been established. Secondly, the specific indices were identified to evaluate the surface state of FAP by the SEM analysis and mapping relationship of the FAP. Finally, the trimming thresholds of FAP were determined by combining the receiver operating characteristic (ROC) curves and Youden index, and the threshold achieved an average accuracy of over 92 % in recognizing the trimming requirements of the FAP. This research provides the theoretical basis for the online trimming technology and intelligent processing of FAP.

The surrogate model for short-term extreme response prediction based on ANN and Kriging algorithm

Numerical simulation is a common method for calculating the short-term extreme response of floating offshore wind turbines (FOWTs). However, it requires significant computational resources. This study presents a dynamic response database for a 5MW semi-submersible FOWT under complex environmental conditions, including wind speed, effective wave height, and wave spectral peak period, using a numerical model. The peak over threshold (POT) method can be used to obtain the parametric database of short-term extreme responses, which includes the short-term extreme response distribution parameters for four responses: float surge, mooring tension, outward bending moment at the leaf root surface (OoPBM) and tower base pitching moment (TBPM). And the parameter database is applied to train models such as the Genetic Algorithm optimization Back Propagation neural network (GA-BP) and Kriging algorithm models. The research indicates that a correlation can be established between environmental conditions and short-term extreme response parameters using two algorithms. The accuracy of surrogate model prediction for some parameters can be improved by grouping the data based on wind speed and training separately. Additionally, selecting the appropriate surrogate model for each parameter separately can improve the accuracy of short-term extreme response prediction.

Remote sensing monitoring of irrigated area in the non-growth season and of water consumption analysis in a large-scale irrigation district

Globally, water scarcity threatens food security, especially in arid and semi-arid food-producing regions, such as the Hetao Irrigation District (HID), where the water consumption during the non-growth season (spring irrigation, autumn irrigation) accounts for more than 30 % of the total irrigation volume. Meanwhile, mapping the characteristics of spatial and temporal evolution of the area of cropland irrigated in the spring and autumn and the pattern of water consumption is crucial for strengthening the management of agricultural water use. Evaporation and infiltration of irrigation water are difficult to capture in time due to insufficient timeliness or accuracy of single remote sensing data. This study integrates seven remote sensing datasets (Landsat5, Landsat7, Landsat8, HJ-1A/B, GF-1, Sentinel-2 and MODIS) for the first time to build a high spatial-temporal resolution dataset, and combines the water index NDWI and MNDWI to extract the maximum water range formed after irrigation by manually adjusting the threshold method. In addition to the global land use/cover dataset (GLC FCS30–1985_2020), the cropland layer was superimposed to accurately identify the cropland spring irrigation and autumn irrigation range from 2000 to 2021 in the HID. In the past 20 years, spring-irrigated (SI) areas increased by 1012.60 km2 (+49.96 %), and the autumn-irrigated (AI) areas decreased by 512.63 km2 (-11.02 %). Combined with the quantity of water diversion and displacement, the characteristics of irrigation water consumption in the non-growth season were further analysed. The results showed that the water consumption of spring irrigation increased by 0.67×108 m3 (+22.26 %), and that of autumn irrigation increased by 4.34×108 m3 (+38.82 %). The decrease of autumn- irrigated area and the increase of water consumption were mainly related to the stability of autumn irrigation water, imperfect irrigation and drainage conditions and insufficient field water management. In 2020­2021, the area of non-growth “autumn irrigation - spring irrigation” repeated irrigation is about 1271.52 km2, which can be used as a key control area. This study provides a theoretical basis and direction for rational water use in non-growth seasons of irrigation districts.

US-Net: U-shaped network with Convolutional Attention Mechanism for ultrasound medical images

Ultrasound imaging, characterized by low contrast, high noise, and interference from surrounding tissues, poses significant challenges in lesion segmentation. To tackle these issues, we introduce an enhanced U-shaped network that incorporates several novel features for precise, automated segmentation. Firstly, our model utilizes a convolution-based self-attention mechanism to establish long-range dependencies in feature maps, crucial for small dataset applications, accompanied by a soft thresholding method for noise reduction. Secondly, we employ multi-sized convolutional kernels to enrich feature processing, coupled with curvature calculations to accentuate edge details via a soft-attention approach. Thirdly, an advanced skip connection strategy is implemented in the UNet architecture, integrating information entropy to assess and utilize texture-rich channels, thereby improving semantic detail in the encoder before merging with decoder outputs. We validated our approach using a newly curated dataset, VPUSI (Vascular Plaques Ultrasound Images), alongside the established datasets, BUSI, TN3K and DDTI. Comparative experiments on these datasets show that our model outperforms existing state-of-the-art techniques in segmentation accuracy.

Deep spatial–spectral difference network with heterogeneous feature mutual learning for sea fog detection

Multispectral remote sensing image-based sea fog detection (SFD) is both important and challenging. Deep learning methods for SFD have become mainstream due to their powerful nonlinear learning capabilities and flexibility. However, existing methods have not fully utilized the physical difference priors in multispectral images (MSI) for SFD, making it difficult to skillfully capture the shape and appearance characteristics of sea fog, thus leading to uncertainties in SFD. We propose the spatial–spectral difference network (S2DNet), a deep encoding–decoding framework that merges inter-spectral and intra-spectral heterogeneous difference features. Specifically, inspired by physics-based difference threshold methods, we developed a physics-inspired inter-spectral difference module (PIDM) that combines feature-level difference with deep neural networks to capture the shape characteristics of sea fog. We designed the intra-spectral difference module (ISDM) using difference convolution to represent sea fog’s fine-grained and dynamic appearance information. Furthermore, inspired by multi-view learning, we propose heterogeneous feature mutual learning (HFML) that seeks robust representations by focusing on semantically invariant aspects within heterogeneous difference features, adapting to the dynamic nature of sea fog. HFML is achieved through global feature mutual learning using an adversarial procedure and local feature mutual learning supported by a novel information-theoretic objective function that links maximizing statistical correlation with expectation maximization. Experiments on two SFD datasets show that integrating physical difference priors into deep learning improves SFD. In both continuous temporal and high spatial resolution SFD tasks, S2DNet outperforms existing advanced deep learning methods. Moreover, S2DNet demonstrates stronger robustness under degraded remote sensing image conditions, highlighting its potential usefulness and practicality in real-world applications.

Molecular characterisation of hepatitis A in the Western Cape province, South Africa in 2023

In 2023, passive laboratory-based surveillance showed an increase in hepatitis A virus (HAV) infection. We investigated hepatitis A incidence using the notifiable medical condition surveillance system (NMCSS) data and molecularly characterised positive blood samples from the Western Cape province for 2023. All HAV IgM seropositive cases from the NMCSS from 1 January to 31 October 2023 in South Africa were investigated. HAV RNA from blood samples that had tested positive for HAV IgM from Western Cape was amplified in the VP1/P2B junction and sequenced (3500Xl Genetic Analyser). Sequences were assembled, aligned (Sequencher) and analysed (Aliview 1.27 and MEGA11). Statistical analysis was performed using Excel and the CuSum2 Threshold to determine suspected outbreaks. In 2023, the incidence of HAV IgM was 6.28/100,000 in South Africa, with the highest incidence in Western Cape province (15.86/100,000). Children aged 5 to 14 years were affected the most in the Western Cape. The positive cases in the Western Cape were above the CuSum2 threshold from January to May 2023, with the highest incidence observed in the City of Cape Town Metropolitan (14.8/100,000). Genotyping was successfully performed on 92.7% (139/150) of serum samples, for which the IB sub-genotype was detected. Three primary mutations R63K, R71S and M104I were observed in more than 49% of the samples. Most of the samples sequenced belonged to patients residing in areas close to each other within the City of Cape Town Southern, Western, and Mitchells Plain sub-districts. The CuSum2 threshold method allowed the identification of suspected HAV outbreaks in the districts within the Western Cape in 2023 while genotyping identified clusters of sub-genotype IB. Genotyping could assist with determining the common source of infection during an outbreak, especially if coupled with epidemiological and geographical data. Further active surveillance can assist in investigating the HAV risk factors for targeted public health responses.

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases. The article aims to review selected methods for segmenting lung medical images based on computed tomography (CT) image data analysis. Included in the analysis are 11 different approaches, including edge algorithms such as Canny, Prewitt, Roberts, Sobel and Log, clustering-based algorithms such as Fuzzy C-means, genetic K-means, k-nearest neighbours, and neural networks such as perceptron, neural and fuzzy sets, and edge binarization techniques, among others. Each method was evaluated for its ability to segment lung structures on CT images accurately. Finally, the contour of the heart from the CT image was determined using the maximum entropy thresholding method. The juxtaposition of different approaches to medical image segmentation is an important contribution to developing medical diagnostic techniques, which can help improve the efficiency of diagnosing lung diseases.

Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping

Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study, based on the Harmonic Models constructed from Long Time-series Surface Water (LTSW) dynamics, an SIA extraction approach (SHM) was introduced to enhance their accuracy and robustness, thereby improving flood mapping. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Sentinel-1/2 remote sensing data were utilized to extract LTSW. Harmonic analysis was applied to the LTSW dataset, using the amplitude terms in the harmonic model to characterise the frequency of variation between land and water for the surface units, thus extracting the SIAs. The results reveal that the harmonic model parameters are capable of portraying SIA. In comparison to the commonly used WIF thresholding method for SIA extraction, the SHM approach demonstrates superior accuracy and robustness. Leveraging the SIA extracted through SHM, a higher level of accuracy in FIA extraction is achieved. Overall, the SHM offers notable advantages, including high accuracy, automation, and robustness. It offers reliable reference water extents for flood mapping, especially in areas with active and complex hydrological dynamics. SHM can play a crucial role in emergency response to flood disasters, providing essential technical support for natural disaster management and related departments.

Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).

Microbiome data exhibit technical and biomedical heterogeneity due to varied processing and experimental designs, which may lead to spurious results if uncorrected. Here, we introduce the Quantile Thresholding (QuanT) method, a comprehensive non-parametric hidden variable inference method that accommodates the complex distributions of microbial read counts and relative abundances. We apply QuanT to synthetic and real data sets and demonstrate its ability to identify unmeasured heterogeneity and improve downstream analysis.

Development of a leaf area index-based relative threshold method for identifying agricultural drought areas

Considering the global aggravated agricultural drought condition, the availability of reliable historical agricultural drought information with high spatiotemporal resolution is crucial for accurate drought monitoring, effective water resources management, and sustainable agricultural development. However, the coarse spatiotemporal resolution of available historical agricultural drought datasets imposes great limitations on drought prevention and mitigation. Recognizing the research gap, this study developed a novel approach of leaf area index (LAI) relative thresholds to generate a 500 m-resolution agricultural drought areas dataset in the North China Plain (NCP) spanning the timeframe from 2006 to 2019. A range of key LAI relative thresholds were established to capture various levels of agricultural drought severity. Subsequently, a 500 m-resolution agricultural drought area dataset was generated, encompassing critical parameters of drought-covered area, drought-damaged area, and crop failure area for both summer-harvest and autumn-harvest seasons in each year. The relative thresholds for drought-covered area, drought-damaged area, and crop failure area yielded percentages of 56 %, 51 %, 34 % for summer-harvest crops and 45 %, 41 %, 28 % for autumn-harvest crops, respectively. To validate the credibility of the generated approach, historical agricultural drought areas data from the Bulletin of Flood and Drought Disasters in China were juxtaposed across various harvest seasons and multiple years. The spatial verification in the Hebei Province (one main province of the NCP) revealed a remarkable consistency between the newly generated dataset and the authentic dataset, demonstrating correlation efficiency estimates ranging from 0.70 to 0.83. The developed approach gives insights into the spatial distribution and coherence of agricultural drought impacted areas and sheds light on revealing the inter-annual dynamics of crop growing seasons. It can support for impact-based agricultural drought monitoring and prediction, and subsequently assist in optimizing agricultural water management and ensuring global food security.

Scatter estimation and correction using time-of-flight and deconvolution in x-ray medical imaging.

Objective Time-of-flight (TOF) scatter rejection requires a total timing jitter,
including the detector timing jitter and the X-ray source's pulses width, of 50 ps or less
to mitigate most of the effects of scattered photons in radiography and CT imaging.
However, since the total contribution of the source and detector to the timing jitter
can be retrieved during an acquisition with nothing between the source and detector,
it can be demonstrated that this contribution may be partially removed to improve
the image quality.
Approach A scatter correction method using iterative deconvolution of the measured
time point-spread function estimates the number of scattered photons detected in each
pixel. To evaluate the quality of the estimation, GATE was used to simulate the
radiography of a water cylinder with bone inserts, and a head and torso in a system
with total timing jitters from 100 ps up to 500 ps full-width-at-half-maximum.
Main results With a total timing jitter of 200 ps, 89% of the contrast degradation
caused by scattered photons was recovered in a head and torso radiography, compared
to 28% with a simple time threshold method. Corrected images using the estimation
have a percent root-mean square error between 2 and 14% in both phantoms with
timing jitters from 100 to 500 ps which is lower than the error achieved with scatter
rejection alone at 100 ps.
Significance TOF X-ray imaging has the potential to mitigate the effects of the
scattering contribution and offers an alternative to anti-scatter grids that avoids loss
of primary photons. Compare to simple TOF scatter rejection using only a threshold,
the deconvolution estimation approach has lower requirements on both the source and
detector. These requirements are now within reach of state-of-the-art systems.

Unveiling the quantitative relationship between microstructural features and quasi-static tensile properties in dual-phase titanium alloys based on data-driven neural networks

The quasi-static mechanical properties of α+β dual-phase titanium alloys are susceptible to their microstructural features, presenting a complex, high-dimensional nonlinear relationship, which hinders the rapid development of high-performance materials. In this work, 4065 micro-representative models were virtually constructed with varying volume fractions of α and β phases and characteristic dimensions via high-throughput finite element simulation, incorporating a cohesive zone model to simulate the interfaces between the two phases. Especially, the established representative models were experimentally verified by two groups of real material microstructures, and the results showed that the relative errors were not more than 9.5 % in microstructural characteristics and quasi-static mechanical properties. Afterward, a neural network model was developed to correlate the quasi-static tensile properties with the microstructural features of the dual-phase TC6 titanium alloys, achieving an 88.2 % accuracy in predicting overall mechanical performance. Utilizing the Shaply Additive Explanation method, it was found that the primary α phase's volume fraction and the secondary α phase's width were the most significant microstructural features affecting quasi-static strength. Specifically, the volume fraction of the primary α phase and the width of the secondary α phase negatively affected strength, while the width of the secondary α phase positively influenced plasticity. Notably, the primary α phase's volume fraction had a quadratic curve pattern of influence on plasticity. The intrinsic mechanisms behind these laws were further revealed based on local stress-strain responses and crack propagation analysis. Ultimately, the optimal microstructural features with strength-plasticity balance were identified through the lower threshold method: a secondary α phase width of about 1 μm and a primary α phase volume fraction ranging from 0.1 to 0.2, effectively facilitating microstructure design.

Signal processing method of ultrasonic gas flowmeter based on transit-time mathematical characteristics

To address the issue of timing errors caused by attenuation and jitter in the echo signal with the conventional threshold method, this paper presents a signal processing method for ultrasonic gas flow meters based on the mathematical characteristics of transit time. First, an improved weighted recursive filtering algorithm based on the mathematical characteristics of transit time is proposed. Then,based on the aforementioned filtering algorithm, a feedback-type threshold adjustment method has been proposed. Finally, the results for the flow meter meeting the performance criteria for a Class 1 accuracy ultrasonic gas flow meter at range of 2∼300 m3/h. This signal processing technique can be implemented at comparatively low cost on existing conventional fixed-threshold flow measurement infrastructures, with the potential to significantly improve accuracy and reliability.

Evolution stage identification and spatial pattern analysis of Nitraria tangutorum nebkhas based on Gaofen-2 satellite data

The quantitative extraction and evolution stage identification of the Nitraria tangutorum nebkhas are the basis for the restoration of regional plants and the reconstruction of degraded ecosystems. In this paper, the Nitraria tangutorum nebkha in Dengkou County of China was taken as the research object. Through the spectral and texture information of Gaofen-2 satellite image, the quantitative extraction of Nitraria tangutorum nebkha area and coverage information was completed using methods of gray threshold method, mathematical morphology, FCLSU mixed pixel decomposition, kernel density spatial analysis; the current evolution stage of the Nitraria tangutorum nebkha was identified, and their spatial distribution characteristics were analyzed. The results showed that: (1) The user accuracy and mapping accuracy of Nitraria tangutorum nebkha extracted from Random Forest combined with object-oriented classification method were up to 90.32%. (2) The method proposed can achieve an accuracy of 93.76% in extracting the spatial position of Nitraria tangutorum nebkhas. (3) The evolution of Nitraria tangutorum nebkhas can be divided into three stages: embryonic or developmental stage, stable stage, and declining stage, with a proportion of 60.70%, 20.97%, and 18.33%, respectively; The Nitraria tangutorum nebkhas in the study area is mainly in their embryonic or developmental stage, and the proportion of Nitraria tangutorum nebkhas in the declining stage is also large. It can provide technical and theoretical support for the precise extraction of nebkhas in arid and semi-arid desert areas, the identification of their current evolutionary stages, and the study of their spatial distribution patterns.

Conditions for late gadolinium enhancement MRI in myocardial infarction model rats that better reflect microscopic tissue staining

Late gadolinium enhancement (LGE) is a widely used magnetic resonance imaging method for assessing cardiac disease. However, the relationship between different LGE signal thresholds and microscopic tissue staining images is unclear. In this study, we performed cardiovascular MRI on myocardial infarction (MI) model rats and evaluated the relationship between LGE with different signal thresholding methods and tissue staining images. We prepared 16 rats that underwent MRI 14–18 days following a surgery to create an MI model. We captured cine and LGE images of the cardiac short-axis and longitudinal two- and four-chamber views. The mean ± 2SD, ± 3SD, and ± 5SD of the pixel values in the non-infarcted area were defined as the LGE area. We compared areas of Sirius red staining, determined by the color tone, with their respective LGE areas at end-diastole and end-systole. We observed that the LGE area calculated as the mean ± 2SD of the non-infarcted area at end-diastole demonstrated a significant positive correlation with the area of Sirius red staining (Pearson’s correlation coefficient in both: 0.81 [p < 0.01]). Therefore, the LGE area calculated as the mean ± 2SD of the non-infarcted area at end-diastole best reflected the MI area in tissue staining.