Original Filter Research Articles

Accuracy of radiomics-Based models in distinguishing between ruptured and unruptured intracranial aneurysms: A systematic review and meta-Analysis

IntroductionIntracranial aneurysms (IAs) pose a severe health risk due to the potential for subarachnoid hemorrhage upon rupture. This study aims to conduct a systematic review and meta-analysis on the accuracy of radiomics features derived from computed tomography angiography (CTA) in differentiating ruptured from unruptured IAs. Materials and MethodsA systematic search was performed across multiple databases for articles published up to January 2024. Observational studies analyzing CTA using radiomics features were included. The area under the curve (AUC) for classifying ruptured vs. unruptured IAs was pooled using a random-effects model. Subgroup analyses were conducted based on the use of radiomics-only features versus radiomics plus additional image-based features, as well as the type of filters used for image processing. ResultsSix studies with 4,408 patients were included. The overall pooled AUC for radiomics features in differentiating ruptured from unruptured IAs was 0.86 (95% CI: 0.84–0.88). The AUC was 0.85 (95% CI: 0.82–0.88) for studies using only radiomics features and 0.87 (95% CI: 0.83–0.91) for studies incorporating radiomics plus additional image-based features. Subgroup analysis based on filter type showed an AUC of 0.87 (95% CI: 0.83–0.90) for original filters and 0.86 (95% CI: 0.81–0.90) for studies using additional filters. ConclusionRadiomics-based models demonstrate very good diagnostic accuracy in classifying ruptured and unruptured IAs, with AUC values exceeding 0.8. This highlights the potential of radiomics as a useful tool in the non-invasive assessment of aneurysm rupture risk, particularly in the management of patients with multiple aneurysms.

Assessment of the physiochemical properties and sustainability of locally produced ceramic G-filter in a long term experiment

In India, Ceramic Water Filters (CWF) named G-filters are becoming a point-of-use household water treatment solution. The filter's reliability may be primarily characterized by the filtrate turbidity and microbial removal efficiency. The temperature and moisture gradients of the location affect its performance. This paper describes a 19-week laboratory investigation of G-filters simulating field conditions. The basic objective during the study was to ascertain the effective useable life of the filter. The discharge rate and turbidity reduction ability of the filters are studied in depth. The G-filters were prepared using locally available salty clay and sawdust taken in equal volume fractions. A total of 30 filters with a 9-liter capacity each were used for the experiments. The results showed that CWFs can reduce turbidity effectively. The influent water used for the study had a mean turbidity of 7.65 NTU, which reduced in the range of 0.03 NTU to 1.03 NTU. Over the course of four months, the average flow rate decreased from 1.94 Lh-1 to 0.93 Lh-1. Although a soft brushing of filters improved the filtration rate but that too failed in the long run. Brunauer–Emmett–Teller (BET) nitrogen adsorption–desorption measurements were used to determine the variation in pore size of filters with time. An average pore radius of 2.91 nm was found in the original filter that has not been used for filtration experiments. Moreover, an average pore radius of 1.77 nm, 1.21 nm, and 1.22 nm were reported in G-filters through which 427.5 L, 807.5 L, and 1035.5 L, respectively, of water were passed. This reduction in pore size is attributed to the clogging of pores over a period of time. This study aims to collect performance data to establish a scientific model using statistical analysis for implementing CWF technology in developing nations.

Enhancing YOLO for occluded vehicle detection with grouped orthogonal attention and dense object repulsion

In real-life complex traffic environments, vehicles are often occluded by extraneous background objects and other vehicles, leading to severe degradation of object detector performance. To address this issue, we propose a method named YOLO-OVD (YOLO for occluded vehicle detection) and a dataset for effectively handling vehicle occlusion in various scenarios. To highlight the model attention in unobstructed region of vehicles, we design a novel grouped orthogonal attention (GOA) module to achieve maximum information extraction between channels. We utilize grouping and channel shuffling to address the initialization and computational issues of original orthogonal filters, followed by spatial attention for enhancing spatial features in vehicle-visible regions. We introduce a CIoU-based repulsion term into the loss function to augment the network’s localization accuracy in scenarios involving densely packed vehicles. Moreover, we explore the effect of the knowledge-based Laplacian Pyramid on the OVD performance, which contributes to fast convergence in training and ensures more detailed and comprehensive feature retention. We conduct extensive experiments on the established occluded vehicle detection dataset, which demonstrates that the proposed YOLO-OVD model significantly outperforms 14 representative object detectors. Notably, it achieves improvements of 4.7% in Precision, 3.6% in AP@0.5, and 1.9% in AP@0.5:0.95 compared to the YOLOv5 baseline.

Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors

The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis.

An Edge Detection Method for Welding Pool Based on An Improved Canny Algorithm

Abstract In this study, an improved Canny algorithm is proposed for detecting welding pool edges in complex environments, considering the issues of inaccurate extraction, discontinuity, and susceptibility to noise in traditional edge detection algorithms. Firstly, the original Gaussian filter is replaced with a hybrid filter that combines dark channel prior defogging and bilateral filtering used for image denoising. Then, a four-direction Sobel operator is utilized to calculate gradient amplitude and direction. Furthermore, the double threshold required for extracting and connecting pool edges is obtained by using an enhanced quadratic Otsu algorithm. Experimental results demonstrate that, compared with the traditional algorithm, the improved Canny algorithm achieves an approximately 51.6% increase in PSNR and about 0.04 improvement in SSIM, enabling more accurate and comprehensive extraction of welding pool edge information.

Design and testing of a multi-electrode apodized acousto-optic filter for arbitrary polarized light.

We present an original acousto-optic tunable filter that is able to filter visible light from 400 to 650 nm and is designed to interact simultaneously with two polarizations. The filter shows an adjustable optical bandwidth and apodization capabilities. These features make it suitable for practical spectroscopic applications. Experimental validation is also presented.

On-chip bioluminescence biosensor for the detection of microbial surface contamination

Detection of microbial pathogens is important for food safety reasons, and for monitoring sanitation in laboratory environments and health care settings. Traditional detection methods such as culture-based and nucleic acid-based methods are time-consuming, laborious, and require expensive laboratory equipment. Recently, ATP-based bioluminescence methods were developed to assess surface contamination, with commercial products available. In this study, we introduce a biosensor based on a CMOS image sensor for ATP-mediated chemiluminescence detection. The original lens and IR filter were removed from the CMOS sensor revealing a 12 MP periodic microlens/pixel array on an area of 6.5 mm × 3.6 mm. UltraSnap swabs are used to collect samples from solid surfaces including personal electronic devices, and office and laboratory equipment. Samples mixed with chemiluminescence reagents were placed directly on the surface of the image sensor. Close proximity of the sample to the photodiode array leads to high photon collection efficiency. The population of microorganisms can be assessed and quantified by analyzing the intensity of measured chemiluminescence. We report a linear range and limit of detection for measuring ATP in UltraSnap buffer of 10–1000 nM and 225 fmol, respectively. The performance of the CMOS-based device was compared to a commercial luminometer, and a high correlation with a Pearson's correlation coefficient of 0.98589 was obtained. The Bland-Altman plot showed no significant bias between the results of the two methods. Finally, microbial contamination of different surfaces was analyzed with both methods, and the CMOS biosensor exhibited the same trend as the commercial luminometer.

Некоторые вопросы, связанные с реализацией множеств притяжения с точностью до наперед заданной окрестности

Questions connected with implementation of attraction sets (AS) in attainability problem with constraints of asymptotic nature (CAN) are considered. It is investigated the possibility of AS implementation accurate to arbitrary neighborhood in class of closures of attainability sets corresponding to concrete sets from the family generating CAN. Moreover, some relations for AS generated by different CAN are considered (disjunction conditions of AS are investigated). General constructions of neighborhood implementation of AS were applied in the case when these AS were considered in the space of ultrafilters of broadly understood measurable space (MS). In particular, the case when CAN are defined by a filter was investigated in detail; for this case, under non-restrictive conditions on the original MS, the set of ultrafilters majorizing the original filter is implemented as AS. In this case (of ultrafilter space) variants of equipment of ultrafilter set with topologies of Stone and Wallman types are investigated separately.

On the practical implementation of the enhanced horizontal derivative filter for potential field data

The enhanced horizontal derivative (EHD) is an enhancement filter whose maxima provide estimated locations of the sources' boundaries. This filter is defined as the total horizontal derivative (THDR) of a weighted sum of vertical derivatives of increasing order. We consider some aspects of the practical implementation of the EHD filter, especially its robustness. A slightly different version of EHD, which we refer to as mEHD, is obtained when we switch the order of THDR and the weighted sum; that is, when we consider the weighted sum of total horizontal derivatives of the successive vertical derivatives. It turns out that mEHD can be more stable and provide a clearer enhanced map than the original filter, as demonstrated with examples of synthetic data and aeromagnetic data from Southern Brazil. Moreover, we address the choice of the weighting coefficients of the vertical derivatives, emphasizing that the standard choice of unitary weights may not be the most appropriate one. The enhanced horizontal derivative (EHD) is an enhancement filter whose maxima provide estimated locations of the sources' boundaries. This filter is defined as the total horizontal derivative (THDR) of a weighted sum of vertical derivatives of increasing order. We consider some aspects of the practical implementation of the EHD filter, especially its robustness. A slightly different version of EHD, which we refer to as mEHD, is obtained when we switch the order of THDR and the weighted sum; that is, when we consider the weighted sum of total horizontal derivatives of the successive vertical derivatives. It turns out that mEHD can be more stable and provide a clearer enhanced map than the original filter, as demonstrated with examples of synthetic data and aeromagnetic data from Southern Brazil.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

A NEW WEATHER FILTER FOR REDUCING WEATHER EFFECT IN CALCULATING SEA ICE CONCENTRATION FROM AMSR2 DATA

Abstract. Ice concentration is one of the most fundamental parameters of sea ice which can be calculated from brightness temperatures measured by passive microwave radiometers such as AMSR2 onboard satellites. However, the presence of atmospheric water vapor, cloud liquid water, etc. is likely to increase the brightness temperatures over open water. As a result, sometimes, certain ice concentrations are calculated over open water covered with heavy clouds. The authors call this kind of area as “false sea ice” and the phenomenon is called the weather effect. In the AMSR2 ice concentration product, a weather filter is used to reduce the weather effect. However, it is known that false sea ice appears occasionally in some areas even in the summertime in AMSR2 ice concentration products. In this study, the authors have modified the original weather filter and added a new weather filter utilizing the following two equations which are TB(23GHzV)<−0.75×(TB(36GHzV)−TB(36GHzH))+253K and TB(36GHzV)−TB(36GHzH) < 57K. If the brightness temperature of the pixel meets the above two equations, we set the ice concentration of the pixel as 0%. By adding the new weather filter, most of the false sea ice that appeared in the ASMR2 ice concentration products could be rejected. JAXA has decided to add this weather filter for producing AMSR2 standard sea ice concentration products.

Layer factor analysis in convolutional neural networks for explainability

Explanatory methods that focus on the analysis of the features encoded by Convolutional Neural Networks (CNNs) are of great interest, since they help to understand the underlying process hidden behind the black-box nature of these models. However, to explain the knowledge gathered in a given layer, they must decide which of the numerous filters to study, further assuming that each of them corresponds to a single feature. This, coupled with the redundancy of information, makes it difficult to ensure that the relevant characteristics are being analyzed. The above represents an important challenge and defines the aim and scope of our proposal. In this paper we present a novel method, named Explainable Layer Factor Analysis for CNNs (ELFA-CNNs), which models and describes with quality convolutional layers relying on factor analysis. Regarding contributions, ELFA obtains the essential underlying features, together with their correlation with the original filters, providing an accurate and well-founded summary. Through the factorial parameters we gain insights about the information learned, the connections between channels, and the redundancy of the layer, among others. To provide visual explanations in a similarly way to other methods, two additional proposals are made: a) Essential Feature Attribution Maps (EFAM) and b) intrinsic features inversion. The results prove the effectiveness of the developed general methods. They are evaluated in different CNNs (VGG-16, ResNet-50, and DeepLabv3+) on generic datasets (CIFAR-10, imagenette, and CamVid). We demonstrate that convolutional layers adequately fit a factorial model thanks to the new metrics presented for factor and fitting residuals (D1, D>, and Res, derive from covariance matrices). Moreover, knowledge about the deep image representations and the learning process is acquired, as well as reliable heat maps highlighting regions where essential features are located. This study effectively provides an explainable approach that can be applied to different CNNs and over different datasets.

Adaptive dynamic kalman filter for high-performance tilt-angles estimation

Estimation of attitude is a potential task for autonomous vehicles as it directly affects the velocity and position estimates as well as the overall system autonomy. The estimation of attitude angles utilizing gyro measurements always suffers from increasing drift with time. Although accelerometer measurements can provide an absolute estimate of pitch and roll angles for static and low dynamics conditions, they have two drawbacks the effect of additional external acceleration and the inability to capture the high dynamics motion. In this paper, an adaptive Kalman filter (AKF) is utilized for integrating the gyros and accelerometer measurements for enhancing the roll and pitch angles estimates by compensating for the effect of linear acceleration as well as adaptively adapting the Kalman filter measurement covariance matrix. The proposed algorithm is evaluated using real offline measurements for a flying vehicle. Further, a comparative analysis is carried out with the original Kalman filter (KF) algorithm and with the imufilter from Sensor Fusion and Tracking Toolbox from Matlab. The results presented satisfactory performance and enhancement for roll and pitch angles estimate.

Robust Recursive Least-Squares Wiener Filter for Linear Continuous-Time Uncertain Stochastic Systems

For linear continuous-time systems with uncertainties in the system and observation matrices, an original robust RLS Wiener filter is designed in this study. The robust RLS Wiener filter does not assume norm-bounded uncertainty for the system and observation matrices, in contrast to the robust Kalman filter. In the design of the robust RLS Wiener filter, the degraded signal, affected by the uncertainties in the system and observation matrices, is modeled by an autoregressive (AR) model. The system and observation matrices for the degraded signal are formulated from the relationship between the AR model of the degraded signal and the state-space model. Estimation formulas for the system and observation matrices are proposed in Section 2. The robust filtering problem is introduced based on the minimization of the mean-square value of the filtering errors for the system states. The robust filtering estimate is given as an integral transformation of the degraded observations using the impulse response function. The integral equation that an optimal impulse response function satisfies is given in Section 3. Theorem 1 presents a robust RLS Wiener filtering algorithm starting from this integral equation. The proposed robust RLS Wiener filter outperforms the existing robust Kalman filter regarding estimate accuracy, as shown by a numerical simulation example.

Performances of a family of new sequential Bayesian filters for input estimation

During their lifetime, structures are usually subjected to some mechanical shocks that generate high levels of vibration that can damage the structure itself as well as the embedded devices. However, the characteristics of these shocks (location, time history, maximum intensity, etc.) are often unknown due to the inaccessibility of the excitation region for direct force measurements or the inability to instrument the system. Therefore, inverse methods have been developed to quantify these complex excitations. Recently, a Bayesian formulation of the input-state estimation problem for linear systems has been proposed by the authors, which unifies most of the state-of-the-art filters. In this paper, we present two novel Bayesian filters derived from this framework: (a) the Correlated Dual Kalman Filter (CDKF), which is one of the filters that naturally follows from the unified Bayesian formulation, and (b) the Component Bayesian Filter (CBF), which promotes spatial sparsity of the input vector. Essentially, these filters differ in the prior distributions used to convey information about the spatial distribution of the input vector to be identified. The performance of these filters is evaluated through a numerical experiment and a real-world application aimed at reconstructing a sparse and transient excitation acting on a linear structure. A comparison of these original filters with other Bayesian filters proposed in the literature is also proposed. In particular, the numerical experiment allows to study the performance of the proposed filters in different scenarios, such as the number of sensors, the measurement noise level or the sensor configuration, while the real-world application allows to test them in operational conditions. More specifically, it is shown that filters promoting the spatial sparsity of the input vector, such as CBF, lead to a consistent identified excitation profile when acceleration measurements are the only available data.

HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.

Active power decoupling scheme of symmetrical LCL structure in single‐phase grid‐connected voltage source inverter for ultra‐high voltage transmission

SummaryThe voltage source converter (VSC) is an important component of ultra‐high voltage (UHV) technology, where the former is responsible for converting new energy sources into electricity and the latter enables the transmission of electricity over long distances. However, the VSC has a serious problem of oscillation power under three‐phase unbalance, which comes from the single‐phase voltage source inverter (VSI) that is the basic structure of VSC. So there is always the requirement for active power decoupling (APD). Conventional passive solutions increase the overall size significantly, and existing active solutions either require the addition of a decoupling topology or rely strongly on their own original topology and more complex control. To overcome this drawback, this paper proposes an improved APD method with a combination of topology‐type and control‐type decoupling features for single‐phase VSI based on the symmetrical LCL filter for solving the power oscillation problem of VSC at the source. The proposed structure can decrease common mode interference and can absorb the oscillation power by the original filter capacitors of the LCL filter. Besides, the original circuit components can be reused, and the decoupling is simple enough to achieve APD without affecting the full bridge control. Next, the control is optimized to reduce the compensation error and input voltage. Finally, the experimental device is carried out to verify the effectiveness of the proposed method.

Warmstarting strategies for convex optimization based multi-channel constrained active noise control filter design

In real-world applications of active noise control (ANC) systems, various constraints are supposed to be satisfied in the controller design process. The optimal control filter coefficients can be obtained by solving a constrained optimization problem, which usually requires a significant computational effort. Recently, a convex formulation in conic form was proposed for ANC filter design. The proposed formulation was shown to result in a computational time reduction by several orders of magnitude. It is of great interest to further improve its efficiency by using a priori information of the optimal filter coefficients. One potential way of achieving this goal is to introduce a warmstart technique so that the filter solution of a similar system or environment can be referred to for selecting the starting point of the optimization algorithm. However, the conic formulation should be solved by the interior-point method, which, in general, is challenging for applying warmstart techniques. In the current work, relaxation methods are proposed to the constraints of original ANC filter design formulation so that the warmstart techniques can be applied. Then, a warmstarting technique proposed in previous study is used to solve a series of perturbed problems, and the performance of the warmstarting technique is investigated. Results show that with the proposed modifications applied, the warmstarting strategy can significantly reduce the number of iterations needed for solving the conic formulation of the ANC filter design problem without much tuning efforts, and the method is effective and robust in various environmental setups.

A Second-Order Method for Removing Mixed Noise from Remote Sensing Images.

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.

Streamlined determination of 3-O-methyldopa in dried blood spots: Prospective screening for aromatic l-amino-acid decarboxylase deficiency

BackgroundAromatic L-amino-acid decarboxylase (AADC) deficiency diagnosis is often delayed by low disease awareness and specific laboratory examinations. We demonstrated that an elevated concentration of L-dopa metabolite 3-O-methyldopa (3-OMD) in dried blood spots could be integrated into a newborn screening program to detect AADC deficiency. MethodsDBS samples for amino acid and acylcarnitine analysis using NeoBase™2 reagents were also analyzed for the 3-OMD concentration using 13C6-phenylalanine as an internal standard. For samples exceeding the pre-defined cutoffs, an additional spot was punched from the original filter paper for second-tier 3-OMD measurement by high performance liquid chromatography (HPLC)-MS/MS assay. Newborns with a 3-OMD concentration exceeding 500 ng/mL were referred for confirmatory testing. ResultsFrom Feb. 2020 to Dec. 2022, 157,371 newborns were screened for AADC deficiency. Eight newborns exhibited an elevated 3-OMD concentration (839–5170 ng/mL). Among them, six newborns were confirmed to carry two pathogenic DDC variants, indicating an incidence of AADC deficiency of ∼1:26,000 (95% confidence interval: 1 in 12,021 to 1 in 57,228). During the follow-up period, all six patients developed typical symptoms of AADC deficiency. ConclusionThe screening for 3-OMD, a target for AADC deficiency, could be easily integrated into the existing newborn screening programs and facilitate the future application for early diagnosis and effective treatment.