Fast Method Research Articles

CResU-Net: a method for landslide mapping using deep learning

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.

Using FT-IR as a fast method to measure fatty acid soaps in in vivo and in vitro digests.

The objective of the current study was to develop a simple method to measure fatty acid soaps, making use of FT-IR, representative for the soap formation observed in clinical trials. Calcium soaps have a unique coordination which leads to a typical double-splitting of the antisymmetric and symmetric carboxylate peaks. Absorbance values of these carboxylate peaks were used together with the absorbance of the hydrocarbon -CH2 antisymmetric and symmetric peaks to calculate the calcium soap absorbance. Based on the linear correlation between the calcium soap absorbance and the calcium soap concentration measured with GC-FID, a model was set-up and subsequently successfully validated to quantify calcium soap concentrations in faecal samples from clinical trials with this FT-IR method. With in vivo as well as in vitro digestion an inverse correlation between the long chain saturated fatty acid part of milk fat containing fat blends used for the infant formulas, and the formation of fatty acid soaps after digestion and defaecation could be observed. There is a clear link between the amount of long chain saturated fatty acids at the sn-1/3 position and their release as free fatty acid after lipolysis with the appearance of fatty acid soaps. These insights enable future development of fat blends for infant nutrition to optimize fatty acid soap formation and thereby gut discomfort in infants. These insights can be used to predict the soap formation capacity of a newly designed fat blend and thereby the improvement of infant nutrition products.

A full-field solution to predict vibrations in the presence of a free surface

Blasting is a common activity in mining that can cause significant damage in the remaining rock mass. The most commonly used method for predicting vibrations is the Swedish or Holmberg-Persson approach. It is a simple and fast method that uses empirical formulation to estimate the vibration levels based on the explosive charge weight, distance to the source, and other parameters. However, it has limited accuracy and does not consider the effect of free surfaces or other boundary conditions. On the other hand, recently developed methods as the full-field solution (FFS) can provide more accurate predictions of vibrations due to excitations provoked by cylindrical columns of explosive, but the inclusion of free surfaces in analytical methods is a challenging problem. This paper presents a new approach that includes free surfaces in the FFS to predict vibrations analytically in the near-field. The main obstacle to including free surfaces in the FFS is the belief that the solution is non-decoupleable in P- and SV-waves separately. The Heelan's approach was previously used to decouple them, but it only provides valid radiation patterns in the far-field and does not account for intrinsic attenuation. In this paper, we address this problem, allowing for the inclusion of free surfaces in the FFS.

Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii

Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts’ metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

Analytical quality by design (AQbD) based optimization of RP-UPLC method for determination of nivolumab and relatlimab in bulk and pharmaceutical dosage forms

BackgroundThe Analytical Quality by Design (AQbD) methodology extends the application of Quality by Design (QbD) principles to the management of the analytical procedure life cycle, encompassing method creation, optimization, validation, and continuous improvement. AQbD assists in creating analytical procedures that are robust, reliable, precise, and cost-efficient. Opdualag™ is a combination of Nivolumab and Relatlimab, which are antibodies that block programmed death receptor-1 (PD-1) and lymphocyte-activation gene 3 (LAG-3) receptors, used to treat advanced melanoma. This work aims to develop and validate a reversed-phase ultra-performance liquid chromatography (RP-UPLC) method using AQbD principles to determine NLB and RTB in pharmaceutical products.ResultsA central composite design (CCD) comprising three factors arranged in five distinct levels was implemented via Design-expert® software to optimize the chromatographic conditions. A mathematical model was constructed and the effects of three independent factors namely flow rate (X1), percentage of methanol in the mobile phase (X2), and temperature (X3) on responses including retention time (Y1–2), resolution factor (Y3), theoretical plates (Y4–5), and tailing factor (Y6–7) were investigated. The software determined the optimal chromatographic conditions for the separation of NLB and RTB, which were as follows: 32.80% methanol in the mobile phase, 0.272 mL/min flow rate, 29.42 °C column temperature, and 260 nm UV detection. The retention time for NLB and RTB were 1.46 and 1.88 min, respectively. The method exhibited linearity across the concentration ranges of 4–24 µg/mL for RTB and 12–72 µg/mL for NLB. The limits of detection (LOD) and limit of quantification (LOQ) for NLB and RTB, respectively, were 0.89 µg/mL, 2.69 µg/mL and 0.15 µg/mL and 0.46 µg/mL. The percentage relative standard deviation (%RSD) of intraday and interday precision for NLB and RTB was below 2. The recovery percentages for NLB and RTB were determined to be 99.57–100.43% and 99.59–100.61%, respectively. Both drugs were found to be susceptible to oxidative and photolytic degradation in forced degradation studies.ConclusionsEmploying the AQbD-based methodology, a straightforward, fast, accurate, precise, specific, and stability-indicating RP-UPLC method has been established for the quantitative analysis of NLB and its RTB in pharmaceutical formulations.

Mechanism-driven improved SVMD: an indirect approach for rail corrugation detection using axle box acceleration

Abstract Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

Leukemia Cancer Cells Segmentation and Classification using Machine Learning

Determining the aim of the project is to detect the leukemia at earlier stage with the help of image processing techniques. Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Acute Lymphoblastic Leukemia (ALL) is a type of leukemia which is more common in children. The term Acute‟ means that leukemia can progress quickly and if not treated may lead to fatal death within few months. Due to its non specific nature of the symptoms and signs of ALL leads wrong diagnosis. Even hematologist finds it difficult to classify the leukemia cells, there manual classification of blood cells is not only time consuming but also inaccurate. Therefore, early identification of leukemia yields in providing the appropriate treatment to the patient. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. We have focused on the changes in the geometry of cells like area, perimeter and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB. After recognizing its statistical properties, types of leukemia will be identified based on the irregularities in the shape.

Employing Constrained Nonnegative Matrix Factorization for Microstructure Segmentation.

Materials characterization using electron backscatter diffraction (EBSD) requires indexing the orientation of the measured region from Kikuchi patterns. The quality of Kikuchi patterns can degrade due to pattern overlaps arising from two or more orientations, in the presence of defects or grain boundaries. In this work, we employ constrained nonnegative matrix factorization to segment a microstructure with small grain misorientations, (<1∘), and predict the amount of pattern overlap. First, we implement the method on mixed simulated patterns-that replicates a pattern overlap scenario, and demonstrate the resolution limit of pattern mixing or factorization resolution using a weight metric. Subsequently, we segment a single-crystal dendritic microstructure and compare the results with high-resolution EBSD. By utilizing weight metrics across a low-angle grain boundary, we demonstrate how very small misorientations/low-angle grain boundaries can be resolved at a pixel level. Our approach constitutes a versatile and robust tool, complementing other fast indexing methods for microstructure characterization.

Development of a hepatic cryoinjury model to study liver regeneration.

The liver is a remarkable organ that can regenerate in response to injury. Depending on the extent of injury, the liver can undergo compensatory hyperplasia or fibrosis. Despite decades of research, the molecular mechanisms underlying these processes are poorly understood. Here, we developed a new model to study liver regeneration based on cryoinjury. To visualise liver regeneration at cellular resolution, we adapted the CUBIC tissue-clearing approach. Hepatic cryoinjury induced a localised necrotic and apoptotic lesion characterised by inflammation and infiltration of innate immune cells. Following this initial phase, we observed fibrosis, which resolved as regeneration re-established homeostasis in 30 days. Importantly, this approach enables the comparison of healthy and injured parenchyma with an individual animal, providing unique advantages to previous models. In summary, the hepatic cryoinjury model provides a fast and reproducible method for studying the cellular and molecular pathways underpinning fibrosis and liver regeneration.

Rapid determination of toxic and essential metal binding proteins in biological samples by size exclusion chromatography-inductively coupled plasma tandem mass spectrometry

Metalloproteins binding with trace elements play a crucial role in biological processes and on the contrary, those binding with exogenous heavy metals have adverse effects. However, the methods for rapid, high sensitivity and simultaneous analysis of these metalloproteins are still lacking. In this study, a fast method for simultaneously determination of both essential and toxic metal-containing proteins was developed by coupling size exclusion chromatography (SEC) with inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). After optimization of the separation and detection conditions, seven metalloproteins with different molecular weight (from 16.0 to 443.0 kDa) were successfully separated within 10 min and the proteins containing iron (Fe), copper (Cu), zinc (Zn), iodine (I) and lead (Pb) elements could be simultaneously detected with the use of oxygen as the collision gas in ICP-MS/MS. Accordingly, the linear relationship between log molecular weight and retention time was established to estimate the molecular weight of unknown proteins. Thus, the trace metal and toxic metal containing proteins could be detected in a single run with high sensitivity (detection limits in the range of 0.0020–2.5 μg/mL) and good repeatability (relative standard deviations lower than 4.5 %). This method was then successfully used to analyze metal (e.g., Pb, Zn, Cu and Fe) binding proteins in the blood of Pb-intoxicated patients, and the results showed a negative correlation between the contents of zinc and lead binding proteins, which was identified to contain hemoglobin subunit. In summary, this work provided a rapid and sensitive tool for screening metal containing proteins in large number of biological samples.

Optical discrimination of pathological red blood cells.

Fast diagnostic methods are crucial to reduce the burden on healthcare systems. Currently, detection of diabetes complications such as neuropathy requires time-consuming approaches to observe the correlated red blood cells (RBCs) morphological changes. To tackle this issue, an optical analysis of RBCs in air was conducted in the 250-2500 nm range. The distinct oscillations present in the scattered and direct transmittance spectra have been analyzed with both Mie theory and anomalous diffraction approximation. The results provide information about the swelling at the ends of RBCs and directly relate the optical data to RBCs morphology and deformability. Both models agree on a reduction in the size and deformability of RBCs in diabetic patients, thus opening the way to diabetes diagnosis and disease progression assessment.

HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} scores (average F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

A fast stereo-matching method for a speckle pattern based on grid and gradient constraints

In the existing binocular stereo-matching methods for a speckle pattern, the pixel-by-pixel correlation weight calculation across the entire global disparity range is time-consuming. To address this issue, this paper presents a fast stereo-matching method for a speckle pattern based on grid and gradient constraints. The grid pattern is projected onto the measured object. The grid features are extracted to form the local disparity constraints, which narrow the search ranges. Moreover, the image gradient is used in selecting the pixels involved in the correlation weight calculation, which reduces the redundant computations. 3D reconstruction experiments of paper and plaster models were employed to evaluate the matching accuracy and speed of the proposed method. Furthermore, the experiments of measuring displacement were designed to validate the accuracy of our method. In contrast to the existing methods, our approach increased the matching speed by 66% on average while ensuring the matching accuracy.

A fast stereo-matching method for a speckle pattern based on grid and gradient constraints

In the existing binocular stereo-matching methods for a speckle pattern, the pixel-by-pixel correlation weight calculation across the entire global disparity range is time-consuming. To address this issue, this paper presents a fast stereo-matching method for a speckle pattern based on grid and gradient constraints. The grid pattern is projected onto the measured object. The grid features are extracted to form the local disparity constraints, which narrow the search ranges. Moreover, the image gradient is used in selecting the pixels involved in the correlation weight calculation, which reduces the redundant computations. 3D reconstruction experiments of paper and plaster models were employed to evaluate the matching accuracy and speed of the proposed method. Furthermore, the experiments of measuring displacement were designed to validate the accuracy of our method. In contrast to the existing methods, our approach increased the matching speed by 66% on average while ensuring the matching accuracy.

Qualitative and quantitative protease activity tests based on protein degradation in three-dimensional structures

The pattern of the activity of proteases is related to distinct physiological states of living organisms. Often activity changes of a certain protease can be assigned to a specific disease. Hence, they are useful biomarkers and a simple and fast determination method of their activity could be a valuable tool for the efficient monitoring of numerous diseases. Here, two different methods for the qualitative and quantitative determination of protease activity are demonstrated using the model system of proteinase K. The first test system is based on a protein-modified and colored 3D silica structure that changes color when exposed to the enzyme. This method has also been used for the detection of matrix metallo-protease 2 (MMP2) with gelatine as protease substrate on the plates. The second detection system uses the decrease in the voltammetric signal of a cytochrome c/DNA multilayer electrode after incubation with a protease to quantitatively determine its proteolytic activity. While activities down to 0.15 U/ml can be detected with the first method, the second one provides detection limits of about 0.03U/ml (for proteinase K.) The functionality of both systems can be demonstrated and ways for further enhancement of sensitivity have been elucidated.

Computer vision–based surface defect identification method for weld images

Welding is the main connection type used in steel structure bridges. As the weld quality significantly affects the performance of a structure, weld defect detection is crucial in the performance evaluation of bridges. With the continuous development of computer vision (CV) techniques and image acquisition equipment, CV-based methods are being increasingly applied to weld defect detection. However, accurate identification of welding defects from several images is challenging. This paper proposes a CV-based method for detecting and quantifying surface porosity defects in welds. CV techniques were used to identify and extract weld defects, and the pixel-level sizes of the weld defects were obtained. The actual sizes of the weld defects were calculated based on a conversion between the image size and actual size. The results show that the proposed image processing method has low complexity and high accuracy in detecting weld defects, providing accurate measurement of the defect size. It is a fast and non-contact detection method for weld defect detection.

Lateral flow paired with RT-LAMP: A speedy solution for Influenza A virus detection in swine

Influenza A Virus in swine (IAV-S) is a zoonotic pathogen that is nearly ubiquitous in commercial swine in the USA. Swine possess sialic acid receptors that allow co-infection of human and avian viruses with the potential of pandemic reassortment. We aimed to develop a fast and robust testing method for IAV-S detection on swine farms. Two primers of the RT-LAMP assay were labeled for use in a lateral flow readout. A commercially available lateral flow kit was used to read the amplicon product. With a runtime of ∼ 45 minutes, the limit of detection for the assay is comparable with an RT-qPCR Cq less than 35, with a sensitivity of 83.5 % and a specificity of 89.6 %. This assay allows veterinarians and producers with limited access to diagnostic services to perform and detect Matrix gene amplification on-site with low equipment costs. The time from sample collection to detection is less than one hour, making this method an accessible, convenient, and affordable tool to prevent the spread of zoonotic disease.

Mo-doped Co LDHs as Raman enhanced substrate for detection of roxarsine

BackgroundRoxarsone (ROX) is widely used as a feed additive, which is indigestible after ingestion by poultry, and most of it can only be excreted into the natural environment and degraded into highly toxic and carcinogenic inorganic arsenic compounds, which pose a hazard to the ecosystem and human health. However, for roxarsone, traditional detection methods require complex and time-consuming procedures, so it is urgent to find a new fast detection method for detection of ROX. ResultsIn this work, we developed a novel Raman enhancement material and utilized the Surface-enhanced Raman scattering (SERS) technique to achieve rapid and sensitive detection of roxarsone. Specifically, Mo-doped cobalt layered double hydroxides (Co-LDHs) semiconductor material (abbreviated as CMM-100) was prepared by a simple method of using ion-assisted MOF etching. Under laser excitation at a wavelength of 532 nm, the CMM-100 showed excellent SERS property to various organic dye molecules such as methylene blue (MB), Toluidine Blue(TB), and Crystal Violet (CV). Especially, an enhancement factor (EF) of 1.4 × 106 was achieved for MB. Compared with the traditional method, this work utilized the fast and non-destructive SERS technology, which achieved a rapid detection of ROX. The detection limit was as low as 9.73 × 10−10 M, and the detection range was from 10−9 M to 10−3 M. SignificanceIn this work, SERS technology was adopted for the rapid and sensitive detection of ROX. This study provides a Raman-enhanced substrate named CMMs for detection of food additives, pesticides, biomolecules, etc., which also broadens the application areas of SERS materials.

A method for recovering adversarial samples with both adversarial attack forensics and recognition accuracy

Adversarial samples deceive machine learning models through small but elaborate modifications that lead to erroneous outputs. The severity of the adversarial sample problem has come to the forefront with the widespread use of machine learning in areas such as security systems, autonomous driving, speech recognition, finance, and medical diagnostics. Malicious attackers can use adversarial samples to circumvent security detection systems, interfere with autonomous driving perception, mislead speech recognition, defraud financial systems, and even cause medical diagnosis errors. The emergence of adversarial samples exposes the vulnerability of existing models and poses challenges for information tracing and forensics after the incident. The main goal of current adversarial sample restoration methods is to improve model robustness. Traditional approaches focus only on improving the model’s classification accuracy, ignoring the importance of adversarial information, which is crucial for understanding the attack mechanism and strengthening future defenses. To address this issue, we propose an adversarial sample restoration method based on the similarity between clean and adversarial sample blocks to balance the needs of adversarial forensics and recognition accuracy. We implement the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Momentum Iterative Attack (MIA) attacks on MNIST, F-MNIST, and EMNIST datasets and perform experimental validation. The results demonstrate that our restoration method significantly enhances the model’s classification accuracy across various datasets and attack scenarios. Comparative analysis shows that the restored samples maintain a high similarity with the original adversarial samples, proving the method’s effectiveness. In addition, we performed performance tests on pre- and post-recovery samples. Taking the MNIST dataset as an example, we observed that the model performance metrics, such as MAPE, MAE, RMSE, and VAPE, of the restored samples improved by 88%, 88%, 65%, and 82%, respectively, after using the FGSM attack. This indicates that our restoration method successfully preserves the information of the generation mechanism of the adversarial samples and improves the model’s performance. This approach balances forensic capability and prediction accuracy, demonstrates a new direction in adversarial sample research, and substantially impacts security defense in practical applications.

Lateral flow paired with RT-LAMP: A speedy solution for Influenza A virus detection in swine

Influenza A Virus in swine (IAV-S) is a zoonotic pathogen that is nearly ubiquitous in commercial swine in the USA. Swine possess sialic acid receptors that allow co-infection of human and avian viruses with the potential of pandemic reassortment. We aimed to develop a fast and robust testing method for IAV-S detection on swine farms. Two primers of the RT-LAMP assay were labeled for use in a lateral flow readout. A commercially available lateral flow kit was used to read the amplicon product. With a runtime of ∼ 45 minutes, the limit of detection for the assay is comparable with an RT-qPCR Cq less than 35, with a sensitivity of 83.5 % and a specificity of 89.6 %. This assay allows veterinarians and producers with limited access to diagnostic services to perform and detect Matrix gene amplification on-site with low equipment costs. The time from sample collection to detection is less than one hour, making this method an accessible, convenient, and affordable tool to prevent the spread of zoonotic disease.