Detection Method Research Articles

Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines

AbstractAcross various global regions abundant in oil and natural gas reserves, the presence of substantial sub‐surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U‐net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state‐of‐the‐art techniques, our model demonstrates superior performance, achieving a stable and validated intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.

EEG-based epileptic seizure detection using deep learning techniques: A survey

Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and spatial shifts in cerebral electrical activity, holding pivotal diagnostic and therapeutic value for epilepsy. Deep learning techniques have made remarkable progress in EEG-based seizure detection over recent years. This review is dedicated to exploring seizure detection approaches based on deep learning, focusing on three distinct avenues. Primarily, we delve into the application of canonical deep learning methods in epilepsy detection. Subsequently, a more in-depth study was conducted on the hybrid models of deep learning. Next, the third is the integration of deep learning and traditional machine learning strategies. Finally, the challenges and future prospects related to this topic are put forward. The uniqueness of this review lies in its novel and comprehensive perspective on the latest research on deep learning-based epilepsy detection by systematically classifying methods, visualizing research progress, and addressing challenges and gaps in current research. It can provide valuable guidance for researchers who want to delve into the field of epileptic seizure detection based on EEG signals.

Identification of novel genetic factors that regulate c-di-AMP production in Staphylococcus aureus using a riboswitch-based biosensor.

Nucleotide secondary messengers regulate various processes in bacteria allowing them to rapidly respond to changes in environmental conditions. c-di-AMP is an essential second messenger required for the growth of the human pathogen Staphylococcus aureus, regulating potassium, osmolyte uptake, and beta-lactam resistance. Cellular concentrations of c-di-AMP are regulated by the activities of two enzymes, DacA and GdpP, which synthesize and hydrolyze c-di-AMP, respectively. Besides these, only a limited number of other factors are known to regulate c-di-AMP levels. Using a c-di-AMP biosensor consisting of the Bacillus subtilis c-di-AMP-binding kimA riboswitch and yfp, we were able to efficiently detect differences in cellular c-di-AMP levels in S. aureus. To identify novel factors that regulate c-di-AMP levels, we introduced the biosensor into a library of S. aureus transposon mutants. In this manner, we obtained mutants with increased c-di-AMP levels that contained insertions in gdpP coding for the c-di-AMP hydrolase and ybbR (cdaR) coding for a c-di-AMP cyclase regulator, thus validating our screen. We also identified two high c-di-AMP mutants with insertions upstream of the nrdIEF operon coding for the ribonucleotide reductase enzyme. Further analysis revealed that the insertion down-regulated nrdIEF expression, indicating that the enzyme is a negative regulator of c-di-AMP production. This negative regulation was dependent on rsh, encoding for the synthase of the endogenous GdpP inhibitor (p)ppGpp. The methods established in this work can be readily adapted for use in other bacteria to uncover genetic or environmental factors regulating c-di-AMP levels.IMPORTANCEc-di-AMP is an important secondary messenger, produced by many bacterial species including the opportunistic pathogen Staphylococcus aureus. In this bacterium, c-di-AMP controls cell wall homeostasis, cell size, and osmotic balance. In addition, it has been shown that strains with high c-di-AMP levels exhibit increased resistance to beta-lactam antibiotics. Here, we developed a biosensor-based method for the rapid detection of c-di-AMP levels in S. aureus. We utilized the biosensor in a genetic screen for the identification of novel factors that impact cellular c-di-AMP. In this manner, we identified the ribonucleotide reductase as a novel factor altering cellular c-di-AMP levels and showed that reducing its expression leads to increased cellular c-di-AMP levels. As methicillin-resistant S. aureus strains are considered as a global health threat, it is important to study processes that dictate cellular c-di-AMP levels, which are associated with antibiotic resistance.

Nighttime large-field video image change detection based on adaptive superpixel reconstruction and multi-scale singular value decomposition fusion

With the development of technology and the needs of social governance, surveillance equipment has been widely used. It is very mature to detect the change of surveillance video images in conventional scenes through video image change detection algorithms. However, in the large field of view environment at night, there are complex random noise and low signal-to-noise ratio in surveillance video images, which makes it difficult for people to find small moving targets. To this end, we propose a new method for nighttime large-field surveillance video image change detection based on adaptive superpixel reconstruction and multi-scale singular value decomposition fusion. The proposed method consists of two parts. On the one hand, an adaptive superpixel reconstruction method is used to reconstruct the two denoised difference images by selecting different segmentation parameters, and the edge information of the two reconstructed difference images is significantly enhanced. On the other hand, a multi-scale singular value decomposition fusion method is used to fuse the two difference images. The multi-scale singular value decomposition fusion obtains a robust difference image by selecting fusion rules at different scales and using the complementary information of different difference images, and the Fuzzy c-means (FCM) clustering algorithm is used to obtain the final changed image. Experimental results on a self-built nighttime large-field video image dataset with two resolutions show that the proposed method is superior to other algorithms in terms of detection accuracy and robustness.

Retraction Note: Human motion image detection and tracking method based on gaussian mixture model combined with light sensor

Retraction Note: Human motion image detection and tracking method based on gaussian mixture model combined with light sensor

PCR enables rapid detection of dermatophytes in practice.

Dermatophytes cause superficial infections of skin, hair, and nails. Even though they rarely cause severe infections, they are relatively common, particularly in primary health care. Diagnosis of dermatophyte infections has relied on relatively slow culture-based methods. Nucleic acid-based detection (PCR) methods might provide results more rapidly. Here, we describe the transition from culture-based methods into PCR-based methods in Northern Finland with a catchment area of approximately 720,000 mostly Caucasian people. This study included 14,330 samples collected between 2019 and 2022. Our results showed that the PCR-based method has become the diagnostic test of choice for these infections in this area. Commercial real-time PCR assay DermaGenius 2.0 complete multiplex test detected more positive results than culture and covered the most important dermatophytes, Candida albicans, and few less common species. With PCR, the mean turn-around-time from sample request to results decreased from 19 days to 16 hours.IMPORTANCESuperficial fungal infections, dermatophytosis, are remarkably common worldwide, affecting an estimated 20%-25% of the global population. In the diagnosis of these infections, fast and accurate results by PCR shorten the time to diagnosis and help clinicians to avoid unnecessary antifungal treatments.

Toward establishing a rapid constant temperature detection method for canine parvovirus based on endonuclease activities.

The NMED method has been established in the laboratory and used for CPV detection. The method has several advantages, including simple sampling, high sensitivity, intuitive results, and no requirement for expensive equipment. The establishment of this method has commercial potential and offers a novel approach and concept for the future development of clinical detection of pathogenic microorganisms.

Application of Electrospun Nanofiber-Based Electrochemical Sensors in Food Safety

Food safety significantly impacts public health and social welfare. Recently, issues such as heavy metal ions, drug residues, food additives, and microbial contamination in food have become increasingly prominent. Electrochemical sensing technology, known for its low cost, simplicity, rapid response, high sensitivity, and excellent selectivity, has been crucial in food safety detection. Electrospun nanofibers, with their high specific surface area, superior mechanical properties, and design flexibility, offer new insights and technical platforms for developing electrochemical sensors. This study introduces the fundamental principles, classifications, and detection mechanisms of electrochemical sensors, along with the principles and classifications of electrospinning technology. The applications of electrospun nanofiber-based electrochemical sensors in food safety detection over the past five years are detailed, and the limitations and future research prospects are discussed. Continuous innovation and optimization are expected to make electrospun nanofiber-based electrochemical sensors a key technology in rapid food safety detection, providing valuable references for expanding their application and advancing food safety detection methods.

Development of a double-antibody sandwich ELISA for detection of SARS-CoV-2 variants based on nucleocapsid protein-specific antibodies.

The COVID-19 pandemic, driven by the SARS-CoV-2 virus, has posed a severe threat to global public health. Rapid, reliable, and easy-to-use detection methods for SARS-CoV-2 variants are critical for effective epidemic prevention and control. The N protein of SARS-CoV-2 serves as an ideal target for antigen detection. In this study, we achieved soluble expression of the recombinant SARS-CoV-2 N protein using an Escherichia coli expression system and generated specific monoclonal antibodies by immunizing BALB/c mice. We successfully developed 10 monoclonal antibodies against the N protein, designated 5B7, 5F2-C11, 5E2-E8, 6C3-D8, 7C8, 9F2-E9, 12H5-D11, 13G2-C10, 14E9-F6, and 15H3-E10. Using these antibodies, we established a sandwich ELISA with 6C3-D8 as the capture antibody and 5F2-C11 as the detection antibody. The assay demonstrated a sensitivity of 0.78 ng/mL and showed no cross-reactivity with MERS-CoV, HCoV-OC43, HCoV-NL63, and HCoV-229E. Furthermore, this method successfully detected both wild-type SARS-CoV-2 and its variants, including Alpha, Beta, Delta, and Omicron. These findings indicate that our sandwich ELISA exhibits excellent sensitivity, specificity, and broad-spectrum applicability, providing a robust tool for detecting SARS-CoV-2 variants.

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

Design of Eddy Current and Capacitance Dual-mode Sensor for Thickness Detection of Thermal Barrier Coatings

Abstract Thermal barrier coatings (TBCs) can markedly enhance the service temperature of high-temperature alloys, thereby enhancing the engine thrust-to-weight ratio. However, thermal barrier coatings must be capable of withstanding a multitude of harsh environments, including high temperatures, high pressures, and high-speed particle impacts, which can potentially lead to damage and failure. Consequently, nondestructive testing of thermal barrier coatings is of particular importance for the assessment of structural health. The heterogeneous composition of thermal barrier coating materials, comprising both conductive and non-conductive materials, presents a challenge for achieving comprehensive detection through a single detection method. As eddy current detection is applicable to conductive materials and capacitive detection is applicable to non-conductive materials, this paper proposes a novel eddy current/capacitance detection dual-mode sensor, which combines the advantages of the two detection modes. This study examines the structural configuration and excitation characteristics of the eddy current and capacitance dual-mode sensor. Furthermore, the interaction between the capacitive electrode and the eddy current coil is examined, and the viability of the dual-mode sensor is demonstrated through experimentation. The experimental results demonstrate that the dual-mode sensor exhibits a sensitivity of 2.67 mΩ/mm for bond coating thickness detection in eddy current mode with an excitation frequency of 5 MHz, and a maximum sensitivity of 80.31 pF/mm for top coating thickness detection in capacitive mode.

Development and evaluation of a loop-mediated isothermal amplification (LAMP) method for Candida glabrata detection.

Loop-mediated isothermal amplification (LAMP) is a simple and rapid nucleic acid method for DNA amplification at a constant temperature. The "gold standard" culture method for yeast detection, has low sensitivity with severe consequences, increasing morbidity and mortality rates. Here, we report the development of a LAMP method for the specific detection of C. glabrata. The specific LAMP primers for C. glabrata detection were designed and evaluated. The LAMP assay accurately detected C. glabrata with no cross-reactivity with other Candida species. The developed molecular method would be a promising tool in the management of invasive candidiasis.

Beekeeping breakthrough: unveiling hive health with a portable membrane inlet mass spectrometry detection method.

Supporting bee populations is essential considering threats posed by human activities like pesticide usage and habitat destruction. However, the current methods for monitoring and analyzing beehives and their surrounding environments are invasive, complex, and time-consuming. These methods often rely heavily on laboratory analyses, making them difficult to implement independently in the field. This study explores the application of portable membrane inlet mass spectrometer (MIMS) for noninvasive hive analysis, demonstrating its ability to detect various compounds indicative of hive conditions and environmental stressors. In addition to the expected compounds found in beehives, such as α-bergamotene, hexadecanoic acid, heptadecane, hexadecanamide, α-bisabolol-, 9-octadecenamide, (Z) - , and benzaldehyde, unexpected compounds, pollutants, like indane (polycyclic aromatic hydrocarbon) and carbofuran (pesticide), were also detected. The MIMS detection method provides rapid, accurate, and real-time results, making it suitable for preventive measures against bee diseases and integral to environmental biomonitoring. This integration of technology represents a significant advancement in bee conservation efforts, offering hope for the future of both bees and ecosystems.

A comparison of conventional and resampled personal reliability in detecting careless responding.

Detecting careless responding in survey data is important to ensure the credibility of study findings. Of several available detection methods, personal reliability (PR) is one of the best-performing indices. Curran, Journal of Experimental Social Psychology, 66, 4-19, (2016) proposed a resampled version of personal reliability (RPR). Compared to the conventional PR or even-odd consistency, in which just one set of scale halves is used, RPR is based on repeated calculation of PR across several randomly rearranged sets of scale halves. RPR should therefore be less affected than PR by random errors that may occur when a specific set of scale half pairings is used for the PR calculation. In theory, RPR should outperform PR, but it remains unclear whether it in fact does, and under what conditions the potential gain in detection accuracy is the most pronounced. We conducted two studies: a simulation study examined the performance of the conventional PR and RPR in detecting simulated careless responding, and a real data example study analyzed their performance when detecting human-generated careless responding. In both studies, RPR turned out to be a significantly better careless response indicator than PR. The results also revealed that using 25 resamples for the RPR computation is sufficient to obtain the expected gain in detection accuracy over the conventional PR. We therefore recommend using RPR instead of the conventional PR when screening questionnaire data for careless responding.

Detection methods and prognosis implications of measurable residual disease in acute myeloid leukemia.

Measurable residual disease (MRD) in acute myeloid leukemia (AML) refers to the quantity of residual leukemic cells in a patient after treatment.According to the latest agreements, MRD in AML offering essential prognostic insights. However, there is ongoing debate regarding MRD-based monitoring and treatment strategies. There are multiple platforms for detecting MRD, each varying in sensitivity and suitability for different patients. MRD not only predicts treatment outcomes but also serves as an indicator of treatment effectiveness and a prognostic biomarker. In AML, most retrospective studies indicate that patients who are MRD-positive or show increasing MRD levels at specific time points during remission have significantly higher risks of relapse and mortality compared to MRD-negative patients. Although achieving MRD-negative status can improve patient prognosis, the possibility of relapse remains. Despite the correlation between MRD and clinical outcomes, MRD assessment methods are not yet standardized, leading to discrepancies in results across different techniques. To provide reliable MRD results, it is essential to optimize and standardize MRD detection methods. Methods for assessing MRD include multiparameter flow cytometry (MFC) and molecular assays, chosen based on disease characteristics. This review focuses on currently available MRD detection methods and discusses how the prognostic value of MRD test results informs personalized treatment strategies for AML patients.

Infrared Thermography as a Diagnostic Tool for the Assessment of Mastitis in Dairy Ruminants

Among the health issues of major concern in dairy ruminants, mastitis stands out as being associated with considerable losses in productivity and compromised animal health and welfare. Currently, the available methods for the early detection of mastitis are either inaccurate, requiring further validation, or expensive and labor intensive. Moreover, most of them cannot be applied at the point of care. Infrared thermography (IRT) is a rapid, non-invasive technology that can be used in situ to measure udder temperature and identify variations and inconsistencies thereof, serving as a benchmarking tool for the assessment of udders’ physiological and/or health status. Despite the numerous applications in livestock farming, IRT is still underexploited due to the lack of standardized operation procedures and significant gaps regarding the optimum settings of the thermal cameras, which are currently exploited on a case-specific basis. Therefore, the objective of this review paper was twofold: first, to provide the state of knowledge on the applications of IRT for the assessment of udder health status in dairy ruminants, and second, to summarize and discuss the major strengths and weaknesses of IRT application at the point of care, as well as future challenges and opportunities of its extensive adoption for the diagnosis of udder health status and control of mastitis at the animal and herd levels.

Ligation-driven hyperbranched DNA assembly for label-free and sequence-specific measurement of RNA N6-methyladenosine in human breast tissues

N6-methyladenosine (m6A) is a critical post-transcriptional regulator, and it is linked to various biological functions (e.g., degradation, export, and translation) and multiple human diseases (e.g., cancers, immune disorders, and neurodegenerative diseases). The current methods for sequence-specific detection of m6A usually suffer from radioactive risk, large sample consumption, washing/separation steps, and false-positive signal. Herein, we develop a ligation-driven hyperbranched DNA assembly-based fluorescent biosensor for label-free and sequence-specific detection of m6A. In this assay, the unmethylated RNA can be identified and cleaved by m6A-sensitive endoribonuclease MazF. The retained intact m6A-RNA is complementary to the padlock probe to form a circular DNA, subsequently initiating ligation-driven hyperbranched rolling circle amplification (HRCA). The obtained HRCA products can be simply quantified with SYBR Gold as a fluorescent indicator. This method can be homogeneously carried out in an isothermal, label-free and antibody-free manner. The limit of detection can reach 0.079 pM. Moreover, this method can differentiate even 0.01 % m6A level in excess coexisting interferences, monitor cellular m6A RNA level with single-cell sensitivity, and differentiate m6A level in breast tumor tissues and normal adjacent tissues, with promising applications in m6A-releated epigenetic research and clinical diagnosis.

Non-invasive, pre-symptomatic detection of potato cyst nematode infection in tomato using chlorophyll fluorescence analysis.

Potato cyst nematodes (PCN) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches ('foci') that only cover a fraction of the acreage. In-field pre-symptomatic localization of this pathogen is valuable as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical as it would take the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, non-destructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, classical plant performance indicators biomass and root surface area were compared with Chl-F. Thermal dissipation ('NPQ_Lss') and actual photosynthetic rate ('QY_Lss') responded at 1 DPI, while QY_Lss was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The efficiency of photosystem II (QY_max) and the potential activity of photosystem II (Fv/Fo) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. While Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other below-ground stressors in the field.

Postmortem distribution of ropivacaine and its metabolite in human body fluids and solid tissues by GC-MS/MS using standard addition method.

An analytical method was developed for determining ropivacaine and its main metabolite, 3-hydroxyropivacaine in biomedical samples using gas chromatography-tandem mass spectrometry (GC-MS/MS). Then, this established method was applied to investigate the distribution of ropivacaine and its metabolite in human fluids and solid tissues obtained from an authentic case ropivacaine involved. The fluid sample was added acetonitrile, and solid tissue was homogenized using a freezer mill and then added into acetonitrile. Then, an internal standard solution was added to the mixtures. The mixture was centrifuged at 12,000 × g for 5min, and the upper layer of acetonitrile was transferred to magnesium sulfate and octadecyl silica (C18) gel for cleaning up the sample. After centrifugation, the upper layer was then evaporated to dryness with nitrogen, and dissolved with methanol, then injected into the GC-MS/MS system. The coefficients of determination (r2) of constructed calibration curves were all greater than 0.999. The limits of detection for ropivacaine and 3-hydroxyropivacaine in target samples were 15ng/mL and 10ng/mL, respectively. The recovery rates of ropivacaine and 3-hydroxyropivacaine ranged from 97.6% to 103% and from 96.5% to 104%, respectively. The inter-day precision values of ropivacaine and 3-hydroxyropivacaine were not greater than 6.25% and 7.98%, respectively, and the inter-day trueness values were not greater than 6.90% and 8.33%, respectively; the intra-day precision and trueness values of ropivacaine and 3-hydroxyropivacaine were not greater than 3.20%, 6.78%, 7.84% and 8.99%, respectively. GC-MS/MS method for simultaneous detection and quantification of ropivacaine and 3-hydroxyropivacaine in biological samples was successfully developed. The method could also be applied to samples obtained from an authentic case; their distribution among tested fluids and solid tissues were also measured. This is the first report on the distribution of ropivacaine and its major metabolite 3-hydroxyropivacaine in a human case.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.