Conventional Identification Methods Research Articles

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics.

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

Comparison of commercial next-generation sequencing assays to conventional culture methods for bacterial identification and antimicrobial susceptibility of samples obtained from clinical cases of canine superficial bacterial folliculitis.

Bacterial identification and antimicrobial susceptibility testing is an important step in timely therapeutic decisions for canine superficial bacterial folliculitis (SBF), commonly caused by Staphylococcus pseudintermedius. Next-generation sequencing (NGS) offers the appeal of potentially expedited results with complete detection of bacterial organisms and associated resistance genes compared to culture. Limited studies exist comparing the two methodologies for clinical samples. To compare and contrast genotypic and phenotypic methods for bacterial identification and antimicrobial susceptibility from cases of canine SBF. Twenty-four client-owned dogs with lesions consistent with SBF were enrolled. A sterile culturette swab was used to sample dogs with SBF lesions. The swab was rinsed in 0.9 mL of sterile phosphate-buffered saline and vortexed to create a homogenous solution. Two swabs for NGS laboratories (Labs) and one swab for culture (Culture Lab) were randomly sampled from this solution and submitted for bacterial identification and antimicrobial susceptibility. No statistical difference regarding turnaround time for NGS Labs compared to Culture Lab was found. NGS Lab 1 identified more organisms than NGS Lab 2 and Culture Lab, which were both statistically significant. There was no statistical difference in detection frequency for Staphylococcus spp. among all laboratories. There was poor agreement for the presence of meticillin resistance and most antimicrobials among all laboratories. Utilisation of NGS as a replacement for traditional culture when sampling canine SBF lesions is not supported at this time.

Intermittent dynamics identification and prediction from experimental data of discrete-mode semiconductor lasers by reservoir computing

Analysis of intermittent dynamics from experimental data is essential to promote the understanding of practical complex nonlinear systems and their underlying physical mechanisms. In this paper, reservoir computing enabled dynamics prediction, and identification of two types of intermittent switching using experimental data from discrete-mode semiconductor lasers are rigorously studied and demonstrated. The results show that, for the dynamics prediction task, both regular and irregular intermittent switching can be predicted reliably by reservoir computing, achieving the average normalized mean-square error of less than 0.015. Additionally, the impact of the number of virtual nodes in the reservoir layer, as well as the train-test split ratio on prediction performance, is explored. For the dynamic identification task, a 2-class classification test is adopted, and the corresponding binary accuracy is calculated to evaluate the identification performance. The results demonstrate that the accuracy of identifying both regular and irregular intermittent switching exceeds 0.996. Compared with the conventional amplitude threshold identification method, the reservoir computing-driven dynamics identification method exhibits superior accuracy, especially in the intermittent transient transition regions.

Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Comparison of the Direct Identification and Short-Term Incubation Methods for Positive Blood Cultures via MALDI-TOF Mass Spectrometry.

Timely pathogen identification in bloodstream infections is crucial for patient care. A comparison is made between positive blood culture (BC) pellets from serum separator tubes using a direct identification (DI) method and colonies on agar plates from a short-term incubation (STI) method with a matrix-assisted laser desorption/ionization Biotyper for the evaluation of 354 monomicrobial BCs. Both the DI and STI methods exhibited similar identification rates for different types of bacteria, except for Gram-positive and anaerobic bacteria. The DI method's results aligned closely with the STI method's results for Enterobacterales, glucose-non-fermenting Gram-negative bacilli (GNB), and carbapenem-resistant Enterobacterales. The DI method exhibited high concordance with the conventional method for GNB identification, achieving 88.2 and 87.5% accuracy at the genus and species levels, respectively. Compared with the STI method, the DI method showed a less successful performance for Gram-positive bacterial identification (50.5 vs. 71.3%; p < 0.01). The DI method was useful for anaerobic bacterial identification of slow-growing microorganisms without any need for colony growth, unlike in the STI method (46.7 vs. 13.3%; p = 0.04). However, both methods could not identify yeast in positive BCs. Overall, the DI method provided reliable results for GNB identification, offering many advantages over the STI method by significantly reducing the turnaround time and enabling quicker pathogen identification in positive BCs.

Isolation and identification of hoylesella marshii causing pleural infection: a case report

BackgroundHoylesella marshii can be isolated from human oral cavities affected by dental pulp and periodontal infections, as well as from the dental plaque of healthy individuals, making it a common bacterium within the oral microbiota. However, its role in causing pleural infections in humans is rare.Case presentationA case of purulent pleural effusion occurred shortly after discharge in an elderly patient who had undergone surgery for gastric cancer. The infection was identified as being caused by an obligate anaerobe through laboratory culture, and was further identified as Hoylesella marshii causing pleural infection through 16 S rRNA gene sequence analysis. Susceptibility testing guided precise treatment with cefoperazone-sulbactam and metronidazole. The patient’s clinical symptoms improved rapidly, laboratory test indicators gradually returned to normal, and the patient ultimately recovered.ConclusionHoylesella marshii can cause pleural infections in humans. Clinical microbiology laboratories should pay special attention to the cultivation of obligate anaerobes when routine aerobic cultures do not show bacterial growth but bacteria are visible on smear staining, and when conventional identification methods fail to identify the bacterium, analysis based on the highly conserved 16 S rRNA gene sequence can accurately and specifically identify the bacterium, guiding clinicians in formulating precise anti-infection strategies.

Advancements in brain tumor identification: Integrating synthetic GANs with federated-CNNs in medical imaging analysis

Brain tumors are a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning and management. However, conventional methods for brain tumor identification through medical imaging analysis often face challenges related to accuracy, efficiency, and privacy concerns. Current approaches may struggle with limited datasets, privacy regulations hindering data sharing, and the need for specialized expertise in interpreting medical images. Accurately identifying brain tumors is pivotal in diagnosis, treatment planning, and patient prognosis. This research proposes a novel approach for advancing brain tumor identification by integrating Synthetic Generative Adversarial Networks with federated convolutional neural Networks in medical imaging analysis. Federated-CNNs are a type of neural network architecture designed for federated learning scenarios. In federated learning, model training occurs locally on data distributed across multiple devices or institutions without exchanging raw data. Federated CNNs allow collaborative model training across these distributed datasets by aggregating local model updates rather than exchanging raw data. This approach ensures that sensitive data remain localized within each participating institution, thus addressing privacy concerns in medical imaging analysis. Our methodology harnesses the power of GANs to generate synthetic brain MRI images, addressing data scarcity issues commonly encountered in medical imaging datasets. These synthetic images are then utilized in conjunction with Federated-CNNs, enabling cooperative model training between many healthcare institutions while maintaining the anonymity and privacy of data. Moreover, integrating Federated CNNs ensures that sensitive medical imaging data remain localized within participating institutions, addressing data privacy concerns and fostering collaboration among medical professionals. The research advances medical imaging analysis by introducing a novel methodology that leverages existing technologies to improve brain tumor identification accuracy. Specifically, the feature extraction phase using DenseNet121, implemented in MATLAB, achieves an outstanding accuracy of 99.82 % and outperforms Various existing methods, including Inception-V3, ResNet-18, and GoogleNet, demonstrating the efficacy of our approach in capturing discriminative features from medical imaging data. This high accuracy underscores the potential of our methodology to enhance diagnostic accuracy and clinical decision-making in neurology and oncology. The research offers a promising avenue for further exploration and innovation in medical imaging analysis, with significant implications for improving patient outcomes and advancing healthcare practices.

Identification of Nontuberculous Mycobacterium Species by Polymerase Chain Reaction - Restriction Enzyme Analysis (PCR-REA) of rpoB gene in Clinical Isolates.

Nontuberculous mycobacteria (NTM) infections are an emerging global health concern with increasing incidence. Conventional identification methods for NTM species in clinical settings are prone to errors. This study evaluates a newer method, polymerase chain reaction-restriction enzyme analysis (PCR-REA) of the rpoB gene, for NTM species identification. The study identified NTM species in clinical samples using conventional biochemical techniques and compared the results with PCR-REA of the rpoB gene. This cross-sectional study was conducted at a tertiary health-care center in North India over 18 months, analyzing both pulmonary and extrapulmonary samples. Two hundred and forty-seven NTM isolates were identified using phenotypic and biochemical methods. The same isolates were subjected to rpoB gene amplification by PCR followed by REA using Msp I and Hae III enzymes. Conventional methods identified 12 different NTM species (153 slow-growing and 94 rapid-growing), whereas PCR-REA identified 16 species (140 slow-growing, 107 rapid-growing). The Mycobacterium avium intracellulare complex was the most common species isolated. PCR-REA demonstrated higher resolution in species identification, particularly in differentiating within species complexes. PCR-REA of the rpoB gene proves to be a simple, rapid, and more discriminative tool for NTM species identification compared to conventional methods. This technique could significantly improve the diagnosis and management of emerging NTM infections in clinical settings.

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

Versatile conductometric biosensors for rapid and selective detection of inflammatory and cardiac biomarkers in saliva

Biosensors have become promising alternatives to the conventional methods in early identification of diseases. However, translation of biosensors from lab to commercial products have challenges such as complex sensor fabrications and complicated detection, and inadequate sensitivity and selectivity. Here, we introduce simple and low-cost fabricated conductometric sensors based on high resistivity silicon wafers (HR-Si) which can be adopted to functionalise with both natural and synthetic antibodies in detecting five biomarkers including interleukin-6, C reactive protein, cardiac troponin I, brain natriuretic peptide, and N terminal-probrain natriuretic peptide. All five biomarkers show selective and rapid (10 min sample incubation and <1 min of reading time) detection in both media of phosphate buffer saline and saliva with the detection limits lower than that of reported healthy levels in saliva. This work highlights the versatility of HR-Si sensors in functionalisation of both natural and synthetic antibodies in sensitive and selective biomarker detection. As these miniaturised conductometric biosensors can be easily modified with on-demand biomaterials to detect corresponding target biomarkers, they enable a new category of compact point-of-care medical devices.

Kinematic Parameter Identification and Error Compensation of Industrial Robots Based on Unscented Kalman Filter with Adaptive Process Noise Covariance

Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively.

Enhancing Power System Reliability Through Advanced Fault Diagnosis Methods: A Deep Learning Approach

In order to attain high reliability in power systems, effective fault detection techniques that can quickly identify and mitigate defects must be developed. In this article, we suggest a novel method for improving power system dependability by using deep learning techniques to identify faults. In order to achieve precise fault identification and classification, our technology uses deep neural networks to automatically learn and extract features from power system data. We thoroughly test our method on a range of fault scenarios and real-world datasets to determine its efficacy. The outcomes show how well our approach performs in comparison to conventional fault identification methods, underscoring the possibility of a major increase in power system reliability. By providing useful insights for improving problem diagnosis techniques in power engineering, this research advances system resilience and minimizing downtime.

A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory

As coal ore and other resources are continuously mined, a three-zone structure is formed underground consisting of a sagging zone, fault zone, and caving zone. The use of well-logging data to identify the three zones is important for production safety and environmental management. Owing to the scarcity of data that can reflect three zones in normal coal mining, conventional identification and prediction methods face challenges when extracting data features, incurring a degree of uncertainty within prediction results. Accordingly, the accurate identification of the three zones has become a critical objective in daily production. To address this issue, we developed a method called a method called backpropagation neural networks with Dempster–Shafer (DS) evidence theory. Initially, we preprocessed the training data and deployed two backpropagation neural networks (BPNNs) to predict the three zones according to two parameters. According to these prediction results, the local and global credibility of each prediction is calculated and used to obtain the basic probability assignment function required for the DS evidence theory. Finally, the DS evidence theory is used to fuse the two BPNNs prediction results, thereby producing the final prediction results. The proposed method was demonstrated to improve prediction accuracy by 6.4% compared to a conventional neural network.

Comprehensive Person Profiling System

The proposed Comprehensive person profiling system aims to use innovative approach to revolutionizing identity verification in financial transactions, particularly within banks or other financial organizations. This project aims to replace traditional national ID usage with a more efficient and secure system. Leveraging advanced technologies such as biometrics, data analytics, and machine learning, the system ensures a comprehensive profiling of individuals, offering a robust alternative to conventional identification methods. By seamlessly integrating into financial institutions, this solution enhances inconveniences of the users’ doing transactions, security, minimizes identity fraud, and streamlines customer authentication processes. The Comprehensive Person Profiling System not only safeguards sensitive information but also enhances the overall efficiency and reliability of financial transactions, marking a significant leap towards a more secure and technologically advanced future in the realm of personal identification.

A Robust Process Identification Method under Deterministic Disturbance

This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic disturbances. Moreover, it shows promising results in providing a reasonably accurate disturbance model despite structural disparities between the actual disturbance and the model. By improving the precision of process models under deterministic disturbances, the proposed method paves the way for developing refined and reliable control strategies, aligning with the evolving demands of modern industries and laying solid groundwork for future research aimed at broadening application across diverse industrial practices.

Molecular dynamics simulation study of nitrogen vacancy color centers prepared by carbon ion implantation into diamond

Nitrogen vacancy (NV) color centers in diamond have useful applications in quantum sensing and fluorescent marking. They can be generated experimentally by ion implantation, femtosecond lasers, and chemical vapor deposition. However, there is a lack of studies of the yield of NV color centers at the atomic scale. In the molecular dynamics simulations described in this paper, NV color centers are prepared by ion implantation in diamond with pre-doped nitrogen and subsequent annealing. The differences between the yields of NV color centers produced by implantation of carbon (C) and nitrogen (N) ions, respectively, are investigated. It is found that C-ion implantation gives a greater yield of NV color centers and superior location accuracy. The effects of different pre-doping concentrations (400–1500 ppm) and implantation energies (1.0–3.0 keV) on the NV color center yield are analyzed, and it is shown that a pre-doping concentration of 1000 ppm with 2 keV C-ion implantation can produce a 13% yield of NV color centers after 1600 K annealing for 7.4 ns. Finally, a brief comparison of the NV color center identification methods is presented, and it is found that the error rate of an analysis utilizing the identify diamond structure + coordination analysis method is reduced by about 7% compared with conventional identification methods.

Grey-box modeling for thermal dynamics of buildings under the presence of unmeasured internal heat gains

An accurate grey-box building thermal model is an essential component for prediction-based control of split air conditioners. As the thermal performance of building envelope improves, the internal heat gains play an increasingly noteworthy influence on the building’s thermal dynamics. However, the internal heat gains are usually not measured, which makes the conventional data-dependent identification methods of building thermal models fail. To improve the accuracy of building thermal models under unmeasured internal heat gains, a two-step parameter identification framework for resistance–capacitance (RC) models is proposed in this paper. First, the time-invariant building model parameters are estimated by designing a novel optimization objective function. Next, the time-varying internal heat gains are identified based on the mismatch in predictions of the estimated building model from the first step and the measured temperature. The effectiveness of the method is evaluated using data from a simulation case and an experiment case. The results show that the proposed method outperforms the conventional identification method as well as two other identification methods that handle the unmeasured internal heat gains. Besides, the internal heat gains estimated by the proposed method can capture the trend of the actual internal loads well.

Developing an eDNA approach for wetland biomonitoring: Insights on technical and conventional approaches

AbstractWetlands are ecologically and culturally significant ecosystems that are experiencing biodiversity declines globally. Biomonitoring techniques that use environmental DNA (eDNA) to detect and monitor biodiversity are well established in lake, riverine, and marine ecosystems. However, their use in wetlands requires further development due to the presence of sediments that block eDNA filters to limit water filtration, alongside a lack of standardized methodology. In this study, we examined eDNA dynamics to understand spatiotemporal biodiversity patterns in an Aotearoa New Zealand wetland and to optimize their application to wetland‐specific challenges. We sampled four sites across Opuatia Wetland at three time points during an austral spring. We conducted conventional taxonomic surveys, tested three different filter sizes (1.2 μm, 5 μm, and semi‐quantitative dacron filters), and assessed our ability to detect foreign DNA (from kea; Nestor notabilis) at different time points and distances post‐release. We found significant differences in DNA sequence composition across time and space, and when using different sized filters. eDNA data generally complemented (versus replaced) conventional survey and identification methods, with certain species only detected by one method or the other. Taxonomic resolution of conventional sampling and identification methods often exceeded that of eDNA. Foreign DNA was detectable 10 m from its release point for up to 1 week post‐release. Our results provide new considerations for future eDNA research in wetland environments, where rapid biomonitoring techniques are needed to support conservation and preservation.

Identification of bacteria of the genus Vibrio in some water sources in the prefectures of Télimélé and Boké (Republic of Guinea).

For a long time, the main problem with water intended for human consumption has been health, and this problem stems from the existence of micro-organisms (bacteria, viruses, protozoa, parasites) that transmit many dangerous infections to humans. Vibrio cholerae is one of these micro-organisms. Cholera is a severe acute diarrhoeal disease caused by Vibrio cholerae, a Gram-negative bacterium that colonises warm, saline and alkaline surface waters, often in association with phytoplankton or zooplankton. The aim of this study was to identify bacteria of the Vibrio genus in the waters of the Télimélé and Boké prefectures. The study was carried out from the beginning of May to the end of September 2021. The samples were analysed in the bacteriology laboratory of the Institut de Recherche en Biologie Appliquée de Guinée (IRBAG) in Kindia, using conventional identification methods. The results of the analysis proved the existence of the bacterial strain in the water analysed. During the analyses, we encountered two types of Vibrio: parahaemolyticus with 1.28% in Télimélé and alginolyticus with 2.56% in Boké. These germs were found in wells and lakes, but Vibrio spp was not found. In addition to bacteria of the species Vibrio parahaemolyticus and Vibrio alginolyticus, we encountered two (2) other types of microorganisms: Enterococcus faecalis observed in ponds and streams and Salmonella spp. found in taps, boreholes and wells.

Tight carbonate reservoir evaluation case study based on neural network assisted fracture identification and analytic hierarchy process

Comprehensive evaluation of reservoirs is an important link in gas reservoir exploration and development. The evaluation of tight carbonate reservoirs often focuses on the characteristics of porosity and permeability, ignoring the important factor of fractures, also the quantitative evaluation of reservoirs is relatively few. It is difficult to identify fractures and evaluate the reservoir factors qualitatively and quantitatively. Herein, the sedimentary microfacies and microporosity of the tight carbonate reservoir of the Ma55 submember in the eastern Sulige area are comprehensively studied by casting thin section, rock physical property, and capillary pressure test data. The backpropagation (BP) neural network algorithm is used to identify and predict fractures. Finally, through the analytic hierarchy process, the above reservoir influencing factors are modeled and quantitatively analyzed for reservoir evaluation. The results show that the highest probability of fracture development in the central and northwest areas of the study area can reach 0.92. The accuracy of the BP neural network model in identifying cracks can reach 80%, which is reliable and effective compared with the conventional logging identification method. Reservoirs can be classified into four types according to their quality. The synthetic weights of porosity, permeability and fracture development probability are 0.2, 0.2 and 0.216 respectively, which are the three most important evaluation parameters. This study improves the accuracy of fracture identification and prediction of tight reservoirs in comprehensive reservoir evaluation, which provides guidance and scheme for more detailed exploration and development of tight carbonate reservoirs.