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Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.
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Proteômica , Espectrometria de Massas em Tandem , Proteômica/métodos , Humanos , Espectrometria de Massas em Tandem/métodos , Células HeLa , Cromatografia Líquida/métodos , Proteoma/análise , Peptídeos/análise , Peptídeos/químicaRESUMO
Ultralow flow LC employs ultra-narrow bore columns and mid-range pL/min to low nL/min flow rates (i.e., ≤20 nL/min). The separation columns that are used under these conditions are typically 2-30 µm in inner diameter. Ultralow flow LC systems allow for exceptionally high sensitivity and frequently high resolution. There has been an increasing interest in the analysis of scarce biological samples, for example, circulating tumor cells, extracellular vesicles, organelles, and single cells, and ultralow flow LC was efficiently applied to such samples. Hence, advances towards dedicated ultralow flow LC instrumentation, technical approaches, and higher throughput (e.g., tens-to-hundreds of single cells analyzed per day) were recently made. Here, we review the types of ultralow flow LC technology, followed by a discussion of selected representative ultralow flow LC applications, focusing on the progress made in bioanalysis of amount-limited samples during the last 10 years. We also discuss several recently reported high-sensitivity applications utilizing flow rates up to 100 nL/min, which are below commonly used nanoLC flow rates. Finally, we discuss the path forward for future developments of ultralow flow LC.
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Cromatografia Líquida , Cromatografia Líquida/métodosRESUMO
In this work, we pioneered the assessment of coupling high-field asymmetric waveform ion mobility spectrometry (FAIMS) with ultrasensitive capillary electrophoresis hyphenated with tandem mass spectrometry (CE-MS/MS) to achieve deeper proteome coverage of low nanogram amounts of digested cell lysates. An internal stepping strategy using three or four compensation voltages per analytical run with varied cycle times was tested to determine optimal FAIMS settings and MS parameters for the CE-FAIMS-MS/MS method. The optimized method applied to bottom-up proteomic analysis of 1 ng of HeLa protein digest standard identified 1314 ± 30 proteins, 4829 ± 200 peptide groups, and 7577 ± 163 peptide spectrum matches (PSMs) corresponding to a 16, 25, and 22% increase, respectively, over CE-MS/MS alone, without FAIMS. Furthermore, the percentage of acquired MS/MS spectra that resulted in PSMs increased nearly 2-fold with CE-FAIMS-MS/MS. Label-free quantitation of proteins and peptides was also assessed to determine the precision of replicate analyses from FAIMS methods with increased cycle times. Our results also identified from 1 ng of HeLa protein digest without any prior enrichment 76 ± 9 phosphopeptides, 18% of which were multiphosphorylated. These results represent a 46% increase in phosphopeptide identifications over the control experiments without FAIMS yielding 2.5-fold more multiphosphorylated peptides.
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Espectrometria de Mobilidade Iônica , Proteômica , Eletroforese Capilar , Espectrometria de Mobilidade Iônica/métodos , Fosfopeptídeos , Proteoma , Proteômica/métodos , Espectrometria de Massas por Ionização por Electrospray/métodos , Espectrometria de Massas em Tandem/métodosRESUMO
In this work, we developed an ultra-sensitive CE-MS/MS method for bottom-up proteomics analysis of limited samples, down to sub-nanogram levels of total protein. Analysis of 880 and 88 pg of the HeLa protein digest standard by CE-MS/MS yielded â¼1100 ± 46 and â¼160 ± 59 proteins, respectively, demonstrating higher protein and peptide identifications than the current state-of-the-art CE-MS/MS-based proteomic analyses with similar amounts of sample. To demonstrate potential applications of our ultra-sensitive CE-MS/MS method for the analysis of limited biological samples, we digested 500 and 1000 HeLa cells using a miniaturized in-solution digestion workflow. From 1-, 5-, and 10-cell equivalents injected from the resulted digests, we identified 744 ± 127, 1139 ± 24, and 1271 ± 6 proteins and 3353 ± 719, 5709 ± 513, and 8527 ± 114 peptide groups, respectively. Furthermore, we performed a comparative assessment of CE-MS/MS and two reversed-phased nano-liquid chromatography (RP-nLC-MS/MS) methods (monolithic and packed columns) for the analysis of a â¼10 ng HeLa protein digest standard. Our results demonstrate complementarity in the protein- and especially peptide-level identifications of the evaluated CE-MS- and RP-nLC-MS-based methods. The techniques were further assessed to detect post-translational modifications and highlight the strengths of the CE-MS/MS approach in identifying potentially important and biologically relevant modified peptides. With a migration window of â¼60 min, CE-MS/MS identified â¼2000 ± 53 proteins on average from a single injection of â¼8.8 ng of the HeLa protein digest standard. Additionally, an average of 232 ± 10 phosphopeptides and 377 ± 14 N-terminal acetylated peptides were identified in CE-MS/MS analyses at this sample amount, corresponding to 2- and 1.5-fold more identifications for each respective modification found by nLC-MS/MS methods.
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Proteômica , Espectrometria de Massas por Ionização por Electrospray , Eletroforese Capilar/métodos , Células HeLa , Humanos , Fosfopeptídeos , Proteômica/métodos , Espectrometria de Massas por Ionização por Electrospray/métodos , Espectrometria de Massas em Tandem/métodosRESUMO
Proteomic analysis of limited samples and single cells requires specialized methods that prioritize high sensitivity and minimize sample loss. Consequently, sample preparation is one of the most important steps in limited sample analysis workflows to prevent sample loss. In this work, we have eliminated sample handling and transfer steps by processing intact cells directly in the separation capillary, online with capillary electrophoresis coupled to tandem mass spectrometry (CE-MS/MS) for top-down proteomic (TDP) analysis of low numbers of mammalian cancer cells (<10) and single cells. We assessed spray voltage injection of intact cells from a droplet of cell suspension (â¼1000 cells) and demonstrated 0-9 intact cells injected with a dependency on the duration of spray voltage application. Spray voltage applied for 2 min injected an average of 7 ± 2 cells and resulted in 33-57 protein and 40-88 proteoform identifications (N = 4). To analyze single cells, manual cell loading by hydrodynamic pressure was used. Replicates of single HeLa cells (N = 4) lysed on the capillary and analyzed by CE-MS/MS demonstrated a range of 17-40 proteins and 23-50 proteoforms identified. An additional cell line, THP-1, was analyzed at the single-cell level, and proteoform abundances were compared to show the capabilities of single-cell TDP (SC-TDP) for assessing cellular heterogeneity. This study demonstrates the initial application of TDP in single-cell proteome-level profiling. These results represent the highest reported identifications from TDP analysis of a single HeLa cell and prove the tremendous potential for CE-MS/MS on-capillary sample processing for high sensitivity analysis of single cells and limited samples.
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Proteômica , Espectrometria de Massas em Tandem , Animais , Proteínas de Ligação a DNA , Células HeLa , Humanos , Mamíferos , Proteoma/análise , Proteômica/métodos , Espectrometria de Massas em Tandem/métodosRESUMO
In-depth LC-MS-based proteomic profiling of limited biological and clinical samples, such as rare cells or tissue sections from laser capture microdissection or microneedle biopsies, has been problematic due, in large, to the inefficiency of sample preparation and attendant sample losses. To address this issue, we developed on-microsolid-phase extraction tip (OmSET)-based sample preparation for limited biological samples. OmSET is simple, efficient, reproducible, and scalable and is a widely accessible method for processing â¼200 to 10,000 cells. The developed method benefits from minimal sample processing volumes (1-3 µL) and conducting all sample processing steps on-membrane within a single microreactor. We first assessed the feasibility of using micro-SPE tips for nanogram-level amounts of tryptic peptides, minimized the number of required sample handling steps, and reduced the hands-on time. We then evaluated the capability of OmSET for quantitative analysis of low numbers of human monocytes. Reliable and reproducible label-free quantitation results were obtained with excellent correlations between protein abundances and the amounts of starting material (R2 = 0.93) and pairwise correlations between sample processing replicates (R2 = 0.95) along with the identification of approximately 300, 1800, and 2000 protein groups from injected â¼10, 100, and 500 cell equivalents, resulting from processing approximately 200, 2000, and 10,000 cells, respectively.
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Proteômica , Manejo de Espécimes , Cromatografia Líquida , Humanos , Espectrometria de Massas , Fluxo de TrabalhoRESUMO
MOTIVATION: Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. RESULTS: We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. AVAILABILITYAND IMPLEMENTATION: https://github.com/shawn-peng/FDR-estimation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Proteômica , Espectrometria de Massas em Tandem , Algoritmos , Bases de Dados de Proteínas , Células HeLa , Humanos , PeptídeosRESUMO
In this work, we pioneered a combination of ultralow flow (ULF) high-efficiency ultranarrow bore monolithic LC columns coupled to MS via a high-field asymmetric waveform ion mobility spectrometry (FAIMS) interface to evaluate the potential applicability for high sensitivity, robust, and reproducible proteomic profiling of low nanogram-level complex biological samples. As a result, ULF LC-FAIMS-MS brought unprecedented sensitivity levels and high reproducibility in bottom-up proteomic profiling. In addition, FAIMS improved the dynamic range, signal-to-noise ratios, and detection limits in ULF LC-MS-based measurements by significantly reducing chemical noise in comparison to the conventional nanoESI interface used with the same ULF LC-MS setup. Two, three, or four compensation voltages separated by at least 15 V were tested within a single LC-MS run using the FAIMS interface. The optimized ULF LC-ESI-FAIMS-MS/MS conditions resulted in identification of 2,348 ± 42 protein groups, 10,062 ± 285 peptide groups, and 15,734 ± 350 peptide-spectrum matches for 1 ng of a HeLa digest, using a 1 h gradient at the flow rate of 12 nL/min, which represents an increase by 38%, 91%, and 131% in respective identifications, as compared to the control experiment (without FAIMS). To evaluate the practical utility of the ULF LC-ESI-FAIMS-MS platform in proteomic profiling of limited samples, approximately 100, 1,000, and 10,000 U937 myeloid leukemia cells were processed, and a one-tenth of each sample was analyzed. Using the optimized conditions, we were able to reliably identify 251 ± 54, 1,135 ± 80, and 2,234 ± 25 protein groups from injected aliquots corresponding to â¼10, 100, and 1,000 processed cells.
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Cromatografia Líquida/métodos , Limite de Detecção , Espectrometria de Massas/métodos , Proteômica/métodos , Células HeLa , Humanos , Nanotecnologia , Fatores de TempoRESUMO
GOAL: To evaluate the analytical parameters of a lateral flow (LF) pepsin immunoassay (Peptest) and assess its suitability in the diagnostics of gastroesophageal reflux disease (GERD). BACKGROUND: Peptest is a noninvasive assay to analyze pepsin in saliva, intended for use in GERD diagnostics. Although commercialized, fundamental studies on its performance are missing. The assay therefore requires basic analytical parameter evaluation to assess its suitability in clinical practice. STUDY: Assay reaction's time dependence, reader device repeatability, and individual LF devices and longitudinal pepsin concentration reproducibility in individual subjects was evaluated. Salivary pepsin was analyzed in 32 GERD patients with extraesophageal reflux symptoms and 13 healthy individuals. RESULTS: The assay's signal increase is not completed at the recommend readout time and continues to increase for another 25 minutes. The relative standard deviation of measurement was good when using the same LF device, ranging from 2.3% to 12.9%, but the reproducibility of 10 different individual LF devices was poor. The random error when analyzing the same saliva sample on 10 LF devices was as high as 36 ng/mL and this value is thus suggested as the positivity cut-off. Pepsin concentration in individual subjects during a 10-day period varied significantly. The sensitivity of the Peptest was 36.8% in the group with acid reflux and 23.1% in the group with weakly acid reflux. The specificity was 61.5%. CONCLUSIONS: The Peptest assay's sensitivity and specificity is low, the results are highly variable and it should not be used as a near-patient diagnostic method in primary care.
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Refluxo Gastroesofágico/diagnóstico , Imunoensaio , Pepsina A/metabolismo , Saliva/metabolismo , Adolescente , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Adulto JovemRESUMO
The analysis of ionic content of exhaled breath condensate (EBC) from one single breath by CE with C(4) D is demonstrated for the first time. A miniature sampler made from a 2-mL syringe and an aluminum cooling cylinder for collection of EBC was developed. Various parameters of the sampler that influence its collection efficiency, repeatability, and effect of respiratory patterns were studied in detail. Efficient procedures for the cleanup of the miniature sampler were also developed and resulted in significant improvement of sampling repeatability. Analysis of EBC was performed by CE-C(4) D in a 60 mM MES/l-histidine BGE with 30 µM CTAB and 2 mM 18-crown-6 at pH 6 and excellent repeatability of migration times (RSD < 1.3% (n = 7)) and peak areas (RSD < 7% (n = 7)) of 12 inorganic anions, cations, and organic acids was obtained. It has been shown that the breathing pattern has a significant impact on the concentration of the analytes in the collected EBC. As the ventilatory pattern can be easily controlled during single exhalation, the developed collection system and method provides a highly reproducible and fast way of collecting EBC with applicability in point-of-care diagnostics.
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Testes Respiratórios/métodos , Condutometria/métodos , Eletroforese Capilar/métodos , Ânions/análise , Asma/diagnóstico , Testes Respiratórios/instrumentação , Cátions/análise , Condutometria/instrumentação , Eletroforese Capilar/instrumentação , Desenho de Equipamento , Expiração , Humanos , Miniaturização , Reprodutibilidade dos Testes , Manejo de EspécimesRESUMO
Clinical and biological samples are often scarce and precious (e.g., rare cell isolates, microneedle tissue biopsies, small-volume liquid biopsies, and even single cells or organelles). Typical large-scale proteomic methods, where significantly higher protein amounts are analyzed, are not directly transferable to the analysis of limited samples due to their incompatibility with pg-, ng-, and low-µg-level protein sample amounts. Here, we report the on-microsolid-phase extraction tip (OmSET)-based sample preparation workflow for sensitive analysis of limited biological samples to address this challenge. The developed platform was successfully tested for the analysis of 100-10,000 typical mammalian cells and is scalable to allow for lower and larger protein amounts and more samples to be analyzed (i.e., higher throughput of analysis).
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Proteômica , Extração em Fase Sólida , Fluxo de Trabalho , Proteômica/métodos , Humanos , Extração em Fase Sólida/métodos , Proteínas/análise , Proteoma/análiseRESUMO
The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity's CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment.
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BACKGROUND: Glycosylation analysis is still challenging, not only because of the extreme structure complexity and conjugation diversity of glycans but also because of instrumental aspects such as the sensitivity limits of analyses. Therefore, glycan analysis by chromatographic methods is very often combined with fluorescence detection in addition to MS. The majority of fluorescent labeling employed before LC separation is based on 2-aminobenzamide, which has several disadvantages such as low labeling yield, poor fluorescence properties, and MS ionization efficiency. Therefore, even after several decades of development of new labels, there is still a need for new labeling tags with improved characteristics. RESULTS: We present the application of a newly synthesized fluorescent label designed for oligosaccharide and glycan analysis by high-performance liquid chromatography with fluorescence detection (HPLC/FLD). The novel hydrazide derivative of dipyrrometheneboron difluoride (BODIPY) was synthesized from 2,4-dimethylpyrrole, methyl succinyl chloride, and boron trifluoride etherate followed by a reaction with hydrazine. The synthesized label was characterized by several analytical methods including NMR, UV/Vis and fluorescence spectroscopy, and mass spectrometry. The labeling reaction via hydrazone formation chemistry was optimized by labeling of maltooligosaccharide standards. The analysis of maltohexaose labeled by BODIPY-hydrazide followed by HPLC/FLD analysis provided the limit of detection in the low tens of femtomole. The presented method based on fluorescence detection is at least 30 times more sensitive than the standard approach employing labeling by 2-aminobenzamide. In addition, the labeling method by BODIPY-hydrazide was used for N-linked glycan profiling of several glycoproteins (ribonuclease B, immunoglobulin G) by RP-HPLC/FLD as well as HILIC/FLD analysis. SIGNIFICANCE: This work represents the design, synthesis, and application of a new fluorescent label based on the BODIPY core and hydrazone formation chemistry for oligosaccharide and glycan analysis by HPLC/FLD. The proposed approach significantly improved the oligosaccharide and glycan analysis in comparison to the commonly used procedure employing 2-aminobenzamide.
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Oligossacarídeos , Polissacarídeos , Cromatografia Líquida de Alta Pressão/métodos , Polissacarídeos/análise , Corantes Fluorescentes/química , Hidrazinas , HidrazonasRESUMO
Nanoflow liquid chromatography-mass spectrometry is key to enabling in-depth proteome profiling of trace samples, including single cells, but these separations can lack robustness due to the use of narrow-bore columns that are susceptible to clogging. In the case of single-cell proteomics, offline cleanup steps are generally omitted to avoid losses to additional surfaces, and online solid-phase extraction/trap columns frequently provide the only opportunity to remove salts and insoluble debris before the sample is introduced to the analytical column. Trap columns are traditionally short, packed columns used to load and concentrate analytes at flow rates greater than those employed in analytical columns, and since these first encounter the uncleaned sample mixture, trap columns are also susceptible to clogging. We hypothesized that clogging could be avoided by using large-bore porous layer open tubular trap columns (PLOTrap). The low back pressure ensured that the PLOTraps could also serve as the sample loop, thus allowing sample cleanup and injection with a single 6-port valve. We found that PLOTraps could effectively remove debris to avoid column clogging. We also evaluated multiple stationary phases and PLOTrap diameters to optimize performance in terms of peak widths and sample loading capacities. Optimized PLOTraps were compared to conventional packed trap columns operated in forward and backflush modes, and were found to have similar chromatographic performance of backflushed traps while providing improved debris removal for robust analysis of trace samples.
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Espectrometria de Massa com Cromatografia Líquida , Proteômica , Proteômica/métodos , Cromatografia LíquidaRESUMO
Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.
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Algoritmos , Demência , Humanos , Idoso , Teorema de Bayes , Aprendizado de Máquina , Demência/diagnóstico , CogniçãoRESUMO
Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.
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Teste de Papanicolaou , Neoplasias do Colo do Útero , Feminino , Humanos , Teste de Papanicolaou/métodos , Neoplasias do Colo do Útero/diagnóstico por imagem , Privacidade , Colo do Útero/diagnóstico por imagem , Redes Neurais de ComputaçãoRESUMO
The article's main contribution is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. The input parameters of the model are the flow intensities of typed cyber-physical attacks, the flow intensities of possible cyber-immune reactions, and the set of probabilities of neutralization of cyber-physical attacks. The set of admissible states of the info-communication system is described taking into account possible variants of the development of the modeled process. The initial parameters of the model are the probabilities of the studied system in the appropriate states at a particular moment. The dynamics of the info-communication system's life cycle are embodied in the form of a matrix of transient probabilities. The mentioned matrix connects the initial parameters in the form of a system of Chapman's equations. The article presents a computationally efficient concept based on Gershgorin's theorems to solve such a system of equations with given initiating values. Based on the presented scientific results, the article proposes the concept of calculating the time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typical cyber-physical attacks. The adequacy of the model and concepts presented in the article is proved by comparing a statically representative amount of empirical and simulated data. We emphasize that the main contribution of the research is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. Based on the created model, the concept of computationally efficient solution of Chapman's equation system based on Gershgorin's theorems and calculating time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typed cyber-physical attacks are formalized. These models and concepts are the highlights of the research.
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The article examines the subject-system interaction session, where the system is understood as a base station, and the subject is understood as a mobile communication device. The peculiarity of the study is taking into account the phenomenon relevant to modern communication infrastructures, which is that the base station supports the division of information traffic into a subspace of guaranteed personalized traffic and a subspace of general-purpose traffic. The study considers a highly critical empirical emergency when the general-purpose traffic subspace may cease to be available at any time. The presented mathematical apparatus describes the impact of such an emergency on the active communication sessions supported by the system in receiving new incoming requests of increasing intensity. To characterize this emergency situation, expressions adapted for practical application are presented to calculate such qualitative parameters as the probability of stability, the probability of failure, and unavailability.
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Comunicação , Redes de Comunicação de Computadores , ProbabilidadeRESUMO
The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.
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COVID-19 , Aprendizado Profundo , Humanos , SARS-CoV-2/fisiologia , Redes Neurais de Computação , AlgoritmosRESUMO
The functional safety assessment is one of the primary tasks both at the design stage and at the stage of operation of critical infrastructure at all levels. The article's main contribution is the information technology of calculating the author's metrics of functional safety for estimating the instance of the model of the cyber-physical system operation. The calculation of metric criteria analytically summarizes the results of expert evaluation of the system in VPR-metrics and the results of statistical processing of information on the system's operation presented in the parametric space Markov model of this process. The advantages of the proposed approach are the following: the need to process orders of magnitude less empirical data to obtain objective estimates of the investigated system; taking into account the configuration scheme and architecture of the security subsystem of the investigated system when calculating the metric; completeness, compactness, and simplicity of interpretation of evaluation results; the ability to assess the achievability of the limit values of the metric criteria based on the model of operation of the investigated system. The paper demonstrates the application of the proposed technology to assess the functional safety of the model of a real cyber-physical system.