Direct mass spectrometry analysis of exhaled human breath in real-time.

The molecular composition of exhaled human breath can reflect various physiological and pathological conditions. Considerable progress has been achieved over the past decade in real-time analysis of exhaled human breath using direct mass spectrometry methods, including selected ion flow tube mass spectrometry, proton transfer reaction mass spectrometry, extractive electrospray ionization mass spectrometry, secondary electrospray ionization mass spectrometry, acetone-assisted negative photoionization mass spectrometry, atmospheric pressure photoionization mass spectrometry, and low-pressure photoionization mass spectrometry. Here, recent developments in direct mass spectrometry analysis of exhaled human breath are reviewed with regard to analytical performance (chemical sensitivity, selectivity, quantitative capabilities) and applications of the developed methods in disease diagnosis, targeted molecular detection, and real-time metabolic monitoring.

High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling.

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high-throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high-throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low-discrepancy sampling, including on-the-fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov-Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Analysis of VOCs in Liquids through Vaporization in a Tubular Oven Monitored by Chemical Ionization Mass Spectrometry.

The analysis of chemical compounds present at trace levels in liquids is important not only for environmental measurements but also, for example, in the health sector. The reference technique for the analysis of Volatile Organic Compounds (VOCs) in liquids is GC, which is difficult to use with an aqueous matrix. In this work, we present an alternative technique to GC to analyze VOCs in water. A tubular oven is used to completely vaporize the liquid sample deposited on a gauze. The oven is heated in the presence of a dinitrogen flow, and the gas is analyzed at the exit of the oven by a chemical ionization mass spectrometer developed in our laboratory. It is a low magnetic field Fourier Transform Ion Cyclotron Resonance (FT-ICR) optimized for real-time analysis. The Proton Transfer Reaction (PTR) used during the Chemical Ionization event results in the selective ionization of the VOCs present in the gas phase. The optimization of the desorption conditions is described for the main operating parameters: temperature ramp, liquid quantity, and nitrogen flow. Their influence is studied using a 100 ppmv aqueous toluene solution. The analytical method is then tested on a mixture of seven VOCs.

Effective voltammetric tool for Nano-detection of triazine herbicide (1-Chloro-3-ethylamino-5-isopropylamino-2,4,6-triazine) by naphthalene derivative.

The development and operation of a nanosensor for detecting the poisonous 1-chloro-3-ethylamino-5-isopropylamino-2,4,6-triazine (Atrazine) are described in this study for the first time. The carbon electrode (CE) surface was modified with cysteine-substituted naphthalene diimide to create this sensitive platform. The developed nanosensor (NDI-cys/GCE) was evaluated for its ability to sense Atrazine using differential pulse voltammetry and cyclic voltammetry. To achieve the best response from the target analyte, the effects of several parameters were examined to optimize the conditions. The cysteine-substituted naphthalene diimide significantly improved the signals of the Atrazine compared to bare GCE due to the synergistic activity of substituted naphthalene diimide and cysteine molecules. Under optimal conditions, atrazine detection limits at the (NDI-cys/GCE) were reported to be 94 nM with a linear range of 10-100 µM. The developed sensing platform also showed positive results when used to detect the atrazine herbicide in real tap water, wastewater, and milk samples. Furthermore, a reasonable recovery rate for real-time studies, repeatability, and stability revealed that the developed electrochemical platform could be used for sample analysis.

Development of two devices for high-throughput screening of ethanol-producing microorganisms by real-time CO2 production monitoring.

Bioethanol's importance as a renewable energy carrier led to the development of new devices for the high-throughput screening (HTS) of ethanol-producing microorganisms, monitoring ethanol production, and process optimization. This study developed two devices based on measuring CO2 evolution (an equimolar byproduct of microbial ethanol fermentation) to allow for a fast and robust HTS of ethanol-producing microorganisms for industrial purposes. First, a pH-based system for identifying ethanol producers (Ethanol-HTS) was established in a 96-well plate format where CO2 emission is captured by a 3D-printed silicone lid and transferred from the fermentation well to a reagent containing bromothymol blue as a pH indicator. Second, a self-made CO2 flow meter (CFM) was developed as a lab-scale tool for real-time quantification of ethanol production. This CFM contains four chambers to simultaneously apply different fermentation treatments while LCD and serial ports allow fast and easy data transfer. Applying ethanol-HTS with various yeast concentrations and yeast strains displayed different colors, from dark blue to dark and light green, based on the amount of carbonic acid formed. The results of the CFM device revealed a fermentation profile. The curve of CO2 production flow among six replications showed the same pattern in all batches. The comparison of final ethanol concentrations calculated based on CO2 flow by the CFM device with the GC analysis showed 3% difference which is not significant. Data validation of both devices demonstrated their applicability for screening novel bioethanol-producer strains, determining carbohydrate fermentation profiles, and monitoring ethanol production in real time.

Structural Health Monitoring Design and Performance Evaluation of a Middle-Span Bridge.

Structural health monitoring (SHM) has attracted significant attention over the past two decades due to its ability to provide real-time insight into the condition of structures. Despite the development of several SHM systems for long-span bridges, which play a crucial role in the assessment of these structures, studies focusing on short- or middle-span bridges remain scarce. This research paper presents an efficient and practical bridge monitoring and warning system established on a middle-span bridge, a key crossroad bridge located in Shenzhen. The monitoring system consists of sensors and measuring points that collect a substantial amount of data, enabling the close monitoring of various operational indicators to facilitate the early detection of threshold exceedances. Based on this system, the subtle condition of the bridge can be evaluated, and the operational condition of the bridge can be studied through the comparative analysis of the collected data. Over four months of monitoring, data including the strain and creep of the main beam, the strain and settlement of piers and the crack width of the bridge body are observed. Furthermore, the real-time operational status of the bridge is analyzed and evaluated through the combination of the collected data and the structural finite element model.

Optimized Sequencing Adaptors Enable Rapid and Real-Time Metagenomic Identification of Pathogens during Runtime of Sequencing.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) offers the promise of unbiased detection of emerging pathogens. However, in indexed sequencing, the sequential paradigm of data acquisition, demultiplexing, and analysis restrain read assignment in advance and real-time analysis, resulting in lengthy turnaround time for clinical metagenomic detection. METHODS: We described the utility of internal-index adaptors with different lengths of barcode in multiplex sequencing. The base composition for each position within these adaptors was well-balanced to ensure nucleotide diversity and optimal sequencing performance and to achieve the early assignment of reads by first sequencing the barcodes. Combined with an automated library preparation device, we delivered a rapid and real-time bioinformatics pathogen identification solution for the Illumina NextSeq platform. The diagnostic performance was evaluated by testing 153 lower respiratory tract specimens using mNGS in comparison to culture, 16S/internal transcribed spacer amplicon sequencing, and additional PCR-based tests. RESULTS: By calculating the average F1 scores of all read lengths under different threshold values, we established the optimal threshold for pathogens identification, and found that 36 bp was the optimal shortest read length for rapid mNGS analysis. Rapid detection had a negative percentage agreement and positive percentage agreement of 100% and 85.1% for bacteria and 97.4% and 80.3% for fungi, when compared to a composite standard. The rapid mNGS solution enabled accurate pathogen identification in about 9.1 to 10.1 h sample-to-answer turnaround time. CONCLUSIONS: Optimized internal index adaptors combined with a real-time analysis pipeline provide a potential tool for a first-line test in critically ill patients.

Perimeter Intrusion Detection by Video Surveillance: A Survey.

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics.

Method for Determining Treated Metal Surface Quality Using Computer Vision Technology.

Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements.

Optimizing the Sensitivity of Biological Particle Detectors through Atmospheric Particle Analysis According to Climatic Characteristics in South Korea.

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary.

An Intelligent Correlation Real-Time Analysis Method for the Mechanical Properties of Members in Super-Span Valve Hall Grid Structure Hoisting Process.

The mechanical performance analysis of the members is the primary basis for evaluating the hoisting quality and safety of the valve hall grid structure. Ordinarily, manual analysis of monitoring data and on-site experience inspection are employed to structural judgment, but it is challenging to evaluate the correlation of the various members and the overall safety of a valve hall. In this paper, an intelligent correlation real-time analysis method based on a BPNN (Back Propagation Neural Network) for the mechanical properties of members is proposed to intelligently control the safety of valve hall grid structure hoisting. The correlation between the mechanical properties of multi-points in the grid structure is used to model the target measuring points. In addition, an intelligent real-time analysis system is used to manage and apply the mechanical property correlation and abnormality of members in real-time. Then, the model is applied to a super-span valve hall in South China, and the application effect is good. The mechanical property correlation model can accurately reflect the mechanical state of the valve hall grid structure hoisting process. Simultaneously, it can effectively pinpoint hidden dangers and locate risk members. It provides a new reference for the normal operation and maintenance of a super-span valve hall grid.

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking.

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

Real Time Monitoring of Wine Vinegar Supply Chain through MOX Sensors.

Vinegar is a fermented product that is appreciated world-wide. It can be obtained from different kinds of matrices. Specifically, it is a solution of acetic acid produced by a two stage fermentation process. The first is an alcoholic fermentation, where the sugars are converted in ethanol and lower metabolites by the yeast action, generally Saccharomyces cerevisiae. This was performed through a technique that is expanding more and more, the so-called "pied de cuve". The second step is an acetic fermentation where acetic acid bacteria (AAB) action causes the conversion of ethanol into acetic acid. Overall, the aim of this research is to follow wine vinegar production step by step through the volatiloma analysis by metal oxide semiconductor MOX sensors developed by Nano Sensor Systems S.r.l. This work is based on wine vinegar monitored from the grape must to the formed vinegar. The monitoring lasted 4 months and the analyses were carried out with a new generation of Electronic Nose (EN) engineered by Nano Sensor Systems S.r.l., called Small Sensor Systems Plus (S3+), equipped with an array of six gas MOX sensors with different sensing layers each. In particular, real-time monitoring made it possible to follow and to differentiate each step of the vinegar production. The principal component analysis (PCA) method was the statistical multivariate analysis utilized to process the dataset obtained from the sensors. A closer look to PCA graphs affirms how the sensors were able to cluster the production steps in a chronologically correct manner.

Protonated acetone ion chemical ionization time-of-flight mass spectrometry for real-time measurement of atmospheric ammonia.

Ammonia (NH3) is ubiquitous in the atmosphere, it can affect the formation of secondary aerosols and particulate matter, and cause soil eutrophication through sedimentation. Currently, the use of radioactive primary reagent ion source and the humidity interference on the sensitivity and stability are the two major issues faced by chemical ionization mass spectrometer (CIMS) in the analysis of atmospheric ammonia. In this work, a vacuum ultraviolet (VUV) Kr lamp was used to replace the radioactive source, and acetone was ionized under atmospheric pressure to obtain protonated acetone reagent ions to ionize ammonia. The ionization source is designed as a separated three-zone structure, and even 90 vol.% high-humidity samples can still be directly analyzed with a sensitivity of sub-ppbv. A signal normalization processing method was designed, and with this new method, the quantitative relative standard deviation (RSD) of the instrument was decreased from 17.5% to 9.1%, and the coefficient of determination was increased from 0.8340 to 0.9856. The humidity correction parameters of the instrument were calculated from different humidity, and the ammonia concentrations obtained under different humidity were converted to its concentration under zero humidity condition with these correction parameters. The analytical time for a single sample is only 60 sec, and the limit of detection (LOD) was 8.59 pptv (signal-to-noise ratio S/N = 3). The ambient measurement made in Qingdao, China, in January 2021 with this newly designed CIMS, showed that the concentration of ammonia ranged from 1 to 130 ppbv.

Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction.

Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an "online" fashion. Widely used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data-in terms of alignment changes, sequence addition or removal-present common scenarios that can benefit from online inference.

Intracellular Detection and Localization of Nanoparticles by Refractive Index Measurement.

The measuring of nanoparticle toxicity faces an important limitation since it is based on metrics exposure, the concentration at which cells are exposed instead the true concentration inside the cells. In vitro studies of nanomaterials would benefit from the direct measuring of the true intracellular dose of nanoparticles. The objective of the present study was to state whether the intracellular detection of nanodiamonds is possible by measuring the refractive index. Based on optical diffraction tomography of treated live cells, the results show that unlabeled nanoparticles can be detected and localized inside cells. The results were confirmed by fluorescence measurements. Optical diffraction tomography paves the way to measuring the true intracellular concentrations and the localization of nanoparticles which will improve the dose-response paradigm of pharmacology and toxicology in the field of nanomaterials.

Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow*.

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data-driven continuous flow synthesis.

Remote Atmospheric Pressure Infrared Matrix-Assisted Laser Desorption-Ionization Mass Spectrometry (Remote IR-MALDI MS) of Proteins.

Remote Infrared Matrix-Assisted Laser Desorption/Ionization (Remote IR-MALDI) system using tissue endogenous water as matrix was shown to enable in vivo real-time mass spectrometry analysis with minimal invasiveness. Initially the system was used to detect metabolites and lipids. Here, we demonstrate its capability to detect and analyze peptides and proteins. Very interestingly, the corresponding mass spectra show ESI-like charge state distribution, opening many applications for structural elucidation to be performed in real-time by Top-Down strategy. The charge states show no dependence toward laser wavelength or length of the transfer tube. Indeed, remote analysis can be performed 5 m away from the mass spectrometer without modification of spectra. On the contrary, addition of glycerol to water shift the charge state distributions toward even higher charge states. The desorption/ionization process is very soft, allowing to maintain protein conformation as in ESI. Observation of proteins and similar spectral features on tissue, when protein standards are deposited on raw tissue pieces, could potentially open the way to their direct analysis from biological samples. This also brings interesting features that could contribute to the understanding of IR MALDI ionization mechanism.

Cellogram: On-the-Fly Traction Force Microscopy.

Traction force microscopy (TFM) derives maps of cell-generated forces, typically in the nanonewton range, transmitted to the extracellular environment upon actuation of complex biological processes. In traditional approaches, force rendering requires a terminal, time-consuming step of cell deadhesion to obtain a reference image. A conceptually opposite approach is provided by reference-free methods, opening to the on-the-fly generation of force maps from an ongoing experiment. This requires an image processing algorithm keeping the pace of the biological phenomena under investigation. Here, we introduce an integrated software pipeline rendering force maps from single reference-free TFM images seconds to minutes after their acquisition. The algorithm tackles image processing, reference image estimation, and finite element analysis as a single problem, yielding a robust and fully automatic solution. The method's capabilities are demonstrated in two applications. First, the mechanical annihilation of cancer cells is monitored as a function of rising environmental temperature, setting a population threshold at 45 °C. Second, the fast temporal correlation of forces produced across individual cells is used to map physically connected adhesion points, yielding typical lengths that vary as a function of the cell cycle phase.