High accuracy tightly-coupled integrity monitoring algorithm for map-matching

A map-matching algorithm employs data from Global Positioning System (GPS), a Geographic Information System (GIS)-based road map and other sensors to first identify the correct link on which a vehicle travels and then to determine the physical location of the vehicle on the link. Due to uncertainties associated with the raw measurements from GPS/other sensors, the road map and the related methods, it is essential to monitor the integrity of map-matching results, especially for safety and mission-critical intelligent transport systems such as positioning and navigation of autonomous and semi-autonomous vehicles. Current integrity methods for map-matching are inadequate and unreliable as they fail to satisfy the integrity requirement due mainly to incorrect treatment of all the related uncertainties simultaneously. The aim of this paper is therefore to develop a new tightly-coupled integrity monitoring method for map-matching by properly treating the uncertainties from all sources concurrently. In this method, the raw measurements from GPS, low-cost Dead-Reckoning (DR) sensors and Digital Elevation Model (DEM) are first integrated using an extended Kalman Filter to continuously obtain better position fixes. A weight-based topological map-matching process is then developed to map-match position fixes onto the road map. The accuracy of the map-matching process is enhanced by employing a range of network features such as grade separation, traffic flow directions and the geometry of road link. The Receiver Autonomous Integrity Monitoring (RAIM) technique, which has been successfully applied to monitor the integrity of aircraft navigation, is modified and enhanced so as to apply it to monitor the quality of map-matching. In the enhanced RAIM method, two modifications are made: (1) a variable false alarm rate (as opposed to a constant false alarm rate) is considered to improve the fault detection performance in selecting the links, especially near junctions. (2) a sigma inflation for a non-Gaussian distribution of measurement noises is applied for the purpose of satisfying the integrity risk requirement. The implementation and validation of the enhanced RAIM method is accomplished by utilising the required navigation performance parameters (in terms of accuracy, integrity and availability) of safety and mission-critical intelligent transport systems. The required data were collected from Nottingham and central London. In terms of map-matching, the results suggest that the developed map-matching method is capable of identifying at least 97.7% of the links correctly in the case of frequent GPS outages. In terms of integrity, the enhanced RAIM method provides better the fault detection performance relative to the traditional RAIM

Multiple reference consistency check for LAAS: a novel position domain approach

Since the traditional Maximum Likelihood-based range domain multiple reference consistency check (MRCC) has limitations in satisfying the integrity requirement of CAT II/III for civil aviation, a Kalman filter-based position domain method has been developed for fault detection and exclusion in the Local Area Augmentation System MRCC process. The position domain method developed in this paper seeks to address the limitations of range domain-based MRCC by focusing not only on improving the performance of the fault detection but also on the integrity risk requirement for MRCC. In addition, the issue of the stability of the Kalman filter in relation to the position domain approach is considered. GPS range corrections from multiple reference receivers are fused by the adaptive Kalman filter at the master station for detecting and excluding the single reference receiver’ failure. The performance of the developed Kalman filter-based MRCC has been compared with the traditional method using experimental data. The results reveal that the vertical protection level is slightly better in the traditional method compared with the developed Kalman filter-based approach under the fault-free case. However, the availability can be improved to over 97% in the proposed method relative to the traditional method under the single-fault case. Furthermore, the fault-tolerant positioning result with an accuracy improvement of more than 32% can be achieved even if different fault types are considered under the single-fault case. In particular, the algorithm can be a candidate option as an augmentable complement for the traditional MRCC and can be implemented in a master station element of the LAAS integrity monitoring architecture

Chemical-Vapor-Assisted Electrospray Ionization for Increasing Analyte Signals in Electrospray Ionization Mass Spectrometry

We report a chemical-vapor-assisted electrospray ionization (ESI) technique to improve the detection sensitivity of ESI mass spectrometry (MS). This simple technique involves introducing a chemical vapor into the sheath gas around the nano-ESI spray tip or through a tubing with its outlet placed close to the spray tip. A variety of chemical vapors were tested and found to have varying degrees of effects on analyte signal intensities. The use of benzyl alcohol vapors in ESI was found to increase signal intensities of standard peptides by up to 4-fold. When this technique was combined with capillary liquid chromatography tandem MS (LC-MS/MS), the number of unique peptides identified in the acid hydrolysate of alpha casein increased by 45% and the number of peptides and proteins identified in a tryptic digest of <i>E. coli</i> cell lysate increased by 13% and 14%, respectively, along with an increased average match score. This technique could also increase the analyte signals for some small molecules, such as phenylephrine, by up to 3-fold. The increased analyte signals observed in the chemical-vapor-assisted ESI process is related to the enhancement of the ionization efficiency in ESI. The method can be readily implemented to an existing ESI mass spectrometer at minimum cost for improving detection sensitivity

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Editorial<div><br></div><div>International Research in Early Childhood Education, vol. 7, no. 3, p. 1</div

Dansylation Metabolite Assay: A Simple and Rapid Method for Sample Amount Normalization in Metabolomics

Metabolomics involves the comparison of the metabolomes of individual samples from two or more groups to reveal the metabolic differences. In order to measure the metabolite concentration differences accurately, using the same amount of starting materials is essential. In this work, we describe a simple and rapid method for sample amount normalization. It is based on dansylation labeling of the amine and phenol submetabolome of an individual sample, followed by solvent extraction of the labeled metabolites and ultraviolet (UV) absorbance measurement using a microplate reader. A calibration curve of a mixture of 17 dansyl-labeled amino acid standards is used to determine the total concentration of the labeled metabolites in a sample. According to the measured concentrations of individual samples, the volume of an aliquot taken from each sample is adjusted so that the same sample amount is taken for subsequent metabolome comparison. As an example of applications, this dansylation metabolite assay method is shown to be useful in comparative metabolome analysis of two different E. coli strains using a differential chemical isotope labeling LC-MS platform. Because of the low cost of equipment and reagents and the simple procedure used in the assay, this method can be readily implemented. We envisage that, this assay, which is analogous to the bicinchoninic acid (BCA) protein assay widely used in proteomics, will be applicable to many types of samples for quantitative metabolomics

Nonocclusive Sweat Collection Combined with Chemical Isotope Labeling LC–MS for Human Sweat Metabolomics and Mapping the Sweat Metabolomes at Different Skin Locations

Human sweat is an excellent biofluid candidate for metabolomics due to its noninvasive sample collection and relatively simple matrix. We report a simple and inexpensive method for sweat collection over a defined period (e.g., 24 h) based on the use of a nonocclusive style sweat patch adhered to a skin. This method was combined with differential chemical isotope labeling (CIL) LC–MS for mapping the metabolome profiles of sweat samples collected from skins of the left forearm, lower back, and neck of 20 healthy volunteers. Three 24-h sweat samples were collected at three different days from each subject for examining day-to-day metabolome variations. A total of 342 LC–MS runs were carried out (two runs were discarded due to instrumental issue), resulting in the detection and relative quantification of 3140 sweat metabolites with 84 metabolites identified and 2716 metabolites mass-matched to metabolome databases. Multivariate and univariate analyses of the metabolome data revealed a location-dependence characteristic of the sweat metabolome, offering a possibility of mapping the sweat metabolic differences according to skin locations. Significant differences in male and female sweat metabolomes could be detected, demonstrating the possibility of using the sweat metabolome to reveal biological variations among different comparative groups. Thus, the combination of noninvasive sweat collection and CIL LC–MS is a robust analytical tool for sweat metabolomics with potential applications including daily monitoring of the sweat metabolome as health indicators, discovering sweat-based disease biomarkers, and metabolomic mapping of sweat collected from different areas of skin with and without injuries or diseases

Determination of Total Concentration of Chemically Labeled Metabolites as a Means of Metabolome Sample Normalization and Sample Loading Optimization in Mass Spectrometry-Based Metabolomics

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with ¹²C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with ¹³C-dansylation. Equal amounts of the ¹²C-labeled individual sample and the ¹³C-labeled pooled urine standard were mixed for LC-MS analysis. This way of concentration normalization among different samples with varying concentrations of total metabolites was found to be critical for generating reliable metabolome profiles for comparison

Microwave-Assisted Protein Solubilization for Mass Spectrometry-Based Shotgun Proteome Analysis

Protein solubilization is a key step in mass spectrometry-based shotgun proteome analysis. We describe a microwave-assisted protein solubilization (MAPS) method to dissolve proteins in reagents, such as NH₄HCO₃ and urea, with high efficiency and with an added benefit that the solubilized proteins are denatured to become more susceptible to trypsin digestion, compared to other conventional protein solubilization techniques. In this method, a sample vial containing proteins suspended in a solubilization reagent is placed inside a domestic microwave oven and subjected to microwave irradiation for 30 s, followed by cooling the sample on ice to room temperature (∼40 s) and then intermittent homogenization by vortex for 2 min. This cycle of microwave irradiation, cooling, and homogenization is repeated six times. In this way, sample overheating can be avoided, and a maximum amount of protein can be dissolved. It was shown that in the case of trypsin digestion of bovine serum albumen (BSA) more peptides and higher sequence coverage could be obtained from the protein dissolved by the MAPS method than the conventional heating, sonication, or vortex method. Compared to the most commonly used vortex-assisted protein solubilization method, MAPS reduces the solubilization time significantly, increases the amount of protein dissolvable in a reagent, and increases the number of proteins and peptides identified from a proteome sample. For example, in the proteome analysis of an <i>Escherichia coli</i> K-12 integral membrane protein extract, the MAPS method in combination with sequential protein solubilization and shotgun two-dimensional liquid chromatography tandem mass spectrometry analysis identified a total of 1291 distinct proteins and 10363 peptides, compared to 1057 proteins and 6261 peptides identified using the vortex method. Because MAPS can be done using an inexpensive microwave oven, this method can be readily adopted

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Editoria

Development of Chemical Isotope Labeling LC-MS for Milk Metabolomics: Comprehensive and Quantitative Profiling of the Amine/Phenol Submetabolome

Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (<i>n</i> = 5) of 1:1, 2:1, and 1:2 ¹²C-/¹³C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (<i>n</i> = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication