Neutral loss scan in complement with high‐resolution MS/MS: combination of detection methods for flavonoid and limonoid glycosides analysis

2022 ◽  
Author(s):  
Rui Min Vivian Goh ◽  
Kim Huey Ee ◽  
Aileen Pua ◽  
Yunle Huang ◽  
Shao Quan Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Neutral Loss ◽  
Detection Methods ◽  
Neutral Loss Scan

Detection and identification of Catalpol metabolites in the rat plasma, urine and faeces using ultra-high performance liquid chromatography-Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry

2020 ◽  
Vol 21 ◽  
Author(s):  
Zedong Xiang ◽  
Shaoping Wang ◽  
Haoran Li ◽  
Pingping Dong ◽  
Fan Dong ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Performance ◽  
Mass Measurement ◽  
Neutral Loss ◽  
Rat Plasma ◽  
Accurate Mass ◽  
Chromatographic Retention ◽  
Rat Urine ◽  
Q Exactive

Background:: Catalpol, an iridoid glycoside, is one of the richest bioactive components present in Rehmannia glutinosa. More and more metabolites of drugs have exhibit various pharmacological effects, thus providing guidance for clinical application. However, few researches have paid attention on the metabolism of catalpol. Objective:: This study aimed to establish a rapid and effective method to identify catalpol metabolites and evaluate the biotransformation pathways of catalpol in rats. Methods:: In this study, catalpol metabolites in rat urine, plasma and faeces were analyzed by UHPLC-Q-Exactive MS for the characterization of metabolism of catalpol. Based on high-resolution extracted ion chromatograms (HREICs) and parallel reaction monitoring mode (PRM), metabolites of catalpol were identified by comparing the diagnostic product ions (DPIs), chromatographic retention times, neutral loss fragments (NLFs) and accurate mass measurement with those of catalpol reference standard. Results: A total of 29 catalpol metabolites were detected and identified in both negative and positive ion modes. Nine metabolic reactions including deglycosylation, hydroxylation, dihydroxylation, hydrogenation, dehydrogenation, oxidation of methylene to ketone, glucuronidation, glycine conjugation and cysteine conjugation were proposed. Conclusion:: A rapid and effective method based on UHPLC-Q-Exactive MS was developed to mine the metabolism information of catalpol. Results of metabolites and biotransformation pathways of catalpol suggested that when orally administrated, catalpol was firstly metabolized into catalpol aglycone, after which phase Ⅰ and phase Ⅱ reactions occurred. However, hydrophilic chromatography-mass spectrometry still needed to further find the polar metabolites of catalpol.

Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

2021 ◽  
Author(s):  
Rick Schrynemeeckers
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Time Lapse ◽  
Field Trials ◽  
Detection Methods ◽  
Ocean Bottom ◽  
Field Development ◽  
Dense Grid ◽  
Hydrocarbon Charge ◽  
Sweet Spots

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

Objective 3D atmospheric front detection in high-resolution numerical weather prediction data

2021 ◽  
Author(s):  
Andreas Beckert ◽  
Lea Eisenstein ◽  
Tim Hewson ◽  
George C. Craig ◽  
Marc Rautenhaus
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Feature Detection ◽  
3D Visualization ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Detection Methods ◽  
Numerical Weather ◽  
Atmospheric Fronts ◽  
Front Detection

<p><span>Atmospheric fronts, a widely used conceptual model in meteorology, describe sharp boundaries between two air masses of different thermal properties. In the mid-latitudes, these sharp boundaries are commonly associated with extratropical cyclones. The passage of a frontal system is accompanied by significant weather changes, and therefore fronts are of particular interest in weather forecasting. Over the past decades, several two-dimensional, horizontal feature detection methods to objectively identify atmospheric fronts in numerical weather prediction (NWP) data were proposed in the literature (e.g. Hewson, Met.Apps. 1998). In addition, recent research (Kern et al., IEEE Trans. Visual. Comput. Graphics, 2019) has shown the feasibility of detecting atmospheric fronts as three-dimensional surfaces representing the full 3D frontal structure. In our work, we build on the studies by Hewson (1998) and Kern et al. (2019) to make front detection usable for forecasting purposes in an interactive 3D visualization environment. We consider the following aspects: (a) As NWP models evolved in recent years to resolve atmospheric processes on scales far smaller than the scale of midlatitude-cyclone- fronts, we evaluate whether previously developed detection methods are still capable to detect fronts in current high-resolution NWP data. (b) We present integration of our implementation into the open-source “Met.3D” software (http://met3d.wavestoweather.de) and analyze two- and three-dimensional frontal structures in selected cases of European winter storms, comparing different models and model resolution. (c) The considered front detection methods rely on threshold parameters, which mostly refer to the magnitude of the thermal gradient within the adjacent frontal zone - the frontal strength. If the frontal strength exceeds the threshold, a so-called feature candidate is classified as a front, while others are discarded. If a single, fixed, threshold is used, unwanted “holes” can be observed in the detected fronts. Hence, we use transparency mapping with fuzzy thresholds to generate continuous frontal features. We pay particular attention to the adjustment of filter thresholds and evaluate the dependence of thresholds and resolution of the underlying data.</span></p>

scholarly journals ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2671 ◽  
Author(s):  
Chunsheng Liu ◽  
Yu Guo ◽  
Shuang Li ◽  
Faliang Chang
Keyword(s):  
High Resolution ◽  
High Precision ◽  
Extraction Method ◽  
High Performance ◽  
Detection Methods ◽  
Average Precision ◽  
Deep Network ◽  
High Resolution Images ◽  
Bounding Boxes ◽  
Detection And Localization

You Only Look Once (YOLO) deep network can detect objects quickly with high precision and has been successfully applied in many detection problems. The main shortcoming of YOLO network is that YOLO network usually cannot achieve high precision when dealing with small-size object detection in high resolution images. To overcome this problem, we propose an effective region proposal extraction method for YOLO network to constitute an entire detection structure named ACF-PR-YOLO, and take the cyclist detection problem to show our methods. Instead of directly using the generated region proposals for classification or regression like most region proposal methods do, we generate large-size potential regions containing objects for the following deep network. The proposed ACF-PR-YOLO structure includes three main parts. Firstly, a region proposal extraction method based on aggregated channel feature (ACF) is proposed, called ACF based region proposal (ACF-PR) method. In ACF-PR, ACF is firstly utilized to fast extract candidates and then a bounding boxes merging and extending method is designed to merge the bounding boxes into correct region proposals for the following YOLO net. Secondly, we design suitable YOLO net for fine detection in the region proposals generated by ACF-PR. Lastly, we design a post-processing step, in which the results of YOLO net are mapped into the original image outputting the detection and localization results. Experiments performed on the Tsinghua-Daimler Cyclist Benchmark with high resolution images and complex scenes show that the proposed method outperforms the other tested representative detection methods in average precision, and that it outperforms YOLOv3 by 13.69 % average precision and outperforms SSD by 25.27 % average precision.

scholarly journals Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1812 ◽  
Author(s):  
Jonathan P. Dash ◽  
Michael S. Watt ◽  
Thomas S. H. Paul ◽  
Justin Morgenroth ◽  
Grant D. Pearse
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Validation Dataset ◽  
Height Range ◽  
Exotic Trees ◽  
Invasive Trees ◽  
Invasive Exotic

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

scholarly journals Suspect screening and targeted analysis of acyl coenzyme A thioesters in bacterial cultures using a high-resolution tribrid mass spectrometer

2021 ◽  
Author(s):  
Nevenka Cakić ◽  
Bernd Kopke ◽  
Ralf Rabus ◽  
Heinz Wilkes
Keyword(s):  
High Resolution ◽  
Mass Spectrometer ◽  
Coenzyme A ◽  
Neutral Loss ◽  
Degradation Pathway ◽  
Suspect Screening ◽  
Bacterial Cultures ◽  
Targeted Analysis ◽  
Wide Range ◽  
Acyl Coenzyme A

AbstractAnalysis of acyl coenzyme A thioesters (acyl-CoAs) is crucial in the investigation of a wide range of biochemical reactions and paves the way to fully understand the concerned metabolic pathways and their superimposed networks. We developed two methods for suspect screening of acyl-CoAs in bacterial cultures using a high-resolution Orbitrap Fusion tribrid mass spectrometer. The methods rely on specific fragmentation patterns of the target compounds, which originate from the coenzyme A moiety. They make use of the formation of the adenosine 3′,5′-diphosphate key fragment (m/z 428.0365) and the neutral loss of the adenosine 3′-phosphate-5′-diphosphate moiety (506.9952) as preselection criteria for the detection of acyl-CoAs. These characteristic ions are generated either by an optimised in-source fragmentation in a full scan Orbitrap measurement or by optimised HCD fragmentation. Additionally, five different filters are included in the design of method. Finally, data-dependent MS/MS experiments on specifically preselected precursor ions are performed. The utility of the methods is demonstrated by analysing cultures of the denitrifying betaproteobacterium “Aromatoleum” sp. strain HxN1 anaerobically grown with hexanoate. We detected 35 acyl-CoAs in total and identified 24 of them by comparison with reference standards, including all 9 acyl-CoA intermediates expected to occur in the degradation pathway of hexanoate. The identification of additional acyl-CoAs provides insight into further metabolic processes occurring in this bacterium. The sensitivity of the method described allows detecting acyl-CoAs present in biological samples in highly variable abundances.

Identification of Novel Recurrent Copy Number Variations and Regions of Copy-Neutral Loss of Heterozygosity by High Resolution Genomic Array in Pre-Treatment and Relapsed B-CLL.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1098-1098
Author(s):  
Samantha JL Knight ◽  
Elham Sadighi Akha ◽  
Adele Timbs ◽  
Tariq Enver ◽  
Andrew R Pettitt ◽  
...  
Keyword(s):  
High Resolution ◽  
Copy Number Variation ◽  
B Cell ◽  
Genomic Instability ◽  
Loss Of Heterozygosity ◽  
Copy Number ◽  
Neutral Loss ◽  
Single Gene ◽  
Number Variation ◽  
Pre Treatment

Abstract Abstract 1098 Poster Board I-120 Background B-cell chronic lymphocytic leukaemia (B-CLL) is the most common form of adult leukaemia in the Western World. It is a heterogeneous disease and important biological and clinical differences have been identified. However, the molecular mechanisms underlying emergence and maintenance of B-CLL after treatment remain elusive. Array based comparative genomic hybridization (aCGH) has revolutionized our ability to perform genome wide analyses of copy number variation (CNV) within cancer genomes. Single Nucleotide Polymorphism arrays (aSNP) provide genotyping and copy number variation data and detect regions of copy neutral Loss of Heterozygosity (cnLOH) with the potential to indicate genes involved in leukaemia pathogenesis. Both technologies are evolving rapidly and emerging platforms are thought to allow high resolution (HR) of abnormalities down to a single gene level. Aim The aim of the current study was therefore to test a HR-aCGH and a HR-aSNP platform for their ability to detect large and small CNVs and regions of cnLOH in B-CLL. More specifically, we wanted to: Method We used a high resolution 244K aCGH platform and a 1Mio SNP array in parallel to test and characterize enriched B-CLL peripheral blood samples (>80% CD19+;CD5+) from 44 clinically annotated patients collected at our institution. To distinguish CNVs seen commonly in the general population the results were compared with ‘in house’ control data sets and the Database of Genomic Variants (http://projects.tcag.ca/variation/). Results Our results show that large abnormalities, already noted by FISH, were reliably identified and the boundaries of abnormalities at 11q22.3, 13q14.2 and 17p could be defined more precisely. In addition, novel and recurrent CNVs within the sample set were identified (1p33; 3p24.3; 3p14.2; 4q12; 4q13.3; 6q21; 6q27; 8p22; 10q24; 11p15.4; 11q12; 11q13.4; 11q14.1; 11q22.1; 11q23.3; 13q14.11; 14q21.1; 15q15.1; 15q25.3; 17p13.3; 17q22; 18p11.32; 18p23; 19p13.13; 19p13.12; 19p13.32; 22q11.21; 22q11.22). Interestingly, some of these abnormalities contain single gene alterations involving oncogenes, chemokine receptors, kinases and transcription factors important in B cell development and differentiation. Assessment of smaller CNVs (less then 10 consecutive oligonucleotides) also revealed recurrent CNVs involving single genes that were clustered according to function and pathways. Comparison of paired pre-treatment and relapse samples showed differences in large CNVs in 6 out of the 14 pairs with the majority being losses within the relapse sample. In particular, relapse samples contained new losses within 2q33.1-2q37.1; 4q13.2-4q13.3; 5q31.3-5q34; 7q36.3; 10q23.1-10q25.1 11q12.3 and multiple losses within 13q14.1-13q14.3. Taken together, these data indicates that genomic instability plays a role in clonal evolution and selection after treatment in at least some patients. Analysis of a bigger cohort of matched pre-treatment and relapse samples is on-going. The importance of copy neutral LOH in B-CLL has been a subject of debate. Using the 1Mio HR-aSNP, we were able to detect multiple regions of cnLOH throughout the genome. Examination of the four regions that are known to have prognostic significance when deleted identified cnLOH involving 13q11-13q34(ter) and cnLOH of 13q21.1-q34(ter) outside the FISH region. Deletions of the 17p13.1 locus including the p53 gene confer poor prognosis in B-CLL and direct treatment decisions. Interestingly, we were able to identify cnLOH involving this region in 5% of samples. In addition, we also noticed cnLOH in 17p13.2 containing genes previously implicated in cancer. The exact pathogenetic and prognostic implications of these findings remain to be established. Conclusion Using HR-aCGH and HR-aSNP we have identified novel recurrent CNVs and regions of cnLOH in patients with B-CLL. Sequential analysis of the same patients over time suggests that at least in some patients, clonal complexity and dynamics are driven by genomic instability. Disclosures No relevant conflicts of interest to declare.

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