Acquisition Mode Research Articles

The challenging patient-recommendations and solutions

The continuous technical development of cardiac computed tomography (CT) over the last decades has led to an improvement in image quality and diagnostic accuracy, while simultaneously reducing radiation exposure. Despite these advancements, certain patient-related factors remain achallenge to conduct a high-quality diagnostic examination. What factors can negatively affect the image quality of cardiac CT and how can these be addressed? Analysis of the available literature on cardiac CT and identification of the quality-limiting factors, discussion, and possible solutions. Tachycardia, arrhythmias, high coronary calcification, the presence of stents and coronary artery bypasses, as well as obesity and anxiety were identified as primary factors that limit image quality and diagnostic accuracy. These issues primarily arise from alack of response or the presence of contraindications to premedication, blooming artifacts, variations in postoperative anatomy, as well as other personal factors. Suggested solutions include optimizing premedication, scanner modifications, the selection of the most suitable acquisition mode, new scanner technologies, and innovative image reconstruction methods including artificial intelligence. Certain factors continue to pose amajor challenge for cardiac CT. Knowledge of alternative premedication, scanner modifications, as well as the use of postprocessing software and new technologies can help overcome these limitations, enabling successful and safe cardiac CTs even in challenging patients.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. Total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

Pharmaceutical metabolite identification in lettuce (Lactuca sativa) and earthworms (Eisenia fetida) using liquid chromatography coupled to high-resolution mass spectrometry and in silico spectral library.

Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and anin silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.

Validity and specificity of BOLD effects and their correction in 1H-fMRS.

This study aimed to characterize blood oxygen level-dependent (BOLD) effects in proton magnetic resonance (1H-MR) spectra obtained during optogenetic activation of the rat forelimb cortex to correct and estimate the accurate changes in metabolite concentration. For a more comprehensive understanding of BOLD effects detected with functional magnetic resonance spectroscopy (fMRS) and to optimize the correction method, a 1 Hz line-narrowing effect was simulated. Then, proton functional magnetic resonance spectroscopy (1H-fMRS) data acquired using stimulated echo acquisition mode (STEAM) at 9.4T in rats (n = 8) upon optogenetic stimulation of the primary somatosensory cortex were utilized. The data were analyzed using MATLAB routines and LCModel. Uncorrected and corrected 1H-MR spectra from the simulated and in vivo data were quantified and compared. BOLD-corrected difference spectra were also calculated and analyzed. Additionally, the effects of stimulated and non-stimulated water on the quantification of metabolite concentration swere investigated. Significant mean increases in water and N-acetylaspartate (NAA) peak heights (+1.1% and +4.5%, respectively) were found to be accompanied by decreased linewidths (-0.5 Hz and -2.8%) upon optogenetic stimulation. These estimates were used for further defining an accurate line-broadening (lb) factor. The usage of a non-data-driven lb introduced false-positive errors in the metabolite concentration change estimates, thereby altering the specificity of the findings. The water and metabolite BOLD contributions were separated using different water scalings within LCModel. The linewidth-matching procedure using a precise lb factor remains the most effective approach for accurately quantifying small (±0.3 μmol/g) metabolic changes in 1H-fMRS studies. A simple and preliminary compartmentation of BOLD effects was proposed, but it will require validation.

Sample-Specific MS/MS Methods in High-Throughput Mass Spectrometry.

The drug discovery process increasingly relies on high-throughput sample analysis to accelerate the identification of viable drug candidates. Recently, chromatographic-free high-throughput mass spectrometry (HT-MS) technologies have emerged, significantly increasing sample readout speed and enabling the analysis of large sample sets. These HT-MS platforms continuously acquire data from various samples into a single data file, presenting challenges in applying distinctive data acquisition methods to specific samples. This study introduces a novel approach that integrates real-time sample loading status to activate sample-specific MS/MS data acquisition methods on the high-throughput acoustic ejection mass spectrometry platform. Effective method switching and high signal reproducibility were demonstrated across different data acquisition window durations in multiple reaction monitoring (MRM), high-resolution MRM (MRMHR), and information-dependent acquisition modes. This advancement provides a user-friendly and robust solution to the method-setting challenges of HT-MS, expanding the implementation of HT-MS platforms in drug discovery and other high-throughput analytical applications.

Exploring methandienone metabolites generated via homogenized camel liver: Advancements for anti-doping applications through High Resolution-Liquid Chromatography Mass Spectrometry analysis.

Anabolic steroids, also known as anabolic-androgenic steroids (AAS), encompass steroidal androgens such as testosterone, as well as synthetic counterparts with similar structures and effects. The misuse of AAS has increased over the years, leading to ethical and welfare concerns in sports. The World Anti-Doping Agency (WADA) and the International Federation for Equestrian Sports (FEI) have banned AAS in relevant sports. Methandienone is one of the most identified anabolic androgenic steroids in sports drug testing, Therefore, reliable detection methods are crucial for effective doping control and maintaining the integrity of the sports. This study explores the use of homogenized camel liver for detecting methandienone metabolites in camels. The biotransformation pathways of methandienone in homogenized camel liver tissues are analyzed using Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) to identify and characterize the phase I and phase II metabolites. Chromatographic separation was achieved using a Thermo-Hypersil C18 column. The study has identified 11 methandienone metabolites (M1-M11), this includes 10 phase I and one phase II metabolite. A glucuronic acid conjugate of methandienone was observed in this study, but no sulfonic acid conjugations were found. The metabolites and their possible chemical structures, along with their fragmentation patterns are confirmed using MSMS (MS2) experiments in data-independent acquisition (DIA) mode. These findings serve as a vital tool for the rapid detection of methandienone, combating its illicit use in camel racing. Comprehensive screenings covering both the parent drug and its metabolites are recommended to improve detection accuracy and ensure regulatory compliance in sports doping. Future research should explore methandienone's metabolite profile in administered camel samples.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Integration of deep neutral network modeling and LC-MS-based pseudo-targeted metabolomics to discriminate easily confused ginseng species

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus and P. japonicus var. major, containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM, and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study.

Albuminuria may precede decreases in the glomerular filtration rate (GFR) and both tests are insensitive predictors of early stages of kidney disease. Our aim was to characterise the urinary proteome in black African individuals with albuminuria and well-preserved GFR from South Africa. This case-controlled study compared the urinary proteomes of 52 normoalbuminuric (urine albumin: creatinine ratio (uACR) < 3 mg/mmol) and 56 albuminuric (uACR ≥ 3 mg/mmol) adults of black African ethnicity. Urine proteins were precipitated, reduced, alkylated, digested, and analysed using an Evosep One LC (Evosep Biosystems, Odense, Denmark) coupled to a Sciex 5600 Triple-TOF (Sciex, Framingham, MA, USA) in data-independent acquisition mode. The data were searched on SpectronautTM 15. Differentially abundant proteins (DAPs) were filtered to include those with a ≥2.25-fold change and a false discovery rate ≤ 1%. Receiver-operating characteristic curves were used to assess the discriminating abilities of proteins of interest. Pathway analysis was performed using Enrichr software. As expected, the albuminuric group had higher uACR (7.9 vs. 0.55 mg/mmol, p < 0.001). The median eGFR (mL/min/1.73 m2) showed no difference between the groups (111 vs. 114, p = 0.707). We identified 80 DAPs in the albuminuria group compared to the normoalbuminuria group, of which 59 proteins were increased while 21 proteins were decreased in abundance. We found 12 urinary proteins with an AUC > 0.8 and a p < 0.001 in the multivariate analysis. Furthermore, an 80-protein model was developed that showed a high AUC ˃ 0.907 and a predictive accuracy of 91.3% between the two groups. Pathway analysis found that the DAPs were involved in insulin growth factor (IGF) functions, innate immunity, platelet degranulation, and extracellular matrix organization. In albuminuric individuals with a well-preserved eGFR, pathways involved in preventing the release and uptake of IGF by insulin growth factor binding protein were significantly enriched. These proteins are indicative of a homeostatic imbalance in a variety of cellular processes underlying renal dysfunction and are implicated in chronic kidney disease.

Capillary zone electrophoresis-tandem mass spectrometry for in-depth proteomics analysis via data-independent acquisition.

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study.

An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues.

This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.

The rental‐function effect: How does product acquisition mode affect consumer preference relating to product attributes?

AbstractPurchasing and renting are two basic modes for consumers to acquire a product. While prior research has investigated which factors affect consumers' choice of renting versus purchasing a product, very limited research studies the outcome of renting versus purchasing a product. Extending this line of research, this paper links product acquisition mode to product attribute preference. Specifically, through a series of eight multimethod studies, including secondary data study, field experiment, and lab experiments, we show that renting a product, as compared to purchasing a product, increases consumers' focus on the product's feasibility aspect rather than its desirability aspect, which in turn drives consumers to prefer the product option with superior function over the one with superior appearance. Furthermore, we show that this rental‐function effect amplifies (attenuates) when the product's brand image is positioned as a mass brand (a luxury brand). This paper uncovers a novel finding of renting products. It also provides practical guidelines for rental companies and marketing managers on how to effectively position their products and optimize advertising strategies.

Potential antioxidant and cytotoxic impacts of defatted extract rich in flavonoids from Styphnolobium japonicum leaves growing in Egypt

Styphnolobium japonicum leaves are considered a rich source of flavonoids, which are the prospective basis for various therapeutic effects. However, there has been a lack of comprehensive cytotoxic studies conducted on these leaves. Therefore, this ongoing investigation aimed to detect and isolate the flavonoids present in S. japonicum leaves, and assess their antioxidant and anticancer properties. The defatted extract from S. japonicum leaves was analyzed using HPLC, which resulted in the identification of seven phenolics and six flavonoids. Rutin and quercetin were found to be the most abundant. Furthermore, a comprehensive profile of flavonoids was obtained through UPLC/ESI–MS analysis in negative acquisition mode. Fragmentation pathways of the identified flavonoids were elucidated to gain relevant insights into their structural characteristics. Furthermore, genistein 7-O-glucoside, quercetin 3-O-rutinoside, and kaempferol 3-O-α-L-rhamnopyranosyl-(1 → 6)-β-D-glucopyranosyl-(1 → 2)-β-D-glucopyranoside were isolated and characterized. The defatted extract rich in flavonoids exhibited significant antioxidant, iron-reducing, free radicals scavenging impacts, and remarkable cytotoxicity against the liver cell line (IC50 337.9μg/ mL) and lung cell line (IC50 55.0 μg/mL). Furthermore, the antioxidant and anticancer capacities of the three isolated flavonoids have been evaluated, and it has been observed that their effects are concentration-dependent. The findings of this research highlight the promising impact of flavonoids in cancer therapy. It is recommended that future scientific investigations prioritize the exploration of the distinct protective and therapeutic characteristics of S. japonicum leaves, which hold significant potential as a valuable natural resource.

Best practices towards the digitization of 3D traces from virtual outcrop models

With the expanding application of virtual outcrop models (VOMs) within the geosciences, there is a growing need to establish standards for the digital measurement of geological discontinuities exposed therein. Such standards should be tailored towards the complexities of natural outcrops, where geological discontinuities often intersect the outcrop topography and are expressed as 3D traces. Digitizing geological discontinuities expressed as trace data is typically conducted manually via polyline interpolation along the exposed trace, with discontinuity orientation estimated through planar model fitting through the polyline's component nodes. Presently, establishing quality control for such measurements lacks standardization due to the absence of robust benchmarks, with the validity of the resultant orientation data heavily reliant on the experience of the interpreter.With the aim of bridging this gap, we present the results of the manual digitization and orientation estimation of bedding planes expressed as traces across seven natural outcrops. We use two digitization strategies: one employing a previewed best-fit plane during digitization and another without. The first digitization method is carried out by an expert user who visually filters data according to visual alignment with the intended bedding prior to best fit plane estimation. In contrast, the non-visually aided method mimics acquisition by a novice user, with no a priori data filtering based upon trace geometry with respect to the outcrop. Comparison of the results obtained by these ‘expert’ and ‘novice’ acquisition modes is aimed at building benchmarks and best practices. Specifically, we analyze parameters derived from the digitized traces and their corresponding best-fit planes. We compare these parameters with the deviation of the best-fit plane from the mean orientation of the bedding surface as measured using visually-aided acquisition. Comparing these datasets reveals that visually-aided digitization yields more precise and accurate bedding measurements, characterized by traces with lower vertex collinearity. Notably, comparable results can be achieved in the non-visually assisted dataset by excluding traces with high node collinearity. Consequently, we provide robust benchmarks for trace collinearity and its relationship to best fit plane quality to aid the practical implementation of the results of this study. Furthermore, we supplement quantitative comparative analysis with recommended best practices for 3D trace digitization, such as ensuring high values of coplanarity, maintaining a quasi-constant node-to-node distance relative to the model's resolution, and ensuring a minimum number of nodes to guarantee the robustness of the fitted planar model. Critically, our study highlights the critical role tacit geological knowledge plays in the robustness of 3D trace digitization from virtual outcrop models.

Optimization of three-dimensional electron diffuse scattering data acquisition

The diffraction patterns of crystalline materials with local order contain sharp Bragg reflections as well as highly structured diffuse scattering. In this study, we quantitatively show how the diffuse scattering in three-dimensional electron diffraction (3D ED) data is influenced by various parameters, including the data acquisition mode, the detector type and the use of an energy filter. We found that diffuse scattering data used for quantitative analysis are preferably acquired in selected area electron diffraction (SAED) mode using a CCD and an energy filter. In this study, we also show that the diffuse scattering in 3D ED data can be obtained with a quality comparable to that from single-crystal X-ray diffraction. As electron diffraction requires much smaller crystal sizes than X-ray diffraction, this opens up the possibility to investigate the local structure of many technologically relevant materials for which no crystals large enough for single-crystal X-ray diffraction are available.

Evaluation of gingival thickness obtained from intraoral scanning registration with cone beam computed tomography at different acquisition modes

To investigate the reliability of gingival thickness measurements obtained from the registration of intraoral scans with cone beam computed tomography (CBCT) examinations at different acquisition modes. CBCT examinations of 9 porcine hemimandibles were acquired using the OP300 Maxio unit operating at the highest-dose protocol with the smallest voxel size as the reference standard for visualizing the gingival surface. Subsequently, the hemimandibles were surrounded by water to simulate soft tissue attenuation of radiation, and additional CBCT examinations were acquired in 4 modes: Endo, High-resolution, Standard-resolution, and Low-dose. These CBCT datasets were registered with corresponding intraoral scans obtained with the Carestream Dental 3600 intraoral scanning system using the Blue Sky Plan 4 software. Four oral radiologists measured the buccal gingival thickness on cross-sectional reconstructions at 4 measurement sites and 2 distances from the gingival margin in the CBCT reference standard examinations and examinations obtained with water and intraoral scan registration. Multifactorial analysis of variance was used to assess the influence of acquisition mode, measurement site, and distance from the gingival margin on measurements (α = 5%; statistical power = 90%). Buccal gingival thickness measurements obtained via CBCT after registration with intraoral scanning were not significantly influenced by acquisition mode (P = .153) or measurement site (P = .089). Gingival thickness measurements derived from the registration of intraoral scans with CBCT examinations at different acquisition modes appear to be reliable.

Solutions to enhance the accuracy of UAV’s images for creating extremely large-scale maps in urban area

Abstract Ho Chi Minh City, the largest urban agglomeration in Vietnam, is at the forefront of enabling the government’s efforts and advocating for smart city initiatives in the coming decades. To facilitate this advancement, the implementation of solutions to support the collection, construction, and updating of spatial databases is crucial. These databases serve as the foundational platform for effective urban management practices. Unmanned Aerial Vehicles (UAVs) have witnessed significant growth across various domains, particularly in spatial data collection. However, challenges arise when acquiring and processing UAV images in urban areas. These challenges include orthomosaic distortion in certain regions, insufficient accuracy for creating extremely large-scale maps (e.g., 1:200 or 1:500), and unclear boundaries between adjacent objects. The objective of this paper is to present flight tests conducted at varying altitudes, with different shooting angles, and varying layouts of Ground Control Points (GCPs), both with and without the UAV’s RTK receiver mode. In order to improve the accuracy of UAV’s images, the results suggest that the precision of orthomosaic, Digital Surface Model (DSM) and point clouds in the context of 3D mapping depends on various factors, including: the determination of the optimal flight altitude, optimizing number of GCPs, selecting an appropriate level of image processing, capturing images from varied angles, and using UAVs with RTK or PPK data acquisition modes to mitigate image distortion.

Spexwavepy: an open-source Python package for X-ray wavefront sensing using speckle-based techniques.

In situ wavefront sensing plays a critical role in the delivery of high-quality beams for X-ray experiments. X-ray speckle-based techniques stand out among other in situ techniques for their easy experimental setup and various data acquisition modes. Although X-ray speckle-based techniques have been under development for more than a decade, there are still no user-friendly software packages for new researchers to begin with. Here, we present an open-source Python package, spexwavepy, for X-ray wavefront sensing using speckle-based techniques. This Python package covers a variety of X-ray speckle-based techniques, provides plenty of examples with real experimental data and offers detailed online documentation for users. We hope it can help new researchers learn and apply the speckle-based techniques for X-ray wavefront sensing to synchrotron radiation and X-ray free-electron laser beamlines.

A tool for estimating ground-based InSAR acquisition characteristics prior to monitoring installation and survey and its differences from satellite InSAR

Abstract. Synthetic Aperture Radar (SAR) acquisition can be performed from satellites or from the ground by means of a so-called GB-InSAR (Ground-Based Interferometry SAR), but the signal emission and the output image geometry slightly differ between the two acquisition modes. Those differences are rarely mentioned in the literature. This paper proposes to compare satellite and GB-InSAR in terms of (1) acquisition characteristics and parameters to consider; (2) SAR image resolution; and (3) geometric distortions that are foreshortening, layover, and shadowing. If in the case of satellite SAR, the range and azimuth resolutions are known and constant along the orbit path, in the case of GB-InSAR their values are terrain-dependent. It is worth estimating the results of a GB-InSAR acquisition that one can expect in terms of range and azimuth resolution, line of sight (LoS) distance, and geometric distortions to select the best installation location when several are possible. We developed a novel tool which estimates those parameters from a digital elevation model (DEM), knowing the GB-InSAR and the slope of interest (SoI) coordinates. This tool, written in MATLAB, was tested on a simple synthetic point cloud representing a cliff with a progressive slope angle to highlight the influence of the SoI geometry on the acquisition characteristics and on two real cases of cliffs located in Switzerland, namely one in the Ticino canton and one in the Vaud canton.

Determination of bioactive substances in 27 edible flowers based on LC-MS/MS

Edible flowers, as a food ingredient, are rich in bioactive substance and have received a lot of attention. In this study, we have established a pseudo-targeting method based on Multi Reaction Monitoring (MRM) - Enhanced Product Ions (EPI) in the Information Dependent Acquisition (IDA) mode. The results showed that a total of 43 metabolites, including flavonoids, phenolic acids, and terpenes were detected in 27 edible flowers using integrated liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy. Rutin was measured in all edible flowers, but the levels varied significantly among the different varieties of flowers, with the highest being about 3500 times the lowest. Albiflorin and paeoniflorin are polyphenolic substance unique to Paeonia suffruticosa. The cyanidin 3-O-glucoside chloride content in reddish-purple flowers is higher than that of other colors. Dicoumarin and 4-hydroxycoumarin were discovered in edible flowers for the first time. This study established a pseudo-targeted detection method to reveal the distribution of polyphenolic metabolites and enriched the research on polyphenolic metabolites in edible flowers. It can also serve as a general strategy to detect metabolites in other agricultural products, providing valuable information about potential metabolites for consumers.