Acquisition Techniques Research Articles

Systems Engineering Approach Towards Sensitive Cellular Fluorine-19 MRI.

In vivo fluorine-19 MRI using F-based tracer media has shown utility and versatility for a wide range of biomedical uses, particularly immune and stem cell detection, as well as biosensing. As with many advanced MRI acquisition techniques, the sensitivity and limit of detection (LOD) invivo is a key consideration for a successful study outcome. In this review, we analyze the primary factors that limit cell LOD. The achievable sensitivity is strongly dependent on the specific composition of tracer, cell type of interest, cell activity, data acquisition and reconstruction methods, and MRI hardware design. Recent innovations in molecular 19F tracer design and image acquisition-reconstruction methods have achieved significant leaps in 19F MRI sensitivity, and integration of these new materials and methods into studies can result in > 10-fold improvement in LOD. These developments will help unlock the full potential of clinical 19F MRI for biomedical applications.

Endoscopic Ultrasound-Guided Pancreatic Tissue Sampling: Lesion Assessment, Needles, and Techniques

Endoscopic ultrasound (EUS)-guided tissue sampling includes the techniques of fine needle aspiration (FNA) and fine needle biopsy (FNB), and both procedures have revolutionized specimen collection from the gastrointestinal tract, especially from remote/inaccessible organs. EUS-FNB has replaced FNA as the procedure of choice for tissue acquisition in solid pancreatic lesions (SPLs) across various society guidelines. FNB specimens provide a larger histological tissue core (preserving tissue architecture) with fewer needle passes, and this is extremely relevant in today’s era of precision and personalized molecular medicine. Innovations in needle tip design are constantly under development to maximize diagnostic accuracy by enhancing histological sampling capabilities. But, apart from the basic framework of the needle, various other factors play a role that influence diagnostic outcomes, namely, sampling techniques (fanning, aspiration or suction, and number of passes), collection methods, on-site evaluation (rapid, macroscopic, or visual), and specimen processing. The choice taken depends strongly on the endoscopist’s preference, available resources at the disposal, and procedure objectives. Hence, in this review, we explicate in detail the concepts and available literature at our disposal on the topic of EUS-guided pancreatic tissue sampling to best guide any practicing gastroenterologist/endoscopist in a not-to-ideal set-up, which EUS-guided tissue acquisition technique is the “best” for their case to augment their diagnostic outcomes.

Reproducibility of intravoxel incoherent motion quantification in the liver across field strengths and gradient hardware.

To evaluate reproducibility and interlobar agreement of intravoxel incoherent motion (IVIM) quantification in the liver across field strengths and MR scanners with different gradient hardware. Cramer-Rao lower bound optimization was performed to determine optimized monopolar and motion-robust 2D (b-value and first-order motion moment [M1]) IVIM-DWI acquisitions. Eleven healthy volunteers underwent diffusion MRI of the liver, where each optimized acquisition was obtained five times across three MRI scanners. For each data set, IVIM estimates (diffusion coefficient (D), pseudo-diffusion coefficients ( and ), blood velocity SDs (Vb1 and Vb2), and perfusion fractions [f1 and f2]) were obtained in the right and left liver lobes using two signal models (pseudo-diffusion and M1-dependent physical) with and without T2 correction (fc1 and fc2) and three fitting techniques (tri-exponential region of interest-based full and segmented fitting and blood velocity SD distribution fitting). Reproducibility and interlobar agreement were compared across methods using within-subject and pairwise coefficients of variation (CVw and CVp), paired sample t-tests, and Bland-Altman analysis. Using a combination of motion-robust 2D (b-M1) data acquisition, M1-dependent physical signal modeling with T2 correction, and blood velocity SD distribution fitting, multiscanner reproducibility with median CVw = 5.09%, 11.3%, 9.20%, 14.2%, and 12.6% for D, Vb1, Vb2, fc1, and fc2, respectively, and interlobar agreement with CVp = 8.14%, 11.9%, 8.50%, 49.9%, and 42.0%, respectively, was achieved. Recently proposed advanced IVIM acquisition, signal modeling, and fitting techniques may facilitate reproducible IVIM quantification in the liver, as needed for establishment of IVIM-based quantitative biomarkers for detection, staging, and treatment monitoring of diseases.

Reproducibility of proton density fat fraction assessment of thigh muscle in a multi-site, multi-vendor cohort study at 10 years after anterior cruciate ligament reconstruction.

Dixon-based magnetic resonance imaging (MRI) intramuscular proton density fat fraction (PDFF) is a potentially useful imaging biomarker of muscle quality. However, multi-vendor, multi-site reproducibility of intramuscular PDFF quantification, required for large clinical studies, can be strongly dependent on acquisition and processing. The purpose of this study was (I) to develop a 6-point Dixon MRI-based acquisition and processing technique for reproducible multi-vendor, multi-site quantification of thigh intramuscular PDFF; and (II) to evaluate the ability of the technique to detect differences in thigh muscle status between operated vs. non-operated limbs in a multi-site study of patients scanned at 10 years after anterior cruciate ligament reconstruction (ACLR). MRI bilateral mid-thigh data acquisition at 3T was harmonized across three sites and two vendors and included high-resolution axial T1-weighted scans and 6-point Dixon scans. Centralized, vendor-independent PDFF quantification was performed and algorithms were evaluated in phantoms to determine the most reproducible approach. A novel image post-processing method was developed to mitigate scaling errors observed on some scanner platforms to improve reproducibility. PDFF measurements in phantoms and control subjects including traveling controls were obtained for assessment of intra-scanner repeatability as well as inter-scanner, inter-vendor, and inter-site reproducibility. Patients from the Multicenter Orthopedic Outcomes Network ACLR cohort were scanned and intramuscular PDFF was compared between thigh muscles of the operated and contralateral limbs. Standard deviation (SD) of PDFF, within-subject SD (wSD), and intraclass correlation coefficient (ICC) were used to characterize repeatability and reproducibility. The proposed scaling correction method improved overall reproducibility in phantoms and traveling controls and was incorporated as part of the Dixon processing pipeline for subsequent analyses. Intra-scanner phantom repeatability ranged between 0.2-0.9% (SD) PDFF (ICC =0.98-1.00), with overall inter-vendor/inter-site reproducibility of 0.7-1.7% (SD) PDFF (ICC =0.97). Control subject repeatability among all scanners and vendors ranged between 0.2-0.8% (wSD) PDFF (ICC =0.95-0.98) with slightly lower inter-site, inter-vendor reproducibility, 0.8-1.2% (wSD) PDFF (ICC =0.92). Intramuscular PDFF was elevated in ACLR vs contralateral thighs for the hamstrings muscle compartment (6.2%±3.5% vs. 5.7%±2.8%, P=0.006), while quadriceps (4.0%±2.0% vs. 4.0%±2.1%, P=0.961) and medial (5.7%±2.5% vs. 5.5%±2.3%, P=0.133) muscle compartments did not show significant PDFF differences. Reproducible multi-site, multi-vendor intramuscular PDFF measurement is enabled by 6-point Dixon MRI with standardized acquisition and processing. The method is sensitive enough to detect differences in muscle groups between operated and non-operated thighs in the patient population 10 years after ACLR.

An automatic 3D tomato plant stemwork phenotyping pipeline at internode level based on tree quantitative structural modelling algorithm

Phenotypic traits of stemwork are important indicators of plant growing status, contributing to multiple research domains including yield estimation, breeding engineering, and disease control. Traditional plant phenotyping with human work faces serious bottlenecks on labour intensity and time consumption. In recent years, the application of Quantitative Structural Modeling (QSM) together with three-dimensional (3D) sensor-based data acquisition techniques provides a feasible solution towards the automatic stemwork phenotyping. Nevertheless, existing QSM-based pipelines are sensitive towards the point cloud quality, and mostly focus on the phenotyping at plant or organ level. Information at internode level which are closely related to photosynthesis and light absorption was generally overlooked. To this end, a 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level. Coloured point clouds are taken as the sensor input of the pipeline. A semantic segmentation based on PointNet++ was used to detect and localise the stemwork points. To improve the quality of the segmented stemwork point clouds, a density-based refining pipeline is proposed containing three main processes: non-replacement resampling, interference branch removal, and noise removal. A Tree Quantitative Structural Modeling (TreeQSM) algorithm was then applied to the stemwork point cloud to construct a digital reconstruction. The target phenotypic traits were finally calculated from the digital model by employing an internode association process. The proposed phenotyping pipeline was evaluated with a test dataset containing three tomato plant cultivars: Merlice, Brioso, and Gardener Delight. The related rooted mean squared errors of calculated internode length, internode diameters, leaf branching angle, leaf phyllotactic angle, and stem length range from 4.8 to 64.4%. Considering the time consuming manual phenotyping process, the proposed work provides a feasible solution towards the high throughput plant phenotyping, from which facilitates the related research on plant breeding and crop management.

Comparative Evaluation of the Dimensional Accuracy of Silicone-Based Putty Reline Impressions with Different Spacer Acquisition Techniques in Fixed Partial Dentures

Comparative Evaluation of the Dimensional Accuracy of Silicone-Based Putty Reline Impressions with Different Spacer Acquisition Techniques in Fixed Partial Dentures

Time-harmonic optical flow with applications in elastography

Abstract In this paper, we propose mathematical models for reconstructing the optical flow in time-harmonic elastography. In this image acquisition technique, the object undergoes a special time-harmonic oscillation with known frequency so that only the spatially varying amplitude of the velocity field has to be determined. This allows for a simpler multi-frame optical flow analysis using Fourier analytic tools in time. We propose three variational optical flow models and show how their minimization can be tackled via Fourier transform in time. Numerical examples with synthetic as well as real-world data demonstrate the benefits of our approach.

Deep learning-enhanced automated mitochondrial segmentation in FIB-SEM images using an entropy-weighted ensemble approach.

Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondrial DNA can result in primary mitochondrial diseases, including neurodegenerative disorders. Early detection of these abnormalities is crucial in slowing disease progression. With recent advances in data acquisition techniques such as focused ion beam scanning electron microscopy, it has become feasible to capture large intracellular organelle volumes at data rates reaching 4Tb/minute, each containing numerous cells. However, manually segmenting large data volumes (gigapixels) can be time-consuming for pathologists. Therefore, there is an urgent need for automated tools that can efficiently segment mitochondria with minimal user intervention. Our article proposes an ensemble of two automatic segmentation pipelines to predict regions of interest specific to mitochondria. This architecture combines the predicted outputs from both pipelines using an ensemble learning-based entropy-weighted fusion technique. The methodology minimizes the impact of individual predictions and enhances the overall segmentation results. The performance of the segmentation task is evaluated using various metrics, ensuring the reliability of our results. We used four publicly available datasets to evaluate our proposed method's effectiveness. Our proposed fusion method has achieved a high score in terms of the mean Jaccard index and dice coefficient for all four datasets. For instance, in the UroCell dataset, our proposed fusion method achieved scores of 0.9644 for the mean Jaccard index and 0.9749 for the Dice coefficient. The mean error rate and pixel accuracy were 0.0062 and 0.9938, respectively. Later, we compared it with state-of-the-art methods like 2D and 3D CNN algorithms. Our ensemble approach shows promising segmentation efficiency with minimal intervention and can potentially aid in the early detection and mitigation of mitochondrial diseases.

Improvement of EME accuracy based on an equivalent voltage acquisition method and corresponding voltage compensation strategy

AbstractIt is a known cost‐effective method to employ a DSP‐based electric machine emulator (EME) for motor control unit testing, this paper introduces a novel approach to enhance the emulator's accuracy. The inaccuracies caused by terminal voltage sampling errors in the motor control unit and voltage distortions in the inverter are addressed in this paper. An advanced equivalent voltage acquisition technique, which samples the duty cycle‐amplitude of the inverter's terminal voltage, is developed. Leveraging the acquired equivalent voltage data, a novel voltage compensation strategy that providing more accuracy in EME performance is proposed. The mathematical foundation of the EME is established using Kirchhoff's voltage and current laws. These findings are independently validated through simulations and experiments. The results provide robust evidence that the proposed equivalent voltage acquisition and compensation strategies can enhance the accuracy of the EME.

Fetal Cardiovascular Magnetic Resonance: History, Current Status, and Future Directions.

Fetal cardiovascular magnetic resonance imaging (MRI) has emerged as a complementary modality for prenatal imaging in suspected congenital heart disease. Ongoing technical improvements extend the potential clinical value of fetal cardiovascular MRI. Ascertaining equivocal prenatal diagnostics obtained with ultrasonography allows for appropriate parental counseling and planning of postnatal surgery. This work summarizes current acquisition techniques and clinical applications of fetal cardiovascular MRI in the prenatal diagnosis and follow-up of fetuses with congenital heart disease. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

A Virtual View Acquisition Technique for Complex Scenes of Monocular Images Based on Layered Depth Images

With the rapid development of stereoscopic display technology, how to generate high-quality virtual view images has become the key in the applications of 3D video, 3D TV and virtual reality. The traditional virtual view rendering technology maps the reference view into the virtual view by means of 3D transformation, but when the background area is occluded by the foreground object, the content of the occluded area cannot be inferred. To solve this problem, we propose a virtual view acquisition technique for complex scenes of monocular images based on a layered depth image (LDI). Firstly, the depth discontinuities of the edge of the occluded area are reasonably grouped by using the multilayer representation of the LDI, and the depth edge of the occluded area is inpainted by the edge inpainting network. Then, the generative adversarial network (GAN) is used to fill the information of color and depth in the occluded area, and the inpainting virtual view is generated. Finally, GAN is used to optimize the color and depth of the virtual view, and the high-quality virtual view is generated. The effectiveness of the proposed method is proved by experiments, and it is also applicable to complex scenes.

SPP1 is a plasma biomarker associated with the dia gnosis and prediction of prognosis in sepsis

In this study, peripheral whole blood samples from 22 hospitalized patients and 10 healthy individuals were analyzed using a combination of Data Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) techniques to identify differentially expressed proteins (DEPs) in sepsis patients’ plasma. The aim was to provide accurate and detailed biomarkers, such as SPP1, for determining the pathological stages of sepsis. SPP1, known as osteopontin1 is a pleiotropic protein with a wide distribution and multifunctional effects. Its protein expression is associated with inflammatory changes, including variations in expression levels in infectious diseases, allergic diseases, and situations involving tissue damage. The registration number was ChiCTR1900021261.The full date of first registration year is 2018. In the affiliated hospital of southwest medical university, 22 sepsis patients were hospitalized from January 2019 to September 2020 and 10 normal healthy individuals were selected for DIA-based quantitative proteomics analysis. In addition to gene ontology analysis and Kyoto Encyclopedia of genes and genomes analysis, enrichment analysis of data was performed and target protein network was screened through joint protein–protein interaction and visualization techniques. The selected protein targets were then validated by Elisa kit. The software was used to analyze the differences comparing the control group to the sepsis group and the sepsis group, as well as between sepsis survivals and non-survivals, and a ROC curve was drawn to evaluate the diagnostic value and prognostic effect of the method of the corresponding target proteins. A total of 174 DEPs were screened by bioinformatics analysis. An analysis of go pathway enrichment revealed the following: These proteins were mainly involved in biological processes among them are the inflammation response, the metabolism of extracellular matrix, the secretion of cell secretions, the activation of cells, and the immune response. According to the Kegg pathway analysis, they were mainly involved in complement cascade polymerization, extracellular protease and glycosylase activation, protein synthesis process, biotin metabolism, leukocyte transmembrane migration, bacterial infection and phagosome formation. SPP1 was identified as a possible plasma biomarker and was therefore further validated using Elisa. As a result of experiments, it has been demonstrated that level in sepsis patients is significantly compared to the normal control group and the level is also higher in non-survivals of sepsis. The ROC curve can be used to see that it can diagnose sepsis more accurately and improve prognostic ability prediction. Cell experiments confirm that SPP1 is highly expressed in sepsis. There is a significant difference in the levels of SPP1 protein between the normal group and the sepsis group; it not only has good diagnostic significance for sepsis, but also provides corresponding reference value for patient prognosis; Therefore, it is more likely to become a biological marker of sepsis over time.

Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities

With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such as deep learning (DL) have demonstrated their strong abilities in various areas, including language, vision, remote sensing (RS), and agrifood systems applications. However, the overall impact of AI on agrifood systems remains unclear. In this article, we thoroughly review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. First, we summarize the data acquisition methods in agrifood systems, including acquisition, storage, and processing techniques. Second, we present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery, covering topics such as agrifood classification, growth monitoring, yield prediction, and quality assessment. Furthermore, we highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI. We hope this survey can offer an overall picture to newcomers in the field and serve as a starting point for their further research. The project website is https://github.com/Frenkie14/Agrifood-Survey.

MR and Ultrasound Elastography for Fibrosis Assessment in Children: Practical Implementation and Supporting Evidence-AJR Expert Panel Narrative Review.

Quantitative MRI and ultrasound biomarkers of liver fibrosis have become important tools in the diagnosis and clinical management of children with chronic liver disease (CLD). In particular, MR elastography is now routinely performed in clinical practice to evaluate the liver for fibrosis. Ultrasound shear-wave elastography has also become widely performed for this purpose, especially in young children. These noninvasive methods are increasingly used to replace liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. Although ultrasound has the advantages of portability and lower equipment cost than MRI, available evidence indicates that MRI may have greater reliability and accuracy in liver fibrosis evaluation. In this AJR Expert Panel Narrative Review, we describe how, why, and when to use MRI- and ultrasound-based elastography methods for liver fibrosis assessment in children. Practical approaches are discussed for adapting and optimizing these methods in children, with consideration of clinical indications, patient preparation, equipment requirements, and acquisition technique, as well as pitfalls and confounding factors. Guidance is provided for interpretation and reporting, and representative case examples are presented.

Regional variations in hyperpolarized 129Xe lung MRI: Insights from CSI-CSSR and CSSR in healthy and irradiated rat models.

To compare pulmonary function metrics obtained with hyperpolarized xenon-129 (HXe) MRS, using chemical shift saturation recovery (CSSR) and CSI-CSSR, in healthy rats and a rat model of radiation-induced lung injury. HXe-MR data were acquired in two healthy rats and one rat with radiation-induced lung injury using whole-lung spectroscopy and CSI-CSSR techniques. The CSI-CSSR acquisitions were performed with both fixed TE and variable TE. Apparent alveolar septal wall thickness, gas transfer dynamics, and regional lung function were quantified and compared across acquisition methods. Spectral analysis included alignment of dissolved-phase frequency spectra using the membrane resonance as reference, segmentation of gas-phase (GP) frequency distribution, and characterization of gas uptake in the vasculature. Complex GP line shapes were observed in rat lungs, necessitating pixel-wise CSI analysis and membrane resonance alignment for improved quantification. Notable differences in alveolar septal wall thickness, dissolved-phase GP ratios, and GP and red blood cell frequencies were found between acquisition techniques and lung conditions. CSI-CSSR provided unique insights into regional lung function, including the identification of distinct GP frequency zones potentially corresponding to different airway structures, and the ability to map relative xenon gas transport. Metrics from fixed-TE and variable-TE acquisitions usually differed by less than 10%, but the latter yielded a 20% SNR gain. HXe-MRS and CSI-CSSR techniques provide similar but not universally interchangeable insights into lung function, particularly in the presence of pathology.

Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for aEuropean multicenter study.

Advanced imaging techniques play apivotal role in oncology. Alarge variety of computed tomography (CT) scanners, scan protocols, and acquisition techniques have led to awide range in image quality and radiation exposure. This study aims at implementing verifiable oncological imaging by quality assurance and optimization (i-Violin) through harmonizing image quality and radiation dose across Europe. The 2‑year multicenter implementation study outlined here will focus on CT imaging of lung, stomach, and colorectal cancer and include imaging for four radiological indications: diagnosis, radiation therapy planning, staging, and follow-up. Therefore, 480 anonymized CT data sets of patients will be collected by the associated university hospitals and uploaded to arepository. Radiologists will determine key abdominopelvic structures for image quality assessment by consensus and subsequently adapt apreviously developed lung CT tool for the objective evaluation of image quality. The quality metrics will be evaluated for their correlation with perceived image quality and the standardized optimization strategy will be disseminated across Europe. The results of the outlined study will be used to obtain European reference data, to build teaching programs for the developed tools, and to create aculture of optimization in oncological CT imaging. The study protocol and rationale for i‑Violin, aEuropean approach for standardization and harmonization of image quality and optimization of CT procedures in oncological imaging, is presented. Future results will be disseminated across all EU member states, and i‑Violin is thus expected to have asustained impact on CT imaging for cancer patients across Europe.

The Influence of Social Presence on Purchase Intention in Live Video Commerce (Empirical Study in Bandung City)

The rapid development of internet technology has changed the way we live and interact. The increasing number of internet users in Indonesia has encouraged business actors to be more innovative in marketing their products digitally. The emergence of direct shopping features on various social media platforms has revolutionized the way consumers shop, allowing transactions to occur instantly and interactively. There is competition between e-commerce that uses live video features such as Shopee, TikTok, Tokopedia, and Lazada. This study aims to determine the role of social presence on purchase intention mediated by immersive experience and trust in live video commerce features in Indonesia. The research methodology employed is a descriptive and quantitative approach, which utilizes data acquisition techniques through online questionnaires to obtain 385 respondents. Purposive sampling is implemented in conjunction with nonprobability sampling. The data collected will be analyzed using SmartPLS version 3.9. The results of this study found that social presence has no effect on purchase intention. Keywords: Social Presence, Live Video Commerce, Purchase Intention.

The Use of Mind Mapping Technique in Acquisition of Grammar Structures in Foreign Language Teaching

Purpose: As in other languages, Turkish also consists of words formed by the combination of sounds and sounds and meaningful integrity formed by the combination of words in a ruleful way. Therefore, it needs some rules for both the formation of sounds, the formation of words and the formation of sentences, which constitutes grammar. In order to learn a foreign language fully and properly, it is necessary to master the rules of grammar. Methods and techniques designed for learning something in the best and shortest time are important and necessary for teaching grammar in foreign language teaching. The aim of this study is to examine the effectiveness of mind mapping technique, which is a different and new technique, in teaching grammar structures in teaching Turkish as a foreign language. Design/Methodology/Approach: In the study, mind mapping diagrams were used to teach grammar structures in teaching Turkish as a foreign language. According to this diagram, language consists of three components: form, function and use. For this reason, all grammar structures to be explained in the lesson were organized according to these three components and diagrams were prepared. Grammar structures were taught through the diagrams whose instructor opinions were taken. “Interview technique”, one of the qualitative data collection tools, was used in the study. For this, a semi-structured interview form was used. At Sakarya University TÖMER, a total of 22 learners at the A2 level were taught with the mind mapping technique for 7 weeks, and then the learners were interviewed one by one with a semi-structured interview form. Content analysis was used to analyze the data. The data obtained through content analysis were divided into themes and sub-themes. Findings: When the mind mapping technique in teaching grammar structures in teaching Turkish as a foreign language was analyzed according to learner opinions, 5 themes and 23 sub-themes were identified. Highlights: According to the opinions of the learners, positive results such as the mind mapping technique being effective in grammar teaching, being useful, having a structure that offers ease of teaching as well as contributing to recall and developing a positive attitude towards the lesson were determined.

Fellgett Revisited: On the Nature of Noise in Two-Dimensional Mass Spectrometry.

Two-dimensional mass spectrometry (2DMS) is a truly data-independent acquisition technique used in the analysis of complex mixtures; however, the nature of the noise within these spectra is not well understood. In this work, 2DMS is tested for conformity with the Fellgett principle: (signal/noise) ∝ √ (no. of data points). Since 2DMS functions through the modulation of ions through a fragmentation region across many scans, the individual scans are considered data points in this experiment. Random noise was shown to be prevalent as the main source of noise in this experiment with minor systematic noise. This means that the minimum size for a 2DMS spectrum that displays a target fragment ion can be determined using a fast-2D equation detailed herein. The effects of existing denoising algorithms were also found to change the relationship between the signal-to-noise ratio and the scan numbers to be of a quasi-linear nature rather than the square root trend observed before denoising.

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024