Pipeline Applications Research Articles

Effect of Earth-Moon’s gravity on TianQin’s range acceleration noise. III. An analytical model

TianQin is a proposed space-based gravitational-wave detector designed to operate in circular high Earth orbits. As a sequel to [Zhang et al. Phys. Rev. D 103, 062001 (2021)], this work provides an analytical model to account for the perturbing effect of the Earth's gravity field on the range acceleration noise between two TianQin satellites. For such an ``orbital noise,'' the Earth's contribution dominates above $5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}\text{ }\text{ }\mathrm{Hz}$ in the frequency spectrum, and the noise calibration and mitigation, if needed, can benefit from in-depth noise modeling. Our model derivation is based on Kaula's theory of satellite gravimetry with Fourier-style decomposition and uses circular reference orbits as an approximation. To validate the model, we compare the analytical and numerical results in two main scenarios. First, in the case of the Earth's static gravity field, both noise spectra are shown to agree well with each other at various orbital inclinations and radii, confirming our previous numerical work while providing more insight. Second, the model is extended to incorporate the Earth's time-variable gravity. Particularly relevant to TianQin, we augment the formulas to capture the disturbance from the Earth's free oscillations triggered by earthquakes, of which the mode frequencies enter TianQin's measurement band above 0.1 mHz. The analytical model may find applications in gravity environment monitoring and noise-reduction pipelines for TianQin.

A Decision-Making Carbon Reinforced Material Selection Model for Composite Polymers in Pipeline Applications

Pipes are manufactured primarily through the extrusion process. One of the material extrusion processes in recent digital manufacturing is additive manufacturing’s fusion deposition modeling. Pipes are made from various materials such as metal and plastic/polymers, and the main challenge has been in selecting the pipe material for the customized application. For the creation of water-passing tubes, this research has chosen appropriate carbon-reinforced polymers that can be used with filament made of polyether ether ketone (PEEK) and polyethylene terephthalate glycol (PETG). For this goal, the analytical hierarchy process, also known as the AHP, is used to choose the best material based on factors such as cost, temperature resistance, printing speed, and mechanical properties of the material. The results revealed that PEEK-CF is a better material for the customized impeller application than PETG-CF. The PEEK-CF obtains the higher priority value of 0.6363, and the PETG-CF obtains 0.2791. This decision-making technique can be used to select other comparable customized applications.

Abstract 2059: Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets

Abstract Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.

Investigation of the Fluid Flow in a Large Ball Valve Designed for Natural Gas Pipelines

Natural gas pipeline networks used for long-distance transportation are expanding quickly, and the construction of special valves with large diameters has especially increased since 2022. The design and manufacturing of the flow control equipment is carried out on a case-by-case basis, in accordance with the parameters required by the beneficiary. In this paper, results obtained by fluid flow simulation with SolidWorks2023 software for a 500 mm diameter trunnion ball valve lead to important information regarding how the fluid flow develops in the intermediary and fully closed positions. The large inner space of the ball allows the development of high-amplitude vortices; thus, the simulation demonstrates that the shut-on/off operation of large-diameter ball valves is mandatory to avoid fast destruction following partial opening. This paper also demonstrates why the metal–metal (MM) sealing with a double-piston effect (DPE) design for seats produces low leakage rates, including for the shut-off position; the pressure field reveals that few gas particles succeed in crossing the upstream sealing zone, and even fewer cross the downstream sealing zone. Additionally, the interpretation of the results explains and highlights the importance of using seats with a DPE design to achieve fire safety, which is required for natural gas pipeline applications.

Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning.

Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials.

A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data

BackgroundThe Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.MethodsThis study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.ResultsThe method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2).

Quasi-static pipeline in electroconvulsive therapy computational modeling

BackgroundComputational models of current flow during Electroconvulsive Therapy (ECT) rely on the quasi-static assumption, yet tissue impedance during ECT may be frequency specific and change adaptively to local electric field intensity. ObjectivesWe systematically consider the application of the quasi-static pipeline to ECT under conditions where 1) static impedance is measured before ECT and 2) during ECT when dynamic impedance is measured. We propose an update to ECT modeling accounting for frequency-dependent impedance. MethodsThe frequency content on an ECT device output is analyzed. The ECT electrode-body impedance under low-current conditions is measured with an impedance analyzer. A framework for ECT modeling under quasi-static conditions based on a single device-specific frequency (e.g., 1 kHz) is proposed. ResultsImpedance using ECT electrodes under low-current is frequency dependent and subject specific, and can be approximated at >100 Hz with a subject-specific lumped parameter circuit model but at <100 Hz increased non-linearly. The ECT device uses a 2 μA 800 Hz test signal and reports a static impedance that approximate 1 kHz impedance. Combined with prior evidence suggesting that conductivity does not vary significantly across ECT output frequencies at high-currents (800–900 mA), we update the adaptive pipeline for ECT modeling centered at 1 kHz frequency. Based on individual MRI and adaptive skin properties, models match static impedance (at 2 μA) and dynamic impedance (at 900 mA) of four ECT subjects. ConclusionsBy considering ECT modeling at a single representative frequency, ECT adaptive and non-adaptive modeling can be rationalized under a quasi-static pipeline.

Machine Learning Data Pipeline for the Democratization of AI

The use of artificial intelligence continues to increase. In healthcare, there has been a recent increase in AI applications to real-time individual patient clinical care, as opposed to population-based research or quality improvement efforts. However, the expertise to evaluate and implement these solutions is limited and often congregates in academic medical centers, creating barriers to adoption for smaller community and rural centers. Lowering the barrier to entry for innovative tools can help address disparities in patient outcomes due to access and other urban/rural contributors. We describe a strategy for evaluating commercially available machine learning models to disseminate lessons learned from developing, validating, and implementing machine learning-based models in clinical care in radiation therapy. In addition, we share an end-to-end data pipeline as open-source code with the tools necessary to identify, extract, organize, and process the data for use in machine-learning applications. We illustrate the application of this data pipeline to the use of brachytherapy to treat female cervical cancer patients. The example will show how we used the proposed pipeline to extract 708 potential participants and applied the developed methods and visualizations to clean the data providing 144 study participants for inclusion in our study. Finally, we discuss the anticipated challenges in implementing machine learning models in commercially available FDA-approved devices and suggest solutions using discrete tools built in different programming languages.

Discovery of Novel Effector Protein Candidates Produced in the Dorsal Gland of Adult Female Root-Knot Nematodes.

Root-knot nematodes (RKN) (Meloidogyne spp.) represent one of the most damaging groups of plant-parasitic nematodes. They secrete effector proteins through a protrusible stylet to manipulate host cells for their benefit. Stylet-secreted effector proteins are produced within specialized secretory esophageal gland cells, one dorsal gland (DG) and two subventral glands (SvG), whose activity differ throughout the nematode life cycle. Previous gland transcriptomic profiling studies identified dozens of candidate RKN effectors but were focused on the juvenile stages of the nematode, when the SvGs are most active. We developed a new approach to enrich for the active DGs of M. incognita adult female RKN for RNA and protein extraction. Female heads were manually cut from the body, and a combination of sonication and vortexing was used to dislodge contents inside the heads. DG-enriched fractions were collected by filtering, using cell strainers. Comparative transcriptome profiling of pre-parasitic second-stage juveniles, female heads, and DG-enriched samples was conducted using RNA sequencing. Application of an established effector mining pipeline led to the identification of 83 candidate effector genes upregulated in DG-enriched samples of adult females that code for proteins with a predicted signal peptide but lack transmembrane domains or homology to proteins in the free-living nematode Caenorhabditis elegans. In situ hybridization resulted in the identification of 14 new DG-specific candidate effectors expressed in adult females. Taken together, we have identified novel candidate Meloidogyne effector genes that may have essential roles during later stages of parasitism. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

A Comprehensive Analysis of In-Line Inspection Tools and Technologies for Steel Oil and Gas Pipelines

The transportation of oil and gas through pipelines is an integral aspect of the global energy infrastructure. It is crucial to ensure the safety and integrity of these pipelines, and one way to do so is by utilizing an inspection tool called a smart pig. This paper reviews various smart pigs used in steel oil and gas pipelines and classifies them according to pipeline structure, anomaly-detection capability, working principles, and application areas. The advantages and limitations of each sensor technology that can be used with the smart pig for in-line inspection (ILI) are discussed. In this context, ultrasonic testing (UT), electromagnetic acoustic transducer (EMAT), eddy current (EC), magnetic flux leakage (MFL), and mechanical contact (MC) sensors are investigated. This paper also provides a comprehensive analysis of the development chronology of these sensors in the literature. Additionally, combinations of relevant sensor technologies are compared for their accuracy in sizing anomaly depth, length, and width. In addition to their importance in maintaining the safety and reliability of pipelines, the use of ILI can also have environmental benefits. This study aims to further our understanding of the relationship between ILI and the environment.

Computer-aided design of offshore S-lay fiber-reinforced polymer pipeline installation

It is well known that the installation loads during offshore pipeline installation are usually higher than the operation loads. Fibre-reinforced plastic (FRP) materials for offshore pipeline applications seem to be very promising due to their high strength, light weight and excellent corrosion and fatigue resistance. However, during S-lay installation, high bending stresses that may cause material failure or buckling take place. Due to the anisotropic nature of multi-layered FRP pipelines, the permissible bending curvature is difficult to estimate. Few published works have proposed a framework for bending stress calculations and those that have provide complex equations that are not convenient for use. In this study, an open numerical code in Mathematica is offered for the first time. Numerical results for S-lay installation of representative pipelines are provided and commended.

Brain Data Standards - A method for building data-driven cell-type ontologies

Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Stress Analysis of Pipe-In-Pipe Systems under Free Span for Deep Water Pipeline Applications

This study examined the phenomena of free span for a pipe -in- pipe (PIP) system for pipeline application. Two different span length of 8 and 30 meters are modelled and simulated using nonlinear stress analysis. The effect of pressure, temperature and gravity on the PIP system are determined and compared with conventional single pipeline. From the results obtained, it is clear that the finite element analysis (FEA) results correlated very well with those calculated using analytical methods. Percentage differences were generally less than 10%, with some discrepancies which were due to assumption of thin-walled theory which assumes a radial stress equals to zero, whereas the FEA calculates a non- zero radial stress. The key finding in this study demonstrated the strong potentials of PIP system in terms of structural reliability for deepwater pipeline application. Specifically, the 30m single pipe in free span (with pressure and temperature) deflected 205.1mm, more than double the corresponding PIP. This knowledge can be beneficial to selection and design considerations for pipeline system responses to both the gravity, thermal and pressure loading as well as the potential failure modes that may results in a typical scenario. Various theoretical calculations of stresses are used to validate the finding in this study of the single pipe and PIP models for flat seabed and free span.

Allele-specific expression analysis for complex genetic phenotypes applied to a unique dilated cardiomyopathy cohort

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein–protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

A STUDY OF THE EFFECT OF FIBER CONTENT AND FIBER ORIENTATION ON THE TENSILE STRENGTH OF GLASS FIBER REINFORCED EPOXY COMPOSITE FOR PIPE PRODUCTION: A STATISTICAL APPROACH

This study was aimed at optimizing the development parameters of a glass fiber reinforced epoxy composite for pipeline application. In this study, the Taguchi experimental design technique was applied to determine the best combination of process parameters (fiber content and orientation) for optimum tensile strength. The optimum tensile strength of 209.65MPa was reported at the fiber content of 50% and Fiber orientation of 45º. Analysis of variance also showed that in optimizing for high tensile strength, the glass fiber content and the fiber orientation are significant factors. The material at optimum tensile properties was applied in the simulation of the behavior of a high-pressured gas pipeline and results proved the reliability of the material in such application.

An Experimental Study on the Interaction between Hydrate Formation and Wax Precipitation in Waxy Oil-in-Water Emulsions

The coupled formation of wax crystals and hydrates is a critical issue for the safety of deep-sea oil and gas exploration and subsea transport pipeline flow. Therefore, this paper conducts an experimental study on the characteristics of methane hydrate formation in a water-in-oil (W/O) system with different wax crystal contents and explores the influence of different initial experimental pressures on the induction period and maximum rate of hydrate formation. The wavelet function was introduced to process the reaction rate and calculate the maximum speed of hydrate formation. Notably, the higher the pressure, the smaller the maximum rate of hydrate formation. We observed that wax crystal precipitation increases the viscosity of the emulsion, which limits the diffusion of gas in the liquid phase during hydrate nucleation and thus delays the hydrate nucleation. The methane gas precipitation also affects the remaining fraction’s wax content and therefore affects the wax precipitation. Secondary hydrate formation was observed several times during the experiment, increasing the risk of pipeline blockage. Overall, this work provides insights into the effect of wax crystal precipitation on hydrate behaviour that could facilitate flow assurance applications in subsea multiphase pipelines and inform the safe transportation of oil and gas pipelines.

99m-Tc MDP Bone Scan Image Enhancement using Pipeline Application of Dynamic Stochastic Resonance Algorithm and Block-Matching 3D Filter.

In this pilot study, we have proposed and evaluated pipelined application of the dynamic stochastic resonance (DSR) algorithm and block-matching 3D (BM3D) filter for the enhancement of nuclear medicine images. The enhanced images out of the pipeline were compared with the corresponding enhanced images obtained using individual applications of DSR and BM3D algorithm. Twenty 99m-Tc MDP bone scan images acquired on SymbiaT6 SPECT/CT gamma camera system fitted with low-energy high-resolution collimators were exported in DICOM format to a personal computer and converted into PNG format. These PNG images were processed using the proposed algorithm in MATLAB. Two nuclear medicine physicians visually compared each input and its corresponding three enhanced images to select the best-enhanced image. The image quality metrics (Brightness, Global Contrast Factor (GCF), Contrast per pixel (CPP), and Blur) were used to assess the image quality objectively. The Wilcoxon signed test was applied to find a statistically significant difference in Brightness, GCF, CPP, and Blur of enhanced and its input images at a level of significance. Images enhanced using the pipelined application of SR and BM3D were selected as the best images by both nuclear medicine physicians. Based on Brightness, Global Contrast Factor (GCF), CPP, and Blur, the image quality of our proposed pipeline was significantly better than enhanced images obtained using individual applications of DSR and BM3D algorithm. The proposed method was found to be very successful in enhancing details in the low count region of input images. The enhanced images were bright, smooth, and had better target-to-background ratio compared to input images. The pipelined application of DSR and BM3D algorithm produced enhancement in nuclear medicine images having following characteristics: bright, smooth, better target-to-background ratio, and improved visibility of details in the low count regions of the input image, as compared to individual enhancements by application of DSR or BM3D algorithm.

Assessing the impact of Zn-MgO-xCn Ternary Coatings on A36 Low Carbon Steels for Oil and Gas Pipeline Applications

Assessing the impact of Zn-MgO-xCn Ternary Coatings on A36 Low Carbon Steels for Oil and Gas Pipeline Applications

Application of BIM Technology in the Construction of Building Fire Pipelines

This With the rapid development of science and technology, the construction industry should also invest in new technologies and new vitality. BIM technology came into being under this background, and has the advantages and characteristics that the traditional technology can't match in the past. New technologies such as BIM must be put into the construction of building fire pipelines. Using its characteristics and properties, it can reduce the pressure during pipeline construction and improve the quality of pipeline construction. It can also effectively improve the construction of building fire pipelines. BIM technology will be used in buildings. It plays a huge role in the construction and application of fire pipelines and brings great convenience. This article will introduce BIM technology through discussion, and analyze the application of BIM technology in the construction of building fire pipelines.

The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing.

The increasing availability of "real-world" data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the "gold standard" for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the "Linguistic Inquiry and Word Count" software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning-topic modeling; and (5) results interpretation and validation. A topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: "contacts/communication;" "social environment;" "work;" and "errands/daily routines." Notably, the sentiment analysis revealed that the "contacts/communication" group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19-related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.