Data Processing Protocols Research Articles

The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.

We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze-tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.

Pooled Analysis of Physical Activity, Sedentary Behavior, and Sleep Among Children From 33 Countries

The prevalence estimates of physical activity, sedentary behavior, and sleep (collectively known as movement behaviors) in 3- and 4-year-old children worldwide remains uncertain. To report the proportion of 3- and 4-year-old children who met the World Health Organization guidelines for physical activity, sedentary behavior, and sleep across 33 countries. Pooled analysis of data from 14 cross-sectional studies (July 2008 to September 2022) identified through systematic reviews and personal networks. Thirty-three countries of varying income levels across 6 geographical regions. Each study site needed to have at least 40 children aged 3.0 to 4.9 years with valid accelerometry and parent-/caregiver-reported screen time and sleep duration data. Data were analyzed from October 2022 to February 2023. Time spent in physical activity was assessed by reanalyzing accelerometry data using a harmonized data-processing protocol. Screen time and sleep duration were proxy reported by parents or caregivers. The proportion of children who met the World Health Organization guidelines for physical activity (≥180 min/d of total physical activity and ≥60 min/d of moderate- to vigorous-intensity physical activity), screen time (≤1 h/d), and sleep duration (10-13 h/d) was estimated across countries and by World Bank income group and geographical region using meta-analysis. Of the 7017 children (mean [SD] age, 4.1 [0.5] years; 3585 [51.1%] boys and 3432 [48.9%] girls) in this pooled analysis, 14.3% (95% CI, 9.7-20.7) met the overall guidelines for physical activity, screen time, and sleep duration. There was no clear pattern according to income group: the proportion meeting the guidelines was 16.6% (95% CI, 10.4-25.3) in low- and lower-middle-income countries, 11.9% (95% CI, 5.9-22.5) in upper-middle-income countries, and 14.4% (95% CI, 9.6-21.1) in high-income countries. The region with the highest proportion meeting the guidelines was Africa (23.9%; 95% CI, 11.6-43.0), while the lowest proportion was in North and South America (7.7%; 95% CI, 3.6-15.8). Most 3- and 4-year-old children in this pooled analysis did not meet the current World Health Organization guidelines for physical activity, sedentary behavior, and sleep. Priority must be given to understanding factors that influence these behaviors in this age group and to implementing contextually appropriate programs and policies proven to be effective in promoting healthy levels of movement behaviors.

On discovery of novel hub genes for ER+ and TN breast cancer types through RNA seq data analyses and classification models

Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG’s exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Label-free photoacoustic computed tomography of visually evoked responses in the primary visual cortex and four subcortical retinorecipient nuclei of anesthetized mice.

Many techniques exist for screening retinal phenotypes in mouse models in vision research, but significant challenges remain for efficiently probing higher visual centers of the brain. Photoacoustic computed tomography (PACT), with optical sensitivity to hemodynamic response (HR) in brain and ultrasound resolution, provides unique advantages in comprehensively assessing higher visual function in the mouse brain. We aim to examine the reliability of PACT in the functional phenotyping of mouse models for vision research. A PACT-ultrasound (US) parallel imaging system was established with a one-dimensional (1D) US transducer array and a tunable laser. Imaging was performed at three coronal planes of the brain, covering the primary visual cortex and the four subcortical nuclei, including the superior colliculus, the dorsal lateral geniculate nucleus, the suprachiasmatic nucleus, and the olivary pretectal nucleus. The visual-evoked HR was isolated from background signals using an impulse-based data processing protocol. rd1 mice with rod/cone degeneration, melanopsin-knockout (mel-KO) mice with photoreceptive ganglion cells that lack intrinsic photosensitivity, and wild-type mice as controls were imaged. The quantitative characteristics of the visual-evoked HR were compared. Quantitative analysis of the HRs shows significant differences among the three mouse strains: (1)rd1 mice showed both smaller and slower responses compared with wild type ( , ) and (2)mel-KO mice had lower amplitude but not significantly delayed photoresponses than wild-type mice ( , ). These results agree with the known visual deficits of the mouse strains. PACT demonstrated sufficient sensitivity to detecting post-retinal functional deficits.

Effect of Data-Processing Methods on Acceleration Summary Metrics of GNSS Devices in Elite Australian Football.

This study aimed to measure the differences in commonly used summary acceleration metrics during elite Australian football games under three different data processing protocols (raw, custom-processed, manufacturer-processed). Estimates of distance, speed and acceleration were collected with a 10-Hz GNSS tracking technology device from fourteen matches of 38 elite Australian football players from one team. Raw and manufacturer-processed data were exported from respective proprietary software and two common summary acceleration metrics (number of efforts and distance within medium/high-intensity zone) were calculated for the three processing methods. To estimate the effect of the three different data processing methods on the summary metrics, linear mixed models were used. The main findings demonstrated that there were substantial differences between the three processing methods; the manufacturer-processed acceleration data had the lowest reported distance (up to 184 times lower) and efforts (up to 89 times lower), followed by the custom-processed distance (up to 3.3 times lower) and efforts (up to 4.3 times lower), where raw data had the highest reported distance and efforts. The results indicated that different processing methods changed the metric output and in turn alters the quantification of the demands of a sport (volume, intensity and frequency of the metrics). Coaches, practitioners and researchers need to understand that various processing methods alter the summary metrics of acceleration data. By being informed about how these metrics are affected by processing methods, they can better interpret the data available and effectively tailor their training programs to match the demands of competition.

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data.

Comprehensive biodiversity data is crucial for ecosystem protection. The Biome mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million observations since 2019. Nonetheless, community-sourced data may exhibit spatial and taxonomic biases. Species distribution models (SDMs) estimate species distribution while accommodating such bias. Here, we investigated the quality of Biome data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating Biome data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2000 records for accurate models (Boyce index ≥ 0.9), while blending the two data sources reduced this to around 300. The uniform coverage of urban-natural gradients by Biome data, compared to traditional data biased towards natural areas, may explain this improvement. Combining multiple data sources better estimates species distributions, aiding in protected area designation and ecosystem service assessment. Establishing a platform for accumulating community-sourced distribution data will contribute to conserving and monitoring natural ecosystems.

Scanning-assisted focal plane-detection system for a sector-field mass spectrometer - Part-I: Simulation and data processing

In this paper, a novel detection system, the Scanning-Assisted Focal Plane-Detection System (SAFPDS), is proposed as a focal plane detector for sector-field mass spectrometers. The principle of SAFPDS is to decouple the spatial resolution and local count rate of the microchannel plate (MCP) focal plane-detection system in order to enhance the local count rate and dynamic range limited by the MCP. This decoupling is achieved by deflecting ions along the vertical direction of the focal plane detector using an ion deflector during acquisition. Simulations of the SAFPDS are reported for optimal geometrical design, operation, and performance. Additionally, a dedicated novel data processing protocol for deriving a representative mass spectrum from the 3D-recorded data obtained with SAFPDS is developed in combination with the SAFPDS hardware. Our study shows that this protocol effectively enhances the spectrometer's Figure of Merit (FOM), defined as the integral of the mass resolution (m/Δm) over the relevant mass range, by a factor of 2.4. Furthermore, the evaluation of the spectrometer equipped with SAFPDS highlights its capability to enhance the local count rate and dynamic range of sector field mass spectrometers with an MCP-based detector by at least one order of magnitude. Importantly, this enhancement is achieved with a deterioration in mass resolving power that remains below 30%, which we consider acceptable given the significant improvements in local count rate and dynamic range.

Evaluation of Bitemark Analysis's Potential Application in Forensic Identification: A Systematic Review.

to investigate the available literature systematically and evaluate the scientific evidence published over the past decade concerning the potential application of bitemark analysis in forensic identification. Two researchers meticulously searched electronic databases from January 2012 to December 2023, including Scopus, PubMed, Web of Science, and the Cochrane Library. Adhering to the PRISMA statement guidelines, this review employed appropriate medical subject headings (MeSHs) and free-text synonyms. Strict inclusion and exclusion criteria were applied during article retrieval. The findings yielded controversial outcomes. Approximately two-thirds of the articles concluded that bitemark analysis is useful in forensic identification, while the remaining articles did not report statistically significant outcomes and cautioned against relying solely on bitemark analysis for identification. The authors assert that bitemark analysis can be a reliable and complementary method for forensic identification, contingent upon the establishment and adoption of a universally accepted global protocol for data collection, processing, and interpretation. Undoubtedly, recent years have witnessed a notable increase in research focused on bitemark identification, driven by the goal of achieving quantitative, objective, reproducible, and accurate results.

Product-specific reaction kinetics in continuous uniform supersonic flows probed by chirped-pulse microwave spectroscopy.

Experimental studies of the products of elementary gas-phase chemical reactions occurring at low temperatures (<50K) are very scarce, but of importance for fundamental studies of reaction dynamics, comparisons with high-level quantum dynamical calculations, and, in particular, for providing data for the modeling of cold astrophysical environments, such as dense interstellar clouds, the atmospheres of the outer planets, and cometary comae. This study describes the construction and testing of a new apparatus designed to measure product branching fractions of elementary bimolecular gas-phase reactions at low temperatures. It combines chirped-pulse Fourier transform millimeter wave spectroscopy with continuous uniform supersonic flows and high repetition rate laser photolysis. After a comprehensive description of the apparatus, the experimental procedures and data processing protocols used for signal recovery, the capabilities of the instrument are explored by the study of the photodissociation of acrylonitrile and the detection of two of its photoproducts, HC3N and HCN. A description is then given of a study of the reactions of the CN radical with C2H2 at 30K, detecting the HC3N product, and with C2H6 at 10K, detecting the HCN product. A calibration of these two products is finally attempted using the photodissociation of acrylonitrile as a reference process. The limitations and possible improvements in the instrument are discussed in conclusion.

A mass defect-based approach for the automatic construction of peak lists for databases of mass spectra with limited resolution: Application to time-of-flight secondary ion mass spectrometry data.

This study has developed a data processing protocol based on mass defect analysis for the automatic construction of unique peak lists addressing the need for the fast and efficient treatment of databases of mass spectra with limited mass resolution. The data processing protocol, implemented in MATLAB, is tested on a database of 126 mass spectra obtained from time-of-flight secondary ion mass spectrometry analysis of the exhaust of a laboratory diesel miniCAST burner deposited on Ti substrates. The data processing protocol converts the mass spectra into a data matrix suitable for chemometrics (peak list) by combining mass defect analysis and multivariate analysis. In particular, the role of the mass defect analysis is expanded to improve mass calibration and automate the construction of the peak list. In this context, mass defect analysis becomes an invaluable technique for the efficient processing of databases of mass spectra with limited mass resolution by allowing the fast and automated construction of a peak list common to all mass spectra, by improving the mass calibration, and finally by reducing the number of molecular formulae consistent with a given accurate mass, thus facilitating the identification of unknown ions.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction.

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Evaluating Source, Scale Risk, and Corrosion Risk of the Produced Water Samples from the Appalachian Basin Based on a Geochemical Database

Summary Statistically, oil and gas production can generate up to 20 times the oil equivalent of produced water. The composition of produced water samples reflects its source, its interactions with reservoir rocks, and downhole (DH) facilities, which are critical for basin evolution, water source determination, and the monitoring, management, and optimization of oil and gas production. For example, scale and corrosion, two of the most severe flow assurance issues accompanied by produced water, can lead to billions of dollars lost every year. However, few studies have developed a standard protocol to extract such valuable information from produced water compositions due to a lack of data and professional models. Using produced water geochemical data from the Appalachian Basin, one of the largest natural gas producers in the US, from the United States Geological Survey (USGS) Produced Waters Geochemical Database (PWGD), we developed a standard protocol to investigate the produced water source, evolution history, and scale and corrosion risks under both DH and surface conditions by means of incorporating the professional models for water-rock interaction and corrosion. The results show that the produced water from the Appalachian Basin possibly evolves from seawater evaporation following a typical evolution pattern of ion concentration and water isotopes, while a group of time-elapsed samples indicates that such an evolution pattern can also be due to the mixture of the injected water and reservoir water. In addition, most produced water samples show obvious risks of mineral scaling (e.g., calcite, barite, and siderite) and CO2 corrosion with corresponding mitigation strategies recommended. This study not only developed a reliable data processing and analysis protocol but also showed the valuable information a systematic analysis of produced water samples can provide for actual oil and gas production.

Embedded Cyber-Physical System for Physiological Control of Ventricular Assist Devices

Ventricular Assist Devices (VADs) play a crucial role in both bridging to transplantation and serving as destination therapy for congestive heart failure (CHF) management. This study aims to address the limitations of existing control strategies for VADs, specifically their inability to adapt automatically to hemodynamic changes. It proposes a novel embedded cyber-physical system (CPS) based on real-time data processing, reconfigurable architecture, and communication protocols aligned with Health 4.0 concepts to enhance physiological control over VADs (PC-VAD). The research employs a multi-objective PC-VAD approach within a hybrid cardiovascular simulator. An embedded CPS is introduced to overcome challenges related to differences in controller characteristics between computers and embedded systems. The study assesses the performance of the embedded CPS by comparing it with a computer-based control system. The embedded CPS demonstrates outcomes comparable to the computer-based control system, maintaining mean arterial pressure and cardiac output at physiological levels. Even in the face of variations in ejection fraction, the embedded CPS dynamically adjusts the pump's rotational speed based on simulated clinical conditions. Notably, there is no aortic reflux to the ventricle through the VAD during testing. These findings affirm the satisfactory control performance of the embedded CPS in regulating VADs. The study concludes that the embedded CPS effectively addresses the limitations of current VAD control strategies, exhibiting control performance comparable to computer-based systems. However, further experimentation and in vivo studies are necessary to validate and ensure its applicability in real-world scenarios.

Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review

IntroductionUntargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type.ObjectivesThis review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication.MethodsStudies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies.ResultsFrom 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable.ConclusionsStandardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

High-resolution Tandem Mass Spectrometry for Studying Chemical Constituents of Gynura bicolor DC.

The separation and analysis of the desired chemical components are important subjects for the fundamental research of traditional Chinese medicine (TCM). Ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) has gradually become a leading technology for the identification of TCM ingredients. Gynura bicolor DC. (BFH), a perennial stemless herb used for medicine and food in China has medicinal effects such as clearing heat, moistening the lung, relieving cough, dispersing stasis, and relieving swelling. Polyphenols and flavonoids contain numerous isomers, which hinder the identification of the complex compounds in BFH. This paper presents a systematic protocol for studying chemical constituents of BFH based on solvent extraction and integrated data via UPLC-Q-TOF-MS. The method described here includes systematic protocols for sample pretreatment, MS calibration, MS acquisition, data processing, and analysis of results. Sample pretreatment includes collection, cleaning, drying, crushing, and extraction. MS calibration consists of multipoint and single-point correction. Data processing includes data importing, method establishment, analysis processing, and result presentation. Representative results of the typical fragmentation pattern of phenolic acids, esters, and glycosides in Gynura bicolor DC. (BFH) are presented in this paper. In addition, organic solvent selection, extraction, data integration, collision energy selection, and method improvement are discussed in detail. This universal protocol can be widely used to identify complex compounds in TCM.

Human Kir2.1 Potassium Channel: Protocols for Cryo-EM Data Processing and Molecular Dynamics Simulations.

Kir channels are potassium (K+) channels responsible for the mechanism of inward rectification, which plays a fundamental role in maintaining the resting membrane potential. There are seven Kir subfamilies, and their opening and closing mechanism is regulated by different regulatory factors. Genetically inherited defects in Kir channels are responsible for several rare human diseases, and for most of them, there are currently no effective therapeutic treatments. High-resolution structural information is not available for several members within the Kir subfamilies. Recently, our group achieved a significant breakthrough by utilizing cryo-EM single-particle analysis to elucidate the first structure of the human Kir2.1 channel. We present here the data processing protocol of the cryo-EM data of the human Kir2.1 channel, which is applicable to the structural determination of other ion channels by cryo-EM single-particle analysis. We also introduce a protocol designed to assess the structural heterogeneity within the cryo-EM data, allowing for the identification of other possible protein structure conformations present in the collected data. Moreover, we present a protocol for conducting all-atom molecular dynamics (MD) simulations for K+ channels, which can be incorporated into various membrane models to simulate different environments. We also propose some methods for analyzing the MD simulations, with a particular emphasis on assessing the local mobility of protein residues.