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Background Inductively coupled plasma mass spectrometry (ICP‐MS) is widely used for the accurate measurement of minerals. However, its application to serum essential mineral measurement has not been fully evaluated. The present study aimed to assess the performance of ICP‐MS for serum minerals by comparing its measurements to those obtained using standard quantification methods. Methods Cross‐sectional data were collected from 282 participants from a single facility in Japan. Serum concentrations of eight key minerals, namely sodium, potassium, calcium, phosphorus, magnesium, iron, zinc, and copper, measured via ICP‐MS and standard methods were compared using Passing–Bablok regression and Bland–Altman plots. Results All minerals, except phosphorus, exhibited good agreement with standard methods, with more stable regression coefficients observed for minerals with greater interindividual variability. After systematically filtering outliers, the mean relative errors were approximately −3% for sodium, potassium, calcium, and magnesium; +5% for iron; 0% for zinc; and −19% for copper. The outliers for iron were primarily due to mild hemolysis, whereas those for zinc were largely attributed to nonhemolysis factors. For phosphorus, the serum total phosphorus concentration measured using ICP‐MS was approximately 3.5 times higher than the serum inorganic phosphorus concentration measured using standard methods, with a weak correlation observed between the two methods. Conclusion This study provides a practical foundation for future research. Understanding ICP‐MS characteristics will facilitate the development of new approaches in clinical diagnostics. Analysis of real‐world cross‐sectional study data revealed relative errors between the standard method and ICP‐MS for the different minerals tested. Additionally, several outliers were observed exclusively in the ICP‐MS results, likely due to hemolysis or other unidentified factors. Although these limitations in ICP‐MS performance cannot be entirely dismissed, comparative results of the standard method and the ICP‐MS approach exhibited good agreement. The unique characteristics of ICP‐MS identified in this study lay a strong foundation for future research, not only for routine clinical measurement of specific minerals but also for disease‐specific analyses leveraging the ability of ICP‐MS to simultaneously measure a wide range of parameters.

Integrating automated vehicles into mixed traffic entails several challenges. Their driving behavior must be designed such that is understandable for all human road users, and that it ensures an efficient and safe traffic system. Previous studies investigated these issues, especially regarding the communication between automated vehicles and pedestrians. These studies used different methods, e.g., videos, virtual reality, or Wizard of Oz vehicles. However, the extent of transferability between these studies is still unknown. Therefore, we replicated the same study design in four different settings: two video, one virtual reality, and one Wizard of Oz setup. In the first video setup, videos from the virtual reality setup were used, while in the second setup, we filmed the Wizard of Oz vehicle. In all studies, participants stood at the roadside in a shared space. An automated vehicle approached from the left, using different driving profiles characterized by changing speed to communicate its intention to let the pedestrians cross the road. Participants were asked to recognize the intention of the automated vehicle and to press a button as soon as they realized this intention. Results revealed differences in the intention recognition time between the four study setups, as well as in the correct intention rate. The results from vehicle–pedestrian interaction studies published in recent years that used different study settings can therefore only be compared to each other to a limited extent.

The setting direction of the evaluation indicators in the current research and the differences of the research methods in terms of principles, advantages and disadvantages and applicability are compared and analyzed. The results show that the current well-facilitated farmland construction has achieved remarkable results. In the suitability evaluation research before project construction, the setting of evaluation indicators focuses more on the natural endowment conditions of regional cultivated land; in the benefit evaluation research after project construction, from The early focus on economic benefit evaluation has gradually developed into an equal emphasis on economic, social and ecological benefit evaluation; in the analysis of influencing factors, the current research is still relatively weak, and there are still few explanations for the differences in the level of benefit improvement in different construction areas. Regarding research methods, future research should comprehensively consider the advantages, disadvantages and applicability of different methods. This study can provide a useful reference for scientifically promoting well-facilitated farmland construction.

Background Chromatin folding gives rise to structural elements among which are clusters of densely interacting DNA regions termed topologically associating domains (TADs). TADs have been characterized across multiple species, tissue types, and differentiation stages, sometimes in association with regulation of biological functions. The reliability and reproducibility of these findings are intrinsically related with the correct identification of these domains from high-throughput chromatin conformation capture (Hi-C) experiments. Results Here, we test and compare 22 computational methods to identify TADs across 20 different conditions. We find that TAD sizes and numbers vary significantly among callers and data resolutions, challenging the definition of an average TAD size, but strengthening the hypothesis that TADs are hierarchically organized domains, rather than disjoint structural elements. Performances of these methods differ based on data resolution and normalization strategy, but a core set of TAD callers consistently retrieve reproducible domains, even at low sequencing depths, that are enriched for TAD-associated biological features. Conclusions This study provides a reference for the analysis of chromatin domains from Hi-C experiments and useful guidelines for choosing a suitable approach based on the experimental design, available data, and biological question of interest.

•Compared results for every step of classification of an episodic memory EEG dataset•Performance improves with extracting greater number of feature types.•Combination of filter and wrapper feature selection outperformed other methods•LASSO outperformed other classifiers, while naive Bayes was the fastest classifier•We provide recommendations for achieving highest classification performance Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies—and classification analyses in general— do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states—the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

Background In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Results Here, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3–35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. Conclusions Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.

Background Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. Results We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. Conclusions We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Various methods have been reported to quantify total biofilm or different components of biofilm; however, these methods are often confusedly used, leading to discrepancies and misleading results. In this study, different methods for quantification of biofilm, including those for total biomass, total amount of bacterial cells, viable cell number, and amount of extracellular polymeric substances, were systematically compared in microtiter plates. To evaluate which method is suitable for assessment of biofilm removal and for bacterial killing, biofilm samples were treated with various cleaners possessing removing and/or killing capacities. It was found that most of the methods tested in this study in general exhibited high reproducibility and repeatability. Crystal Violet staining was a simple but reliable method for total biomass quantification. Total bacteria cell numbers could be reliably quantified by the fluorescent DNA-binding dye Acridine Orange. Viable cells could be quantified by either an ATP-based assay or a proliferation assay. Both of these viability methods showed a broad detection range and led to precise measurement. For quantification of proteins in the biofilm, staining with fluorescein isothiocyanate was most suitable. Furthermore, it was revealed that a combination of different methods is required to determine if a cleaner kills or removes biofilm.

This paper presents the results of the first international comparative study of commonly applied analytical methods for microplastic analysis. Although it was shown that the comparability between previously published studies is highly limited, there are ambitious efforts regarding the standardization of microplastic analysis. This comparative study serves as a first step to assess the suitability of frequently used methods in microplastic research. Furthermore, it highlights obstacles when conducting a comparative study for microplastics. Results from 17 laboratories from eight different countries are compared. Samples comprised of five different types of microplastic reference particles with diameters ranging from 8 µm to 140 μm suspended in ultrapure water. Microscopy, Fourier-transform infrared microspectroscopy (μ-FTIR), Raman microspectroscopy (μ-Raman), thermo-extraction-and-desorption- or pyrolysis- combined with gas chromatography coupled to mass spectrometry (Σ-GC/MS), scanning electron microscopy and particle counter were compared regarding results on total particle number, polymer type, number of particles and/or particle mass for each polymer type. In the scope of this comparative study, for the identification of polymer type μ-Raman and Σ-GC/MS performed best. The quantification of polymer mass for identified polymer types was questionable for Σ-GC/MS, whereas other methods failed to determine the correct polymer mass. Quantification of particle number per identified polymer type was evaluated successful for μ-FTIR and the quantification of total particle numbers was best for microscopy and to a lesser extent for μ-FTIR. Remarkable was the large variance of results between the methods but also within the methods. The latter is likely due to individual interpretations of methods and preparation protocols, in particular in regard to the handling of blank values. Results strongly emphasize the need for standardization and validation of analytical methods in microplastic research both on a global scale as well as in the context of individual laboratories.

Background Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects. Methods We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement. Results The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others. Conclusions Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this.