OBJECTIVE: In this study, we aimed to develop an application that computes dose values resembling diagnostic reference level (DRL) conditions when disparity prevents direct dose comparisons between the national diagnostic reference levels in Japan 2020 (Japan DRLs_2020) and facility-specific computed tomography (CT) protocols.
METHODS: We developed an application using the R programming language and RStudio software that computes dose values and median values based on Japan DRLs_2020 imaging conditions following extraction of necessary information for dose calculations from the Radiation Dose Structured Report (RDSR) and Digital Imaging and Communications in Medicine (DICOM) tags. To ensure a user-friendly experience, we used the Shiny package to develop a graphical user interface that enables the application to operate seamlessly in web browsers.
RESULTS: The developed application successfully facilitated the calculation of dose and median values that aligned with the Japan DRLs_2020 for protocols whose imaging range and acquisition timing differed from those of the Japan DRLs_2020.
CONCLUSIONS: By calculating dose values that align with DRL conditions, our application contributes to the implementation and optimization of dose management in CT for facilities that use diverse imaging protocols.

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as \"CGPD-tetramers,\" wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum.

With global climate change leading to increasing intensity and frequency of droughts, as well as the growing problem of soil salinization, these factors significantly affect crop growth, yield, and resilience to adversity. Oats are a cereal widely grown in temperate regions and are rich in nutritive value; however, the scientific literature on the response of oat to drought and salt stress has not yet been analyzed in detail. This study comprehensively analyzed the response of oat to drought stress and salt stress using data from the Web of Science core database and bibliometric methods with R (version4.3.1), VOSviewer (version 1.6.19), and Citespace (version6.3.1.0) software. The number of publications shows an increasing trend in drought stress and salt stress in oat over the past 30 years. In the field of drought-stress research, China, the United States, and Canada lead in terms of literature publication, with the most academic achievements being from China Agricultural University and Canadian Agricultural Food University. The journal with the highest number of published papers is Field Crops Research. Oat research primarily focuses on growth, yield, physiological and biochemical responses, and strategies for improving drought resistance. Screening of drought-tolerant genotypes and transformation of drought-tolerant genes may be key directions for future oat drought research. In the field of salt-stress research, contributions from China, the United States, and India stand out, with the Chinese Academy of Agricultural Sciences and Inner Mongolia Agricultural University producing the most significant research results. The largest number of published articles has been found in the Physiologia Plantarum journal. Current oat salt-stress research primarily covers growth, physiological and biochemical responses, and salt-tolerance mechanisms. It is expected that future oat salt research will focus more on physiological and biochemical responses, as well as gene-editing techniques. Despite achievements under single-stress conditions, combined drought and salt-stress effects on oat remain understudied, necessitating future research on their interaction at various biological levels. The purpose of this study is to provide potential theoretical directions for oat research on drought and salt stress.

As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We applied dimensionality reduction and clustering to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states in a mouse dataset that we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semiautomated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain region-specific manner. Within this pool of features, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and circularity. When considered together, these sets of features drove different morphological clusters. Our tools captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy-to-use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.

Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.

Recently, there has been considerable progress in developing new technologies and equipment for the medical field, including minimally invasive surgeries. Evaluating the effectiveness of these treatments requires study designs like randomized controlled trials. However, due to the nature of certain treatments, randomization is not always feasible, leading to the use of observational studies. The effect size estimated from observational studies is subject to selection bias caused by confounders. One method to reduce this bias is propensity scoring. This study aimed to introduce a propensity score matching process between two groups using a practical example with R. Additionally, Rex, an Excel add-in graphical user interface statistical program, is provided for researchers unfamiliar with R programming. Further techniques, such as matching with three or more groups, propensity score weighting and stratification, and imputation of missing values, are summarized to offer approaches for more complex studies not covered in this tutorial.

BACKGROUND: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence.
OBJECTIVE: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
METHODS: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients\' demographic data. The script formats the data longitudinally and calculates each day\'s medication implementation.
RESULTS: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence.
CONCLUSIONS: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.

BACKGROUND: Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity.
METHODS: By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots.
RESULTS: In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting.
CONCLUSIONS: While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .