File List nonlinear_regression.R Description The "nonlinear_regression.R" program provides a short example (with data) of one way to perform nonlinear regression in R (version 2.8.1). This example is not meant to provide extensive information on or training in programming in R, but rather is meant to serve as a starting point for performing nonlinear regression in R. R is a free statistical computing and graphics program that may be run on of UNIX platforms, Windows and MacOS. R may be downloaded here: http://www.r-project.org/.

There are several good
 resources for learning how to program and perform extensive statistical
 analyses in R, including:

 Benjamin M. Bolker. Ecological Models and Data in R. Princeton
 University Press, 2008. ISBN 978-0-691-12522-0. [
 http://www.zoology.ufl.edu/bolker/emdbook/ ]

 Other references are provided at http://www.r-project.org/ under

“Documentation” and “Books”.

Functional diversity (FD) is an important component of biodiversity that quantifies the difference in functional traits between organisms. However, FD studies are often limited by the availability of trait data and FD indices are sensitive to data gaps. The distribution of species abundance and trait data, and its transformation, may further affect the accuracy of indices when data is incomplete. Using an existing approach, we simulated the effects of missing trait data by gradually removing data from a plant, an ant and a bird community dataset (12, 59, and 8 plots containing 62, 297 and 238 species respectively). We ranked plots by FD values calculated from full datasets and then from our increasingly incomplete datasets and compared the ranking between the original and virtually reduced datasets to assess the accuracy of FD indices when used on datasets with increasingly missing data. Finally, we tested the accuracy of FD indices with and without data transformation, and the effect of missing trait data per plot or per the whole pool of species. FD indices became less accurate as the amount of missing data increased, with the loss of accuracy depending on the index. But, where transformation improved the normality of the trait data, FD values from incomplete datasets were more accurate than before transformation. The distribution of data and its transformation are therefore as important as data completeness and can even mitigate the effect of missing data. Since the effect of missing trait values pool-wise or plot-wise depends on the data distribution, the method should be decided case by case. Data distribution and data transformation should be given more careful consideration when designing, analysing and interpreting FD studies, especially where trait data are missing. To this end, we provide the R package “traitor” to facilitate assessments of missing trait data.

File List glmmeg.R: R code demonstrating how to fit a logistic regression model, with a random intercept term, to randomly generated overdispersed binomial data. boot.glmm.R: R code for estimating P-values by applying the bootstrap to a GLMM likelihood ratio statistic. Description glmm.R is some example R code which show how to fit a logistic regression model (with or without a random effects term) and use diagnostic plots to check the fit. The code is run on some randomly generated data, which are generated in such a way that overdispersion is evident. This code could be directly applied for your own analyses if you read into R a data.frame called “dataset”, which has columns labelled “success” and “failure” (for number of binomial successes and failures), “species” (a label for the different rows in the dataset), and where we want to test for the effect of some predictor variable called “location”. In other cases, just change the labels and formula as appropriate. boot.glmm.R extends glmm.R by using bootstrapping to calculate P-values in a way that provides better control of Type I error in small samples. It accepts data in the same form as that generated in glmm.R.

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

The C2Metadata (“Continuous Capture of Metadata”) Project automates one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software. Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis. Scripts used with statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations. SDTL can be used to add variable-level provenance to data catalogs and codebooks and to create “variable lineages” for auditing software operations. This repository provides examples of scripts and metadata for use in testing C2Metadata tools.

This is an archive of the data contained in the "Transformations" section in PubChem for integration into patRoon and other workflows.

For further details see the ECI GitLab site: README and main "tps" folder.

Credits:

Concepts: E Schymanski, E Bolton, J Zhang, T Cheng;

Code (in R): E Schymanski, R Helmus, P Thiessen

Transformations: E Schymanski, J Zhang, T Cheng and many contributors to various lists!

PubChem infrastructure: PubChem team

Reaction InChI (RInChI) calculations (v1.0): Gerd Blanke (previous versions of these files)

Acknowledgements: ECI team who contributed to related efforts, especially: J. Krier, A. Lai, M. Narayanan, T. Kondic, P. Chirsir, E. Palm. All contributors to the NORMAN-SLE transformations!

March 2025 released as v0.2.0 since the dataset grew by >3000 entries! The stats are:

14 March 2025

Unique Transformation Entries: 10904# Unique Reactions by CID: 9152# Unique Reactions by IK: 9139# Unique Reactions by IKFB: 8574# Unique NORMAN-SLE Compounds by CID: 8207# Unique ChEMBL Compounds by CID: 1419# Unique Compounds (all) by CID: 9267# Unique Predecessors (all) by CID: 3724# Unique Successors (all) by CID: 7331# Range of XlogP Differences: -9.9,10# Range of Mass Differences: -957.97490813,820.227106427

Instructions on how to use the data can be found within the repository.

Differential Coexpression ScriptThis script contains the use of previously normalized data to execute the DiffCoEx computational pipeline on an experiment with four treatment groups.differentialCoexpression.rNormalized Transformed Expression Count DataNormalized, transformed expression count data of Medicago truncatula and mycorrhizal fungi is given as an R data frame where the columns denote different genes and rows denote different samples. This data is used for downstream differential coexpression analyses.Expression_Data.zipNormalization and Transformation of Raw Count Data ScriptRaw count data is transformed and normalized with available R packages and RNA-Seq best practices.dataPrep.rRaw_Count_Data_Mycorrhizal_FungiRaw count data from HtSeq for mycorrhizal fungi reads are later transformed and normalized for use in differential coexpression analysis. 'R+' indicates that the sample was obtained from a plant grown in the presence of both mycorrhizal fungi and rhizobia. 'R-' indicate...

Dataset description

This dataset was created during the research carrried out for the PhD of Negin Afsharzadeh and the subsequent manuscript arising from this research. The main purpose of this dataset is to create a record of the raw data that was used in the analyses in the manuscript.

This dataset includes:

• raw data generated from experiments stored in an Excel spreadsheet with each sheet corresponding to a specific experiment or part of an experiment (Afsharzadeh_et_al_2024.xlsx)
• R script used to analyse the raw data in the software, R (Afsharzadeh_et_al.R)
• datasets that were used to analyse the data in the statistical software, R (germindata.txt, light.txt)

Context and methodology

Brief description of experiments:

In this study, we aimed to optimize approaches to improve the biotechnological production of important metabolites in G. glabra. The study is made up of four experiments that correspond to particular figures/tables in the manuscript and data, as described below.

Experiment 1:

We tested approaches for the cultivation of G. glabra, specifically the breaking of seed dormancy, to ensure timely and efficient seed germination. To do this, we tested the effect of different pretreatments, sterilization treatments and growth media on the germination success of G. glabra.

This experiment corresponds to:

• Manuscript: Table 1 and Figure 1
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_1'); Afsharzadeh_et_al.R; germindata.txt

Experiment 2 (Table 2):

We aimed to optimize the induction of hairy roots in G. glabra. Four strains of R. rhizogenes were tested to identify the most effective strain for inducing hairy root formation and we tested different tissue explants (cotyledons/hypocotyls) and methods of R. rhizogenes infection (injection or soaking for different durations) in these tissues.

This experiment corresponds to:

• Manuscript: Table 2
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_2')

Experiment 3 (Figure 2):

Eight distinct hairy root lines were established and the growth rate of these lines was measured over 40 days.

This experiment corresponds to:

• Manuscript: Figure 2, Table S2
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Figure_2')

Experiment 4 (Figure 3):

We aimed to test different qualities of light on hairy root cultures in order to induce higher growth and possible enhanced metabolite production. A line with a high growth rate from experiment 3, line S, was selected for growth under different light treatments: red light, blue light, and a combination of blue and red light. To assess the overall impact of these treatments, the growth of line S, as well as the increase in antioxidant capacity and total phenolic content, were tracked over this induction period.

This experiment corresponds to:

• Manuscript: Figure 3, Figure S4
• Data: Afsharzadeh_et_al_2024.xlsx (Sheets 'Figure_3_FW', 'Figure_3_FRAP', 'Figure_3_Phenol'); Afsharzadeh_et_al.R; light.txt

Technical details

To work with the .R file and the R datasets, it is necessary to use R: A Language and Environment for Statistical Computing and a package within R, aDHARMA. The versions used for the analyses are R version 4.4.1 and aDHARMA version 0.4.6.

The references for these are:

R Core Team, R: A Language and Environment for Statistical Computing 2024. https://www.R-project.org/

Hartig F, DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models 2022. https://CRAN.R-project.org/package=DHARMa

Abstract This dataset was created within the Bioregional Assessment Programme. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. This dataset contains a set of generic R scripts that are used in the propagation of uncertainty through numerical models. ## Dataset History The dataset contains a set of R scripts that are loaded as a library. The R scripts are used to carry out the propagation of uncertainty through numerical models. The scripts contain the functions to create the statistical emulators and do the necessary data transformations and backtransformations. The scripts are self-documenting and created by Dan Pagendam (CSIRO) and Warren Jin (CSIRO). ## Dataset Citation Bioregional Assessment Programme (2016) R-scripts for uncertainty analysis v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/322c38ef-272f-4e77-964c-a14259abe9cf.

To get the consumption model from Section 3.1, one needs load execute the file consumption_data.R. Load the data for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the data and build the model (starting line 225). The final consumption data can be found in one file for each year in ./data/CONSUMPTION/MEGA_CONS_list.Rdata

To get the results for the optimization problem, one needs to execute the file analyze_data.R. It provides the functions to compare production and consumption data, and to optimize for the different values (PV, MBC,).

To reproduce the figures one needs to execute the file visualize_results.R. It provides the functions to reproduce the figures.

To calculate the solar radiation that is needed in the Section Production Data, follow file calculate_total_radiation.R.

To reproduce the radiation data from from ERA5, that can be found in data.zip, do the following steps: 1. ERA5 - download the reanalysis datasets as GRIB file. For FDIR select "Total sky direct solar radiation at surface", for GHI select "Surface solar radiation downwards", and for ALBEDO select "Forecast albedo". 2. convert GRIB to csv with the file era5toGRID.sh 3. convert the csv file to the data that is used in this paper with the file convert_year_to_grid.R

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

This dataset contains several files related to our research paper titled "Attention Allocation to Projection Level Alleviates Overconfidence in Situation Awareness". These files are intended to provide a comprehensive overview of the data analysis process and the presentation of results. Below is a list of the files included and a brief description of each:

R Scripts: These are scripts written in the R programming language for data processing and analysis. The scripts detail the steps for data cleaning, transformation, statistical analysis, and the visualization of results. To replicate the study findings or to conduct further analyses on the dataset, users should run these scripts.

R Markdown File: Offers a dynamic document that combines R code with rich text elements such as paragraphs, headings, and lists. This file is designed to explain the logic and steps of the analysis in detail, embedding R code chunks and the outcomes of code execution. It serves as a comprehensive guide to understanding the analytical process behind the study.

HTML File: Generated from the R Markdown file, this file provides an interactive report of the results that can be viewed in any standard web browser. For those interested in browsing the study's findings without delving into the specifics of the analysis, this HTML file is the most convenient option. It presents the final analysis outcomes in an intuitive and easily understandable manner. For optimal viewing, we recommend opening the HTML file with the latest version of Google Chrome or any other modern web browser. This approach ensures that all interactive functionalities are fully operational.

Together, these files form a complete framework for the research analysis, aimed at enhancing the transparency and reproducibility of the study.

A highly strained macrocycle comprising four [4]helicene panels, [4]cyclo[4]helicenylene ([4]CH, 1), was synthesized through a one-pot macrocyclization and chemically reduced by alkali metals (Na and K), revealing a four-electron reduction process. The resulting di-, tri-, and tetraanions of compound 1 were isolated and crystallographically characterized by X-ray diffraction. Owing to the four axially chiral bi[4]helicenyl fragments, a reversible stereo transformation of 1 between the (S,R,S,R)- and (S,S,R,R)-configurations was disclosed upon the two-electron uptake, which was rationally understood by theoretical calculations. The (S,S,R,R)-configuration of 12– was further stabilized in triply reduced and tetra-reduced states, where structural deformation led by charges and metal complexation was observed. This study proposed an approach to alter the configuration of cycloarylenes in addition to thermal treatment.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson or negative binomial distribution. Little research has been done to assess how data transformations impact Gaussian model-based clustering with respect to clustering performance and accuracy in estimating the correct number of clusters in RNA-Seq data. In this article, we investigate Gaussian model-based clustering performance and accuracy in estimating the correct number of clusters by applying four data transformations (i.e., naïve, logarithmic, Blom, and variance stabilizing transformation) to simulated RNA-Seq data. To do so, an extensive simulation study was carried out in which the scenarios varied in terms of: how genes were selected to be included in the clustering analyses, size of the clusters, and number of clusters. Following the application of the different transformations to the simulated data, Gaussian model-based clustering was carried out. To assess clustering performance for each of the data transformations, the adjusted rand index, clustering error rate, and concordance index were utilized. As expected, our results showed that clustering performance was gained in scenarios where data transformations were applied to make the data appear “more” Gaussian in distribution.

Variation in observed global generic richness over the Phanerozoic must be partly explained by changes in the numbers of fossils and their geographic spread over time. The influence of sampling intensity (i.e., the number of samples) has been well addressed, but the extent to which the geographic distribution of samples might influence recovered biodiversity is comparatively unknown. To investigate this question, we create models of genus richness through time by resampling the same occurrence dataset of modern global biodiversity using spatially explicit sampling intensities defined by the paleo-coordinates of fossil occurrences from successive time intervals. Our steady-state null model explains about half of observed change in uncorrected fossil diversity and a quarter of variation in sampling-standardized diversity estimates. The inclusion in linear models of two additional explanatory variables associated with the spatial array of fossil data (absolute latitudinal range of occurrences, percent of occurrences from shallow environments) and a Cenozoic step increase the accuracy of steady-state models, accounting for 67% of variation in sampling-standardized estimates and more than one third of the variation in first differences. Our results make clear that the spatial distribution of samples is at least as important as numerical sampling intensity in determining the trajectory of recovered fossil biodiversity through time, and caution the overinterpretation of both the variation and the trend that emerges from analyses of global Phanerozoic diversity. Methods Fossil data were downloaded from the Palebobiology Database and manually cleaned to remove errors (i.e., non-marine organisms being included in the marine dataset). Modern marine invertebrate data were downloaded from the Ocean Biodiversity Information system using the R API. Further data transformations and statistical analyses were performed on the datasets using the R code provided.

In this lesson, students will explore the relationship between reef cover and human disturbance. Students will manipulate a large dataset and perform normality tests, data transformations, correlations, and a simple linear regression in R Studio.

Transformations towards sustainable land systems require leverage points where land-use policies can benefit people and nature. Here, we present a novel approach that identifies and evaluates leverage points along land-use trajectories, which explicitly incorporate path dependency. We apply the approach in the biodiversity hotspot Madagascar, where smallholder agriculture results in a land-use trajectory reaching from old-growth forests via forest fragments and vanilla agroforests to shifting cultivation. Integrating interdisciplinary empirical data on biodiversity, ecosystem functions and agricultural productivity, we assess trade-offs and co-benefits at three leverage points along the trajectory. We find that leverage points are path-dependent: two leverage points target the transformation of old-growth forests and forest fragments to other land uses and result in considerable trade-offs. In contrast, one leverage point allows for the transformation of land under shifting cultivation into agroforests and offers clear co-benefits. Incorporating path-dependency is essential to identify leverage points for sustainable land-use transformations.

Protein-Protein, Genetic, and Chemical Interactions for Quilliam LA (1999):M-Ras/R-Ras3, a transforming ras protein regulated by Sos1, GRF1, and p120 Ras GTPase-activating protein, interacts with the putative Ras effector AF6. curated by BioGRID (https://thebiogrid.org); ABSTRACT: M-Ras is a Ras-related protein that shares approximately 55% identity with K-Ras and TC21. The M-Ras message was widely expressed but was most predominant in ovary and brain. Similarly to Ha-Ras, expression of mutationally activated M-Ras in NIH 3T3 mouse fibroblasts or C2 myoblasts resulted in cellular transformation or inhibition of differentiation, respectively. M-Ras only weakly activated extracellular signal-regulated kinase 2 (ERK2), but it cooperated with Raf, Rac, and Rho to induce transforming foci in NIH 3T3 cells, suggesting that M-Ras signaled via alternate pathways to these effectors. Although the mitogen-activated protein kinase/ERK kinase inhibitor, PD98059, blocked M-Ras-induced transformation, M-Ras was more effective than an activated mitogen-activated protein kinase/ERK kinase mutant at inducing focus formation. These data indicate that multiple pathways must contribute to M-Ras-induced transformation. M-Ras interacted poorly in a yeast two-hybrid assay with multiple Ras effectors, including c-Raf-1, A-Raf, B-Raf, phosphoinositol-3 kinase delta, RalGDS, and Rin1. Although M-Ras coimmunoprecipitated with AF6, a putative regulator of cell junction formation, overexpression of AF6 did not contribute to fibroblast transformation, suggesting the possibility of novel effector proteins. The M-Ras GTP/GDP cycle was sensitive to the Ras GEFs, Sos1, and GRF1 and to p120 Ras GAP. Together, these findings suggest that while M-Ras is regulated by similar upstream stimuli to Ha-Ras, novel targets may be responsible for its effects on cellular transformation and differentiation.