Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data. The tutorial is published in a blog for The Node (https://thenode.biologists.com/converting-excellent-spreadsheets-tidy-data/education/)

This file contains the Fourier Transform Infrared Spectroscopy (FTIR) Spectroscopy Data from NOAA R/V Ronald H. Brown ship during VOCALS-REx 2008.

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.

Fisheries management is generally based on age structure models. Thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. The otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. It contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. The traditional human reading method to determine age is very time-consuming. Automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. Using the routinely measured otolith data of plaice (Pleuronectes platessa; Linnaeus, 1758) and striped red mullet (Mullus surmuletus; Linnaeus, 1758) in the eastern English Channel and north-east Arctic cod (Gadus morhua; Linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. Contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. To finalize this standardization process, all images were resized and binarized. Several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. For this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). This method was approved to these three species and could be applied for others species for age determination and stock identification.

Credit report of Transform Sr Llc 3333 Beverly R contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

A luminescent Eu(III) chiral coordination polymer with a structural transformation system, [Eu(hfa)3((R)-bidp)]n (hfa: haxafluoroacetylacetonato, (R)-bidp: (R)-1,1′-binaphthyl-2,2′-bis(diphenylphosphinate), is reported. Single-crystal X-ray analysis revealed a characteristic helical polymer structure of [Eu(hfa)3((R)-bidp)]n with hydrogen–fluorine/π interactions. [Eu(hfa)3((R)-bidp)]n shows high thermostability (decomposition temperature = 320 °C) and strong luminescence properties (the 4f–4f emission quantum yield = 76%) in the solid state due to its tight packing and asymmetric structure. [Eu(hfa)3((R)-bidp)]n is also transformed from a polymer to monomer structure in liquid media. The chiroptical properties of the monomer form in liquid media were characterized by using circular dichroism and circularly polarized luminescence spectra. In this study, structural and photophysical properties of a luminescent Eu(III) chiral coordination polymer with a structural transformation system were demonstrated.

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

Replication data for working paper: A more efficient approach to converting ASCII files and cleaning data in R with the speedycode package

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.

This data and code archive contains the following files and folders:

* README
Description: text file with this description

* flowchart.pdf
Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.

* runAll.sh
Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)

* Folder "DataRaw"
Description: folder for raw data files
This folder contains the following files:

- DataRaw/COWS.xlsx
Description: MS-Excel file with the number of cows per county
Source: USDA NASS Quickstats
Observations: All available counties and years from 2002 to 2012

- DataRaw/milk_state.xlsx
Description: MS-Excel file with average monthly milk yields per cow
Source: USDA NASS Quickstats
Observations: All available states from 1981 to 2018

- DataRaw/TMAX.csv
Description: CSV file with daily maximum temperatures
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/VPD.csv
Description: CSV file with daily maximum vapor pressure deficits
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/countynamesandID.csv
Description: CSV file with county names, state FIPS codes, and county FIPS codes
Source: US Census Bureau
Observations: All counties

- DataRaw/statecentroids.csv
Descriptions: CSV file with latitudes and longitudes of state centroids
Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function
Observations: All states

* Folder "DataGenerated"
Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Results"
Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Figures"
Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.

* Folder "Tables"
Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.

* Folder "logFiles"
Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.

* PrepareCowsData.R
Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses

* PrepareWeatherData.R
Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses

* PrepareMilkData.R
Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses

* CalcFrequenciesTHI_Temp.R
Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state

* CalcAvgTHI.R
Description: R script that calculates the average THI in each state

* PreparePanelTHI.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins

* PreparePanelTemp.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins

* PreparePanelFinal.R
Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses

* EstimateTrendsTHI.R
Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set

* EstimateModels.R
Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications

* CalcCoefStateYear.R
Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification

* SearchWeightMonths.R
Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term

* TestModelSpec.R
Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10

* CreateFigure1a.R
Description: R script that creates subfigure a of Figure 1

* CreateFigure1b.R
Description: R script that creates subfigure b of Figure 1

* CreateFigure2a.R
Description: R script that creates subfigure a of Figure 2

* CreateFigure2b.R
Description: R script that creates subfigure b of Figure 2

* CreateFigure2c.R
Description: R script that creates subfigure c of Figure 2

* CreateFigure3.R
Description: R script that creates the subfigures of Figure 3

* CreateFigure4.R
Description: R script that creates the subfigures of Figure 4

* CreateFigure5_TableS6.R
Description: R script that creates the subfigures of Figure 5 and Table S6

* CreateFigureS1.R
Description: R script that creates Figure S1

* CreateFigureS2.R
Description: R script that creates Figure S2

* CreateTableS2_S3_S7.R
Description: R script that creates Tables S2, S3, and S7

* CreateTableS4_S5.R
Description: R script that creates Tables S4 and S5

* CreateTableS8.R
Description: R script that creates Table S8

* CreateTableS9.R
Description: R script that creates Table S9

Lactoylglutathione lyase (GLO1) catalyses the transformation of methylglyoxal (MGXL) and glutathione (GSH) to (R)-S-lactoylglutathione ((R)-S-LGSH), an intermediate in pyruvate metabolism. MGXL is a reactive 2-oxoaldehyde byproduct of normal metabolism that is a carcinogen and a mutagen (Ridderstrom et al. 1998). This is the first step in the glyoxalase system, a critical two-step detoxification system for MGXL.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

In this project we have reviewed existing methods used to homogenize data and developed several new methods for dealing with this diversity in survey questions on the same subject. The project is a spin-off from the World Database of Happiness, the main aim of which is to collate and make available research findings on the subjective enjoyment of life and to prepare these data for research synthesis. The first methods we discuss were proposed in the book ‘Happiness in Nations’ and which were used at the inception of the World Database of Happiness. Some 10 years later a new method was introduced: the International Happiness Scale Interval Study (HSIS). Taking the HSIS as a basis the Continuum Approach was developed. Then, building on this approach, we developed the Reference Distribution Method.

Salicylate (ST) and ATP react with coenzyme A to form salicylate-CoA (ST-CoA), AMP, and pyrophosphate in a reaction catalyzed by xenobiotic/medium-chain fatty acid:CoA ligase (Vessey et al. 2003).

Plasmids have been identified in most species of Rickettsia examined, with some species maintaining multiple different plasmids. Three distinct plasmids were demonstrated in Rickettsia amblyommii AaR/SC by Southern analysis using plasmid specific probes. Copy numbers of pRAM18, pRAM23 and pRAM32 per chromosome in AaR/SC were estimated by real-time PCR to be 2.0, 1.9 and 1.3 respectively. Cloning and sequencing of R. amblyommii AaR/SC plasmids provided an opportunity to develop shuttle vectors for transformation of rickettsiae. A selection cassette encoding rifampin resistance and a fluorescent marker was inserted into pRAM18 yielding a 27.6 kbp recombinant plasmid, pRAM18/Rif/GFPuv. Electroporation of Rickettsia parkeri and Rickettsia bellii with pRAM18/Rif/GFPuv yielded GFPuv-expressing rickettsiae within 2 weeks. Smaller vectors, pRAM18dRG, pRAM18dRGA and pRAM32dRGA each bearing the same selection cassette, were made by moving the parA and dnaA-like genes from pRAM18 or pRAM32 into a vector backbone. R. bellii maintained the highest numbers of pRAM18dRGA (13.3 – 28.1 copies), and R. parkeri, Rickettsia monacensis and Rickettsia montanensis contained 9.9, 5.5 and 7.5 copies respectively. The same species transformed with pRAM32dRGA maintained 2.6, 2.5, 3.2 and 3.6 copies. pRM, the plasmid native to R. monacensis, was still present in shuttle vector transformed R. monacensis at a level similar to that found in wild type R. monacensis after 15 subcultures. Stable transformation of diverse rickettsiae was achieved with a shuttle vector system based on R. amblyommii plasmids pRAM18 and pRAM32, providing a new research tool that will greatly facilitate genetic and biological studies of rickettsiae.

Abstract: This repository provides different artifacts developed in and used for the evaluation of the dissertation "Building Transformation Networks for Consistent Evolution of Interrelated Models". It serves as a reproduction package for the contributions and evaluations of that thesis. The artifacts comprise an approach to evaluate compatibility of QVT-R transformations, evaluations of interoperability issues in transformation networks and approaches to avoid them, a language to define consistency between multiple models, and an evaluation of this language. The package contains a prepared execution environment for the different artifacts. In addition, it provides a script to run the environment for some of the artifacts and automatically resolve all dependencies based on Docker. TechnicalRemarks: Instructions on how to use the data can be found within the repository.

R Code for transformation SVS coordinates to the orthomosaic coordinate system

Normally in humans, adenine generated in processes such as polyamine biosynthesisis can be salvaged by conversion to AMP, catalyzed by APRT (adenine phosphoribosyltransferase). In the absence of APRT activity, however, accumulated adenine is instead converted to 2,8-dioxo-adenine. Accumulation of insoluble crystals of 2,8-dioxo-adenine in the kidneys causes the kidney damage that is a major symptom of APRT deficiency in humans (Van Acker et al. 1977; Bollée et al. 2012). Three missense mutant alleles are annotated here (Chen et al. 1991; Hidaka et al. 1988; Sahota et al. 1994); nonsense, insertion-deletion, and splice-site mutations have also been reported (reviewed by Bollée et al. 2012).

Protein-Protein, Genetic, and Chemical Interactions for Morrison KB (2002):ETV6-NTRK3 transformation requires insulin-like growth factor 1 receptor signaling and is associated with constitutive IRS-1 tyrosine phosphorylation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Congenital fibrosarcoma (CFS) and cellular mesoblastic nephroma (CMN) are pediatric spindle cell malignancies that share two specific cytogenetic abnormalities: trisomy of chromosome 11 and a t(12;15)(p13;q25) translocation. The t(12;15) rearrangement creates a transcriptionally active fusion gene that encodes a chimeric oncoprotein, ETV6-NTRK3 (EN). EN transforms NIH3T3 fibroblasts through constitutive activation of both the Ras-mitogen-activated protein kinase (MAPK) pathway and the phosphatidylinositol-3'kinase (PI3K)-Akt pathway. However, the role of trisomy 11 in CFS and CMN remains unknown. In this study we demonstrate elevated expression of the chromosome 11p15.5 insulin-like growth factor 2 gene (IGF2) in CFS and CMN tumors. Moreover, we present evidence that an intact IGF signaling axis is essential for in vitro EN-mediated transformation. EN only very weakly transformed so-called R-murine fibroblasts derived from mice with a targeted disruption of the IGF1 receptor gene (IGFRI), but transformation activity was fully restored in R- cells engineered to re-express IGFRI (R+ cells). We also observed that the major IGFRI substrate, insulin-receptor substrate-1 (IRS-1), was constitutively tyrosine phosphorylated and could be co-immunoprecipitated with EN in either R- or R+ cells expressing the EN oncoprotein. IRS-1 association with Grb2 and PI3K p85, which link IGFRI to the Ras-MAPK and PI3K-Akt pathways, respectively, was enhanced in both cell types in the presence of EN. However, activation of the Ras-MAPK and PI3K-Akt pathways was markedly attenuated in EN-expressing R- cells compared to EN-transformed R+ cells. This suggests that IRS-1 may be functioning as an adaptor in EN signal transduction, but that a link to EN transformation pathways requires the presence of IGFRI. Our findings indicate that an intact IGF signaling axis is essential for EN transformation, and are consistent with a role for trisomy 11 in augmenting this pathway in EN expressing tumors.

Common vetch (Vicia sativa) is a multi-purpose legume widely used in pasture and crop rotation systems. Vetch seeds have desirable nutritional characteristics and are often used to feed ruminant animals. Although transcriptomes are available for vetch, problems with genetic transformation and plant regeneration hinder functional gene studies in this legume species. Therefore, the aim of this study was to develop a simple, efficient and rapid hairy root transformation system for common vetch to facilitate functional gene analysis. At first, we infected the hypocotyls of 5-day-old in vitro or in vivo, soil-grown seedlings with Rhizobium rhizogenes K599 using a stabbing method and produced transgenic hairy roots after 24 days at 19 and 50% efficiency, respectively. We later improved the hairy root transformation in vitro by infecting different explants (seedling, hypocotyl-epicotyl, and shoot) with R. rhizogenes. We observed hairy root formation at the highest efficiency in shoot and hypocotyl-epicotyl explants with 100 and 93% efficiency, respectively. In both cases, an average of four hairy roots per explant were obtained, and about 73 and 91% of hairy roots from shoot and hypocotyl-epicotyl, respectively, showed stable expression of a co-transformed marker β-glucuronidase (GUS). In summary, we developed a rapid, highly efficient, hairy root transformation method by using R. rhizogenes on vetch explants, which could facilitate functional gene analysis in common vetch.

In our previous study, it was shown that Riemerella anatipestifer, a Gram-negative bacterium, is naturally competent, but the genes involved in the process of natural transformation remain largely unknown. In this study, a random transposon mutant library was constructed using the R. anatipestifer ATCC11845 strain to screen for the genes involved in natural transformation. Among the 3000 insertion mutants, nine mutants had completely lost the ability of natural transformation, and 14 mutants showed a significant decrease in natural transformation frequency. We found that the genes RA0C_RS04920, RA0C_RS04915, RA0C_RS02645, RA0C_RS04895, RA0C_RS05130, RA0C_RS05105, RA0C_RS09020, and RA0C_RS04870 are essential for the occurrence of natural transformation in R. anatipestifer ATCC11845. In particular, RA0C_RS04895, RA0C_RS05130, RA0C_RS05105, and RA0C_RS04870 were putatively annotated as ComEC, DprA, ComF, and RecA proteins, respectively, in the NCBI database. However, RA0C_RS02645, RA0C_RS04920, RA0C_RS04915, and RA0C_RS09020 were annotated as proteins with unknown function, with no homology to any well-characterized natural transformation machinery proteins. The homologs of these proteins are mainly distributed in the members of Flavobacteriaceae. Taken together, our results suggest that R. anatipestifer encodes a unique natural transformation machinery.