wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions

Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open-source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate-simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud-computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.journal articl

Inhibition of Post-Synaptic Kv7/KCNQ/M Channels Facilitates Long-Term Potentiation in the Hippocampus

Activation of muscarinic acetylcholine receptors (mAChR) facilitates the induction of synaptic plasticity and enhances cognitive function. In the hippocampus, M1 mAChR on CA1 pyramidal cells inhibit both small conductance Ca2+-activated KCa2 potassium channels and voltage-activated Kv7 potassium channels. Inhibition of KCa2 channels facilitates long-term potentiation (LTP) by enhancing Ca2+calcium influx through postsynaptic NMDA receptors (NMDAR). Inhibition of Kv7 channels is also reported to facilitate LTP but the mechanism of action is unclear. Here, we show that inhibition of Kv7 channels with XE-991 facilitated LTP induced by theta burst pairing at Schaffer collateral commissural synapses in rat hippocampal slices. Similarly, negating Kv7 channel conductance using dynamic clamp methodologies also facilitated LTP. Negation of Kv7 channels by XE-991 or dynamic clamp did not enhance synaptic NMDAR activation in response to theta burst synaptic stimulation. Instead, Kv7 channel inhibition increased the amplitude and duration of the after-depolarisation following a burst of action potentials. Furthermore, the effects of XE-991 were reversed by re-introducing a Kv7-like conductance with dynamic clamp. These data reveal that Kv7 channel inhibition promotes NMDAR opening during LTP induction by enhancing depolarisation during and after bursts of postsynaptic action potentials. Thus, during the induction of LTP M1 mAChRs enhance NMDAR opening by two distinct mechanisms namely inhibition of KCa2 and Kv7 channels

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

Automatic analysis of dual-channel Droplet Digital PCR experiments

The ability to quantify the amount of DNA in a sample is an essential technique in biology and is used in many fields of research. Droplet digital polymerase chain reaction (ddPCR) is an advanced technology developed for this purpose that enables more accurate and sensitive quantification than traditional real-time PCR. In ddPCR, nucleic acid (e.g., genomic DNA) within a sample is partitioned into thousands of droplets, along with the reagents needed to amplify and detect one or more DNA target sequences. After amplification takes place in all droplets, each droplet is individually read by a two-colour fluorescence detection system to determine whether or not it contains the target sequence. ddPCR experiments utilizing both fluorescence wavelengths are termed dual-channel, while simpler experiments can make use of only one fluorescence wavelength and are thus classified as single-channel. Droplets containing amplified product exhibit high fluorescence and are said to be positive, while those without product show little or no fluorescence and are considered negative. Using this binary, or digital, classification of droplets, the number of positive and negative droplets can be counted to allow for an absolute quantification of template abundance in the starting sample. ddPCR instruments are now available commercially and their use is growing. But, there are a very limited number of tools available for downstream data analysis. The key step in ddPCR data analysis is droplet gating: using the end-point fluorescence data to gate, or classify, droplets as either positive or negative for a given template. The proprietary software provided by BioRad Inc., a ddPCR instrument manufacturer, is currently the only program available to automatically analyze dual-channel ddPCR data. However, because this analysis tool often produces poor results, many ddPCR users resort to time-consuming and subjective manual data analyses, emphasizing the clear need for new ddPCR analysis tools. In this thesis, I devise an algorithm for automatic analysis of dual-channel ddPCR data that can objectively and reproducibly perform droplet gating. The proposed analysis method has been implemented in an R package and is also available as a web application online for easy and open access to any ddPCR user.Science, Faculty ofGraduat

rsalad : an R package with a mix of tools to improve data analysis efficiency

This R package is a collection of useful functions to make data analysis in R easier.Science, Faculty ofStatistics, Department ofUnreviewedGraduat

ddpcr: an R package and web application for analysis of droplet digital PCR data

<div><br></div>An R package that provides an interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. It also includes an interactive web application with a visual user interface to facilitate analysis for anyone who is not comfortable with using R. The app is <a href="http://daattali.com/shiny/ddpcr/">available online</a> or it can be <a href="https://github.com/daattali/ddpcr#r-interactive">run locally</a>

Visualizing cancer data in the United States

The software provided here is an interactive application that allows users to explore data about cancer incidences/deaths in the US. The data was taken from the US CDC. The software was written in the R programming language and made available as a web application via the Shiny R package. The software allows the user to filter the data by different metrics and visualize the data in different ways.Science, Faculty ofStatistics, Department ofUnreviewedGraduat