Raymond Carragher (0000-0002-0120-625X), Chris Robertson.
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Open access data on Mosquito Alert participation and sampling effort. This dataset contains data on participation and sampling effort in the Mosquito Alert citizen science system. It can be used for a variety of purposes, including (a) to adjust estimates of mosquito population densities and human-mosquito encounters based on sampling effort, and (b) to better understand the dynamics of citizen scientists' participation. The data is organized spatially by grids of "sampling cells," drawn at intervals of 0.05 degree and 0.025 degree latitude and longitude, and it is based on optional anonymous background tracks from the Mosquito Alert app. The dataset includes raw track counts aggregated in sampling cells, along with estimates of sampling effort based on a model of participants' propensity to send any report as a function of the time elapsed since they first began participating. The repository is hosted on both Zenodo and GitHub and contains the following files: sampling_effort_daily_cellres_025.csv.gz - Daily participation and sampling effort in 0.025 degree sampling cells. sampling_effort_daily_cellres_025_metadata.json - Metadata for the 0.025 degree sampling cell data. sampling_effort_daily_cellres_05.csv.gz - daily participation and sampling effort in 0.05 degree sampling cells. sampling_effort_daily_cellres_05_metadata.json - Metadata for the 0.05 degree sampling cell data. CITATION.cff - Shows how to cite this dataset. LICENSE - License for this dataset. .gitignore - Specifies which files are excluded from the git repository. README.md - This file. .github/workflows/auto-release.yml - Script used for automating releases from GitHub.
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1,4-Bis(bromomethyl)benzene (1.00 g, 3.79 mmol, 1.00 equiv) and triphenylphosphine (2.48 g, 9.47 mmol, 2.50 equiv) were put in suspension in N,N-dimethylformamide (15.0 mL) The solution was heated to 150 ��C for 3 h which provoked the precipitation of the desired product. The solution was then allowed to reach to 21 ��C.
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To a stirred solution of p-tolunitrile (10.0 g, 85 mmol, 1.00 equiv) in 100 mL of CHCl3 was added NBS (15.6 g, 88 mmol, 1.03 equiv) and benzoyl peroxide (2.07 g, 8.5 mmol, 0.100 equiv). The mixture was stirred at 60 °C over night. The mixture was filtered hot and the solvent was evaporated under reduced pressure.
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A 20 mL crimp vial was charged with 2-(diphenylphosphino)pyridine (0.500 g, 1.90 mmol, 1.00 eq.), tris(4-chlorophenyl)phosphine (1.39 g, 3.80 mmol, 2.00 eq.) and CuI (0.723 g, 3.80 mmol, 2.00 eq.) under argon. 15 mL of dry DCM (SPS) were added under argon and the suspension was degassed with argon for 5 min. The reaction mixture was stirred at 25 °C for 15 h. The orange solution was poured dropwise in 300 mL of n-pentane.
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GeneDive is a powerful but easy-to-use application that can search, sort, group, filter, highlight, and visualize interactions between drugs, genes, and diseases (DGR). GeneDive also facilitates topology discovery through the various search modes that reveal direct and indirect interactions between DGR.
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Overview The program was originally designed to quantify the network structure of Physarum polycephalum plasmodia growing across agar surfaces or evacuating from a constrained arena (Fricker et al., 2016). Fully quantitative measurements use transmission imaging to estimate the thickness of the plasmodium using the Lambert-Beer law. The program can determine: The length and width of the veins; The topological organisation of the network; The predicted hydraulic conductivity and accessibility; The predicted flow on the network from a (short) time-series, including adjustment for the intervening plasmodial sheets; The size and shape of the polygonal regions enclosed by the network; Topological measures of the network structure can also be extracted following conversion of the pixel skeleton to a weighted, un-directed graph, where nodes represent junctions and edges represent the veins that connect them. A range of graph-theoretic measures are then calculated, including predicted transport efficiency and betweenness centrality.The simplest method to identify the network would be an intensity-based segmentation of the transmission image to give a binary image, with ones representing the veins and zeros for the background. However, the resultant binary image is critically dependent on the value for the threshold used, and it is rare that a single threshold provides adequate segmentation without either losing dimmer structures if it is set too high, or artificially expanding and fusing adjacent regions if it is set too low. This is particularly problematic with developing networks in Physarum, as the veins form from retraction of the intervening plasmodial sheets, which make segmentation difficult until a well delineated network is present. Thus the approach adopted here exploits additional intensity-independent information over a range of scales and orientations to enhance the network structure, prior to segmentation as a single-pixel wide skeleton. The skeleton is then used as a template to interrogate the image locally to provide an estimate of the relative amount of plasmodium present and to provide an indication of the vein or sheet width. If the initial data is a time series and the entire network has been captured, it is possible to predict the flows in the network from the changes in volume in each edge (and adjacent plasmodial sheet) . This allows comparison of network structure, predicted flow and models, such as Murray's Law (Akita et al., 2016). Installation The program requires a minimum of Windows 64-bit, 8Gb RAM, 1600×900 screen resolution. The program is provided as either a standalone package for Windows (Physarum network.exe) or as a MatLab app (Physarum_network_app.mlappinstall). Full installation of the standalone program requires an internet connection to download and install the appropriate MatLab Runtime Library (currently 2023a) from the MathWorks website. A demo data set is included in the zip file. Copy the demo folder (and sub-folders) to a suitable location, run the Physarum_network program and navigate to the demo folder. Load the demo file, and click on 'Process all'. Hopefully the program will run through a complete analysis of the demo image. References Fricker, M.D., Akita, D., Heaton, L.L.M., Jones, N., Obara, B. and Nakagaki, T. (2017) Automated analysis of Physarum network structure and dynamics. J. Phys. D: Appl. Phys. 50, 254005. Doi:10.1088/1361-6463/aa72b9 Akita, D., Kunita,I., Fricker, M.D., Kuroda, S., Sato, K. and Nakagaki, T. (2016) Models for Murray's Law. J. Phys. D. 50, 024001. Doi: 10.1088/1361-6463/50/2/024001
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Mutational antigenic profiling of Adimab antibodies in the Omicron BA.1 background
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v1.0 repo release for NFACT Repo: the National Food Access and COVID Research Team (NFACT) is a collaborative, interdisciplinary multi-state research effort that uses common measurement tools, codebooks, code, data aggregation tools, and outreach materials to collectively examine and communicate the effect of COVID-19 on household food access and security.
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Minor code fix for final version
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Raymond Carragher (0000-0002-0120-625X), Chris Robertson.
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Open access data on Mosquito Alert participation and sampling effort. This dataset contains data on participation and sampling effort in the Mosquito Alert citizen science system. It can be used for a variety of purposes, including (a) to adjust estimates of mosquito population densities and human-mosquito encounters based on sampling effort, and (b) to better understand the dynamics of citizen scientists' participation. The data is organized spatially by grids of "sampling cells," drawn at intervals of 0.05 degree and 0.025 degree latitude and longitude, and it is based on optional anonymous background tracks from the Mosquito Alert app. The dataset includes raw track counts aggregated in sampling cells, along with estimates of sampling effort based on a model of participants' propensity to send any report as a function of the time elapsed since they first began participating. The repository is hosted on both Zenodo and GitHub and contains the following files: sampling_effort_daily_cellres_025.csv.gz - Daily participation and sampling effort in 0.025 degree sampling cells. sampling_effort_daily_cellres_025_metadata.json - Metadata for the 0.025 degree sampling cell data. sampling_effort_daily_cellres_05.csv.gz - daily participation and sampling effort in 0.05 degree sampling cells. sampling_effort_daily_cellres_05_metadata.json - Metadata for the 0.05 degree sampling cell data. CITATION.cff - Shows how to cite this dataset. LICENSE - License for this dataset. .gitignore - Specifies which files are excluded from the git repository. README.md - This file. .github/workflows/auto-release.yml - Script used for automating releases from GitHub.
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1,4-Bis(bromomethyl)benzene (1.00 g, 3.79 mmol, 1.00 equiv) and triphenylphosphine (2.48 g, 9.47 mmol, 2.50 equiv) were put in suspension in N,N-dimethylformamide (15.0 mL) The solution was heated to 150 ��C for 3 h which provoked the precipitation of the desired product. The solution was then allowed to reach to 21 ��C.
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To a stirred solution of p-tolunitrile (10.0 g, 85 mmol, 1.00 equiv) in 100 mL of CHCl3 was added NBS (15.6 g, 88 mmol, 1.03 equiv) and benzoyl peroxide (2.07 g, 8.5 mmol, 0.100 equiv). The mixture was stirred at 60 °C over night. The mixture was filtered hot and the solvent was evaporated under reduced pressure.
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A 20 mL crimp vial was charged with 2-(diphenylphosphino)pyridine (0.500 g, 1.90 mmol, 1.00 eq.), tris(4-chlorophenyl)phosphine (1.39 g, 3.80 mmol, 2.00 eq.) and CuI (0.723 g, 3.80 mmol, 2.00 eq.) under argon. 15 mL of dry DCM (SPS) were added under argon and the suspension was degassed with argon for 5 min. The reaction mixture was stirred at 25 °C for 15 h. The orange solution was poured dropwise in 300 mL of n-pentane.
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GeneDive is a powerful but easy-to-use application that can search, sort, group, filter, highlight, and visualize interactions between drugs, genes, and diseases (DGR). GeneDive also facilitates topology discovery through the various search modes that reveal direct and indirect interactions between DGR.
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Overview The program was originally designed to quantify the network structure of Physarum polycephalum plasmodia growing across agar surfaces or evacuating from a constrained arena (Fricker et al., 2016). Fully quantitative measurements use transmission imaging to estimate the thickness of the plasmodium using the Lambert-Beer law. The program can determine: The length and width of the veins; The topological organisation of the network; The predicted hydraulic conductivity and accessibility; The predicted flow on the network from a (short) time-series, including adjustment for the intervening plasmodial sheets; The size and shape of the polygonal regions enclosed by the network; Topological measures of the network structure can also be extracted following conversion of the pixel skeleton to a weighted, un-directed graph, where nodes represent junctions and edges represent the veins that connect them. A range of graph-theoretic measures are then calculated, including predicted transport efficiency and betweenness centrality.The simplest method to identify the network would be an intensity-based segmentation of the transmission image to give a binary image, with ones representing the veins and zeros for the background. However, the resultant binary image is critically dependent on the value for the threshold used, and it is rare that a single threshold provides adequate segmentation without either losing dimmer structures if it is set too high, or artificially expanding and fusing adjacent regions if it is set too low. This is particularly problematic with developing networks in Physarum, as the veins form from retraction of the intervening plasmodial sheets, which make segmentation difficult until a well delineated network is present. Thus the approach adopted here exploits additional intensity-independent information over a range of scales and orientations to enhance the network structure, prior to segmentation as a single-pixel wide skeleton. The skeleton is then used as a template to interrogate the image locally to provide an estimate of the relative amount of plasmodium present and to provide an indication of the vein or sheet width. If the initial data is a time series and the entire network has been captured, it is possible to predict the flows in the network from the changes in volume in each edge (and adjacent plasmodial sheet) . This allows comparison of network structure, predicted flow and models, such as Murray's Law (Akita et al., 2016). Installation The program requires a minimum of Windows 64-bit, 8Gb RAM, 1600×900 screen resolution. The program is provided as either a standalone package for Windows (Physarum network.exe) or as a MatLab app (Physarum_network_app.mlappinstall). Full installation of the standalone program requires an internet connection to download and install the appropriate MatLab Runtime Library (currently 2023a) from the MathWorks website. A demo data set is included in the zip file. Copy the demo folder (and sub-folders) to a suitable location, run the Physarum_network program and navigate to the demo folder. Load the demo file, and click on 'Process all'. Hopefully the program will run through a complete analysis of the demo image. References Fricker, M.D., Akita, D., Heaton, L.L.M., Jones, N., Obara, B. and Nakagaki, T. (2017) Automated analysis of Physarum network structure and dynamics. J. Phys. D: Appl. Phys. 50, 254005. Doi:10.1088/1361-6463/aa72b9 Akita, D., Kunita,I., Fricker, M.D., Kuroda, S., Sato, K. and Nakagaki, T. (2016) Models for Murray's Law. J. Phys. D. 50, 024001. Doi: 10.1088/1361-6463/50/2/024001
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