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2,031 results for “transformation”
Modifying plant photosynthesis and growth via simultaneous chloroplast transformation of Rubisco large and small subunits
<p>Engineering improved Rubisco poses a crucial strategy for enhancing photosynthesis but is challenged by the alternate locations of the plastome rbcL gene and nuclear RbcS genes. Here we develop a RNAi-RbcS Nicotiana tabacum (tobacco) master-line, tobRrΔS, amenable to rbcL-rbcS co-engineering by chloroplast transformation. Four tobacco genotypes coding alternative rbcS genes and adjoining 5ˈ-intergenic sequences revealed Rubisco production was highest in the lines incorporating a rbcS gene whose codon use and 5ˈUTR matched rbcL. These lines produced up to 50% the wild-type Rubisco content. Additional tobacco lines coding potato rbcL-rbcS operons examined how the differing mesophyll made small subunits (pS1, pS2, pS3) or the trichome pST-25 subunit influenced Rubisco biogenesis, catalysis, leaf physiology and plant growth. Rubisco levels were ~15% lower in leaves expressing pS3 relative to those producing pS1, pS2, and pST. However, the pS3-subunit increased carboxylation rate (kcatC) by 13% and carboxylation efficiency (CE, kcatC divided by the Km for CO2) by 17% relative to the pS1 and pS2 subunits as a result of the βa-βb loop substitutions Asn-55-His and Lys-57-Ser. By contrast tobacco photosynthesis and growth were most impaired in lines producing the pST-subunit that reduced CE and CO2/O2 specificity of potato Rubisco by 40% and 15% respectively.<br> </p>
Data for "Shear zone development in serpentinised mantle: Implications for the strength of oceanic transform faults"
<p>Data (structural data used in serpentinite shear zone maps, Raman point analysis data, and EDS point analysis for oxide weight percents) used in the paper "Shear zone development in serpentinised mantle: Implications for the strength of oceanic transform faults"</p>
Data from: Epidermal growth factor receptor and transforming growth factor-ß signaling contributes to variation for wing shape in Drosophila melanogaster
Wing development in Drosophila is a common model system for the dissection of genetic networks and their roles during development. In particular, the RTK and TGF-ß regulatory networks appear to be involved with numerous aspects of wing development, including patterning, cell determination, growth, proliferation, and survival in the developing imaginal wing disc. However, little is known as to how subtle changes in the function of these genes may contribute to quantitative variation for wing shape, per se. In this study 50 insertional mutations, representing 43 loci in the RTK, Hedgehog, TGF-ß pathways, and their genetically interacting factors were used to study the role of these networks on wing shape. To concurrently examine how genetic background modulates the effects of the mutation, each insertion was introgressed into two wild-type genetic backgrounds. Using geometric morphometric methods, it is shown that the majority of these mutations have profound effects on shape but not size of the wing when measured as heterozygotes. To examine the relationships between how each mutation affects wing shape hierarchical clustering was used. Unlike previous observations of environmental canalization, these mutations did not generally increase within-line variation relative to their wild-type counterparts. These results provide an entry point into the genetics of wing shape and are discussed within the framework of the dissection of complex phenotypes.
Data from: Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation
Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within‐habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual‐based species abundance data or replicated, sample‐based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within‐habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.
Web-based content organization and the transformation of traditional classification systems
<p> Traditional hierarchical classification systems were designed to optimize the management, findability and use of physical collections of objects. But as more and more collections of objects are being accessed and used on the Web, these predominant classification models have been modified by facetted taxonomies with semantic relationships. The diverse uses of information require specialized classification strategies that reach beyond simple use cases. The ubiquity of web search engines and hypertext is also leading to new interest in labelling and describing named entities. This paper discusses these developments and provides examples for their utility in a variety of organizations, profit and non-profit.</p>
The impact of transformational leadership on university lecturers' organizational performance in China
<p>Raw data for "The impact of transformational leadership on university lecturers’ organizational performance in China".</p>
Figure 2 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 2 Template of the online whiteboard for the handbook workshops (translated into English).
Figure 3 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 3 Flashlight at the end of the DF transfer workshop; photo: Torsten Görg.
Figure 4 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 4 On-site excursion during the SLS transfer workshop; photo: Nicole Herzog.
TRANSFORMATIONS IN TRANSLATION POLYCOMPONENTIAL COMPOSITE SENTENCES FROM ENGLISH INTO UZBEK
Open the record for dataset details and reuse information.
Supplementary data: "Speed of technological transformations required in Europe to achieve different climate goals"
<p>This repository includes results discussed in the paper "<a href="https://arxiv.org/abs/2109.09563">Speed of technological transformations required in Europe to achieve different climate goals</a>"</p> <p>In the paper, we used the open energy modelling framework <a href="https://pypsa.org/">PyPSA</a>, the model <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec v0.5.0</a> and the cost and technology assumptions included in the repository <a href="https://github.com/PyPSA/technology-data">technology-data v0.2.0</a></p> <p>The directory 'version-baseline' includes the network objects obtained as an outcome of the optimization for the different years and carbon budgets in the baseline scenario.</p> <p>This dataset is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Fig. 8 in The Diversity Of Cuculiform And Piciform Species In Partly Transformed Riparian Zambezi Forest
Fig. 8. Distribution of breeding pairs of barbets in Zambezi riparian forest.
Fig. 4 in The Diversity Of Cuculiform And Piciform Species In Partly Transformed Riparian Zambezi Forest
Fig. 4. Monthly rainfall in Katima Mulilo in 2014 and 2015.
Fig. 3 in The Diversity Of Cuculiform And Piciform Species In Partly Transformed Riparian Zambezi Forest
Fig. 3. Zambezi forest fringing Zambezi River.
Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses (Replication Package)
<p>Replication package for the ASE'22 paper "Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses"</p>
EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics
<h1>EchoGPT</h1> <p>This folder contains the supplementary data and code for the submission "EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics" to the NeurIPS 2024 conference.</p> <h2>Dependencies</h2> <p>Matlab 2024a or higher is needed with the following toolboxes:</p> <ul> <li>Image Processing Toolbox</li> <li>Parallel Computing Toolbox</li> <li>Deep Learning Toolbox</li> <li>Signal Processing Toolbox</li> <li>System Identification Toolbox</li> </ul> <h2>Data</h2> <h3>Simulation</h3> <p>This folder contains the saved sonar images (energyscapes) as well as the motion data for each frame from a particular simulation run (LongRun1). These are saved into batches and saved as compressed .mat files within the <em>\DataCalculated\SimulationData\LongRun1\Raw</em> folder.</p> <h3>Trained model</h3> <p>The model used in the experiments of this submission is saved in <em>\DataCalculated\Networks</em> as a Matlab dlnetwork object. There is a trained (with weights) and an untrained version available.</p> <h2>Code</h2> <h3>Data pre-processing</h3> <p>To extract the data from the batch .mat files into seperate frames a script <strong>preprocessEchoPT.m</strong> is available in the main folder. This will save these individual frames once again to individual .mat files into a folder <em>\DataCalculated\ESSequences</em> by default.</p> <h3>Training</h3> <p>To train the model a Matlab script <strong>trainEchoPT.m</strong> is provided.</p> <h3>Evaluation</h3> <p>To generate the figures of the submission as well as additional outputs such as GIFs three different evaluation scripts are available:</p> <ul> <li><strong>evaluateEchoPT.m</strong>: Evaluate the trained model on the dataset.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_ARLoop.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow with autoregressive prediction.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_NoAR.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow without autoregressive prediction.</li> </ul> <h3>Source</h3> <p>Within the folder <em>\Source</em> all additional Matlab functions and classes can be found.</p> <h2>Open-Source libraries included in this project</h2> <ul> <li>Progress bar by HyunGwang Cho <a href="https://www.mathworks.com/matlabcentral/fileexchange/121363-progress-bar-cli-gui-parfor?s_tid=srchtitle">(link)</a></li> </ul>
Graph D2A: D2A Dataset for Vulnerability Detection Transformed into ECPG Format for Training GNNs
<p>The Graph D2A is based on the dataset 'D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis'. Graph D2A was created by transforming individual samples from D2A into the form of Extended Code Property Graphs (ECPGs). The resulting dataset is designed for graph neural network models (GNNs) for detection on vulnerabilities in source code.</p> <p>Graph D2A was developed as part of the Master's thesis 'Evaluating Reliability of Static Analysis Results Using Machine Learning' at BUT FIT.</p>
Code and data for 'Alpha, beta and gamma diversity in intact, mixed and transformed landscapes'
<p>The data and scripts in this database are analyses for a research:<strong> </strong>Alpha, beta and gamma diversity in intact, mixed and transformed landscapes. Please refer to the README file and the paper for details about the usage of the data and methodology.</p> <p>Software: R 4.1.0</p>
Figure 4 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 4. – Automatic otolith image orientation method.
Figure 6 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 6. – Example of otolith image standardization for the three species.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.