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Human Microbiome Action - Social Media Templates
<p>This comprehensive resource pack includes ready-to-use messages in PNG format and versatile templates for Twitter/X, LinkedIn, and Instagram available as PowerPoint files. Designed to streamline your communication efforts, these templates enable you to effectively share the latest advancements and insights in human microbiome research with your audience.</p>
B2K template images for neuroimaging in baboon
<p><strong>B2K template images for neuroimaging in baboon</strong><br> <a href="http://purl.org/net/kbmd">Black KJ</a>, Snyder AZ, Koller JM, Gado MH, Perlmutter JS<br> Washington University School of Medicine, St. Louis, MO<br> 28 July 2000 (last updated 15 February 2001)<br> <em>please reference this page as </em><a href="http://purl.org/net/kbmd/b2k">purl.org/net/kbmd/b2k</a></p> <p><strong>Brief description</strong><br> The b2k and b2kf images ("b" for baboon, "f" for blood flow) are intended for use as templates for automated 3D image registration algorithms for neuroimaging stidies in baboons. The b2k template is an average (after spatial normalization) of T1-weighted MRI images from 9 normal baboons, and the b2kf template is averaged from 397 [<sup>15</sup>O]water PET images in 7 normal baboons. Coordinates from the 1968 Davis and Huffman photomicrographic atlas of baboon brain can be derived from pixel coordinates in these images according to the conventions described in the attached <strong>appendix.html</strong> file (they are automatically computed by SPM based on the supplied .hdr files). Details of the images' formation and a test of the accuracy of our image registration software when registering MPRAGE and PET blood flow images to these templates appear in <a href="http://doi.org/10.1006/nimg.2001.0752">NeuroImage</a>. The GIF files show a quick view of one slice of the template images. Corresponding images are also available for use with <em>Macaca nemestrina</em> (the pig-tailed <a href="http://purl.org/net/kbmd/n2k">macaque</a>) and <em>Macaca fascicularis</em> (<a href="http://purl.org/net/kbmd/cyno/">cynomolgus</a> monkey).</p> <p><strong>Terms of use</strong><br> Cite the following reference in any publications that make use of these images: Black KJ, Snyder AZ, Koller JM, Gado MH, Perlmutter JS: <a href="http://doi.org/10.1006/nimg.2001.0752">Template images for nonhuman primate neuroimaging 1. Baboon</a>. NeuroImage 2001; 14(3):736-743. DOI 10.1006/nimg.2001.0752</p> <p><strong>Download instructions</strong><br> 1) choose desired image format<br> 2) download one of the image files in that row (choose .zip or .tar.gz)<br> 3) the *.txt and/or *.ifh files are in ASCII format and explain image conventions. Highly suggested reading!<br> 4) I would like to know if you find these images useful. Please email me (<a href="mailto:kevin@wustl.edu">kevin@wustl.edu</a>).</p> <p><strong>SPM format</strong> (Neurologic orientation, R on R) <a href="3D"spm/b2k_spm_images.zip"">b2k_spm_images.zip</a> <a href="3D"spm/b2k_spm_images.tar.gz"">b2k_spm_images.tar.gz</a></p> <p><strong>Analyze format = 4dint format</strong> (Radiologic orientation, R on L) <a href="3D"ana/b2k_ana_images.zip"">b2k_ana_images.zip</a> <a href="3D"ana/b2k_ana_images.tar.gz"">b2k_ana_images.tar.gz</a></p> <p><strong>4dfp format</strong> (WUSM NIL internal format) <a href="3D"4dfp/b2k_4dfp_images.zip"">b2k_4dfp_images.zip</a> <a href="3D"4dfp/b2k_4dfp_images.tar.gz"">b2k_4dfp_images.tar.gz</a></p> <p><sup>*</sup>The "tight" mask approximates actual brain and was created by hand from b2k.4dfp.img using Analyze AVW. Its volume is 185 ml (185479 voxels). <em>Note: Bonferroni correction for overall p < 0.05 with 185479 independent tests is p < 2.70 x 10<sup>-7</sup>, corresponding to a Z score of about 5.14 (two tails) or 5.01 (one tail).</em> The loose mask (used for image registration with our local software) was created by dilating the tight mask slightly; its volume is 242 ml (241739 voxels).</p>
DsecF Drosophila sechellia Female Template Brain
<p>An nc82-stained averaged brain constructed from 26 female D. sechellia brains. Voxel size: (0.461, 0.461, 1) microns</p>
DsecM Drosophila sechellia Male Template Brain
<p>An nc82-stained averaged brain constructed from 21 male D. sechellia brains. Voxel size: (0.461, 0.461, 1) microns</p>
Mapping Template
<p>Language-Independent Mapping Template for Knowledge Graph Creation</p>
Excitatory synaptic structural abnormalities produced by templated aggregation of α-syn in the basolateral amygdala
<p>Tabular raw data for each graph in the manuscript</p>
Focused learning by antibody language models using preferential masking of non-templated regions
<p><strong>Motivation.</strong> While existing antibody language models (AbLMs) excel at predicting germline residues, they often struggle with mutated and non-templated residues, which concentrate in the complementarity-determining regions (CDRs) and are crucial for determining antigen-binding specificity. Many of these models are trained using a masked language modeling (MLM) objective with uniform masking probabilities; however, antibody recombination is modular in nature, creating relatively distinct regions of high and low complexity (non-templated and templated, respectively). We sought to determine whether and to what extent AbLMs can improve when trained using an alternative masking strategy based on this observation.</p> <p><strong>Results.</strong> We developed a variation on MLM called <strong><em>Preferential Masking</em></strong>, which alters masking probabilities to amplify training signals from the CDR3. We pre-trained two AbLMs using either uniform or preferential masking and observed that the latter improves pre-training efficiency and residue prediction accuracy in the highly variable CDR3. Preferential masking also improves antibody classification by native chain pairing and binding specificity, suggesting improved CDR3 understanding and indicating that non-random, learnable patterns help govern antibody chain pairing. We further show that specificity classification is largely informed by residues in the CDRs, demonstrating that AbLMs learn meaningful patterns that align with immunological understanding.</p> <p><strong>Files. </strong>The following files are included in this repository:</p> <ul> <li><strong><em>uniform_250k.tar.gz</em></strong>: Model weights for the Uniform-250k model.</li> <li><strong><em>uniform_350k.tar.gz</em></strong>: Model weights for the Uniform-350k model.</li> <li><strong><em>preferential_250k.tar.gz</em></strong>: Model weights for the Preferential-250k model.</li> <li><strong><em>train-eval-test_cdr-mask.tar.gz</em></strong>: Datasets used to train all three models above. Compressed folder containing three files: <em>A_train.csv</em>, <em>A_eval.csv</em>, and <em>B_test.csv</em>. Each row contains a natively paired sequence with its corresponding label-encoded CDR mask, designed to align with the tokenized amino acid sequence. Sequences were obtained from <a href="https://doi.org/10.1038/s41586-022-05371-z">Jaffe et al.</a> and <a href="https://doi.org/10.1016/j.celrep.2024.114307">Hurtado et al</a>. These are referenced in the paper as Dataset A (<em>A_train.csv, A_eval.csv)</em>, and Dataset B (<em>B_test.csv</em>)<em>.</em></li> <li><strong><em>test-set_annotations.tar.gz</em></strong>: Unpaired annotations for all test set (Dataset B) sequences: <em>B_test-set_annotations.csv</em>. Used for Fig. 3 and Fig. 4D. Annotations can be mapped back to the paired sequences using their `sequence_id` and `locus` information.</li> <li><strong><em>pair_classification.tar.gz</em></strong>: Two classification datasets used to train the classifier models in Figure 4: <em>C_native-0_shuffled-1.csv</em> (Dataset C) and <em>D_native-0_shuffled-1.csv</em> (Dataset D). Dataset C sequences were obtained from <a href="https://doi.org/10.1038/s41586-022-05371-z">Jaffe et al.</a> and <a href="https://doi.org/10.1016/j.celrep.2024.114307">Hurtado et al</a> (Dataset B), and Dataset D sequences were obtained from <a href="https://doi.org/10.1038/s41590-022-01230-1">Phad et al</a> and data generated as part of this study.</li> <li><strong><em>CoV_classification.tar.gz</em></strong>: Classification dataset used to train the classifier models in Figure 5: <em>E_hd-0_cov-1.csv </em>(Dataset E). CoV antibody sequences were obtained from <a href="https://doi.org/10.1093/bioinformatics/btaa739">CoV-AbDAb</a>, and healthy donor sequences were obtained from <a href="https://doi.org/10.1038/s41590-022-01230-1">Phad et al</a>.</li> </ul> <p><strong>Code.</strong> All code used for model training, testing, and figure generation is available under the MIT license on <a href="https://github.com/brineylab/preferential-masking-paper">GitHub.</a></p> <p> </p>
Molten Salt Templated Synthesis of Covalent Isocyanurate Frameworks with Tunable Morphology and High CO2 Uptake Capacity
<p>The use of reactive molten salts, i.e., ZnCl2, as a soft template and a catalyst has been actively investigated in the preparation of covalent triazine frameworks (CTFs). Although the soft templating effect of the salt melt is more prominent at low<br> temperatures, close to the melting point of ZnCl2, leading to the formation of abundant micropores, a significant mesopore<br> formation is observed that is due to the partial carbonization and other side reactions at higher temperatures (>400 °C).<br> Evidently, high-temperature synthesis of CTFs in various eutectic salt mixtures of ZnCl2 with alkali metal chloride salts also leads to mesopore formation. We reasoned that using the isocyanate moieties instead of cyano groups in the monomer, 1,4-phenylene isocyanate, could enable efficient interactions between carbonyl moieties and alkali metal ions to realize efficient salt templating to form covalent isocyanurate frameworks (CICFs). In this direction, the trimerization of 1,4-phenylene diisocyanate was carried out under ionothermal conditions at different reaction temperatures using ZnCl2 (CICF) and the eutectic salt mixture of KCl/NaCl/ZnCl2 (CICF-KCl/NaCl) as the reactive solvents. We observed notable differences in the morphologies of the two polymers, whereas CICF showed irregular-shaped micrometer-sized particles, the CICF-KCl/NaCl exhibited a filmlike morphology. Moreover, favorable ion-dipole interactions between alkali metal cations and oxygen atoms of the monomer facilitated two-dimensional growth and the formation of a purely microporous framework in the case of CICF-KCl/NaCl along with a near theoretical retention of the nitrogen content at 500 °C. The CICF-KCl/NaCl showed a BET surface area of 590 m2 g−1 along with a CO2 uptake capacity of 5.9 mmol g−1 at 273 K and 1.1 bar because of its high microporosity and nitrogen content. On the contrary, in the absence of alkali metal ions, CICF showed high mesopore content and a moderate CO2 uptake capacity. This study underscores the importance of the strength of the interactions between the salts and the monomer in the ionothermal synthesis to control the morphology,<br> porosity, and gas uptake properties of the porous organic polymers</p>
MAED template Reconstruction Base Year - Manufacturing (Industry Sector) and Household Sector (Urban & Rural)
<p>MAED template Reconstruction Base Year - Manufacturing (Industry Sector) and Household Sector (Urban & Rural) for the Hands-on 6 of the Open Learn Create an online course on MAED. The Template for "MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector)" is available for download here: https://doi.org/10.5281/zenodo.7750256. </p>
MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector)
<p>MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector) for Hands-on 5 of the Open Learn Create Course on MAED. </p>
Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)
<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>
The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization
<h1>The SIGMA templates and atlases for the Wistar Rat Brain</h1> <p>The current document is a short description of the second version of the SIGMA resources for the Wistar rat brain. For a full description of the resources and the methodologies used to create them please consult the main publication [Barriere D.A. et al 2019].</p> <p>The SIGMA resources are a set of standardized MRI compatible templates and atlases meant to support the analysis of multimodal MRI data of the rat brain. They were developped as part of the SIGMA project, a collaborative project between French (CEA and INSERM) and Portuguese (ICVS) institutions (FCT-ANR/NEU-OSD/0258/2012). They provide a unified and standardized framework for the analysis of multimodal rat brain imaging data, allowing the reporting of results within the coordinate system of the Paxinos-Watson atlas.</p> <p>In this second version, standardized MRI compatible templates have been built from the original acquired data (11.7 Tesla Bruker Scanner at Neuropsin center <a href="https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx" rel="nofollow">https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx</a>) and emulated using the methods developed by Gabriel A. Devenyi (<a href="https://github.com/gdevenyi">https://github.com/gdevenyi</a>) and available here : <a href="https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction">https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction</a>. This pipeline is a re-implementation of the ANTs template construction pipeline requiring ANTs for the primary commands, and running on our cluster facilities using qbatch (<a href="https://islande.hub.inrae.fr/infrastructure" rel="nofollow">https://islande.hub.inrae.fr/infrastructure</a>).</p> <p>Using this methodology we firstly, updated the previous SIGMA spaces (T2sw, T2w, T1w) previously generated using the DARTEL Methods implemented in SPM8 and normalized the whole head images instead of brain.</p> <p>Secondly, we updated the probabilistic maps of the rat brain which are mandatory for the automatic segmentation of the rat brain and standardisation of morphometric analysis. Namely, we created new maps of Grey Matter, White Matter, CSF, Skull and outbrain. Those maps allow the use of SIGMA with the latter release of SPM12, a popular neuroimaging software dedicated to brain imaging analysis but also with ANTs, FSL and AFNI. Additionnally, we revised the Grey Matter/White Matter segmentations since the limits of which (particularly at the thalamic level) were a matter to debate with some users in the previous version of SIGMA.</p> <p>Thirdly, additionnal templates have been created using the optimized ANTs methodology to create from original unpublished data diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions.</p> <p>Eventually, using the same strategy, we created a CT/18FDG reference space from data obtained previously [Barrière D.A. et al 2018] which has been normalized with the MRI ex-vivo SIGMA template allowing to the SIGMA resource to propose a multimodal space for CT/TEP/MRI normalisation.</p> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#organisation-of-the-sigma-resources"></a></h2> <h2>Organisation of the SIGMA resources</h2> <p>The SIGMA resources have been organized as four sections : anatomical Imaging, functional imaging, atlases and TEP/CT imaging</p> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#anatomical-imaging"></a></h3> <h3>Anatomical Imaging</h3> <p>In this section a set of templates, priors and brain masks is available for ex-vivo and in-vivo data normalization</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-t2-weighted"></a></h4> <h4>Ex-vivo T2*-weighted</h4> <p>T2*-weighted template + T2*-weighted map + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_csf.nii.gz SIGMA_ExVivo_Anatomical_Brain_gm.nii.gz SIGMA_ExVivo_Anatomical_Brain_mask.nii.gz SIGMA_ExVivo_Anatomical_Brain_out.nii.gz SIGMA_ExVivo_Anatomical_Brain_skull.nii.gz SIGMA_ExVivo_Anatomical_Brain_t2starmap.nii.gz SIGMA_ExVivo_Anatomical_Brain_template.nii.gz SIGMA_ExVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-diffusion"></a></h4> <h4>Ex-vivo diffusion</h4> <p>B0 template + FA template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.25x0.25x0.25mm.</p> <div> <pre><code> SIGMA_ExVivo_Diffusion_Brain_b0.nii.gz SIGMA_ExVivo_Diffusion_Brain_csf.nii.gz SIGMA_ExVivo_Diffusion_Brain_fa.nii.gz SIGMA_ExVivo_Diffusion_Brain_gm.nii.gz SIGMA_ExVivo_Diffusion_Brain_mask.nii.gz SIGMA_ExVivo_Diffusion_Brain_out.nii.gz SIGMA_ExVivo_Diffusion_Brain_skull.nii.gz SIGMA_ExVivo_Diffusion_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-t2-weighted"></a></h4> <h4>In-vivo T2-weighted</h4> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_csf.nii.gz SIGMA_InVivo_Anatomical_Brain_gm.nii.gz SIGMA_InVivo_Anatomical_Brain_mask.nii.gz SIGMA_InVivo_Anatomical_Brain_out.nii.gz SIGMA_InVivo_Anatomical_Brain_skull.nii.gz SIGMA_InVivo_Anatomical_Brain_template.nii.gz SIGMA_InVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-diffusion"></a></h4> <h4>In-vivo diffusion</h4> <p>T2-weighted template + B0 template + FA template + ADC template + brain mask.</p> <p>Spatial resolution 0.375x0.375x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Diffusion_Brain_adc.nii.gz SIGMA_InVivo_Diffusion_Brain_b0.nii.gz SIGMA_InVivo_Diffusion_Brain_fa.nii.gz SIGMA_InVivo_Diffusion_Brain_mask.nii.gz SIGMA_InVivo_Diffusion_Brain_t2.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#functional-imaging"></a></h3> <h3>Functional Imaging</h3> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Functional_Brain_csf.nii.gz SIGMA_InVivo_Functional_Brain_epi.nii.gz SIGMA_InVivo_Functional_Brain_gm.nii.gz SIGMA_InVivo_Functional_Brain_mask.nii.gz SIGMA_InVivo_Functional_Brain_t2.nii.gz SIGMA_InVivo_Functional_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-rat-brain-atlas-version-20--waxholm-atlas-feat-sigma"></a></h3> <h3>SIGMA Rat Brain Atlas Version 2.0 : Waxholm atlas Feat. SIGMA</h3> <p>In this second version of the SIGMA resources we deliver a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Methods (2023, <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41592-023-02034-3.pdf" target="_blank" rel="noopener"></a>). The Waxholm rat brain atlas is currently the best numerical 3D atlas of the rat brain. In accordance with authors of this paper we are authorized to modify and embed the WHS atlas within the SIGMA environement to standardize the identification of brain territories. We provide a normalized version the WHS for both ex-vivo and in-vivo of the anatomical SIGMA templates. Finally, we offer linear and non-linear transformations to enable your data to commute between the SIGMA and WHS ex-vivo environments using ANTs commands.</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-atlas"></a></h4> <h4>Ex-vivo atlas</h4> <p>WHS rat brain atlas normalized in ex-vivo T2*-weighted SIGMA template + List of 222 labels created in ITKSnap Format + linear and non-linear transformations for SIGMA-WHS journeys (WHS-to-SIGMA_Transformations folder).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_ExVivo_Anatomical_Brain_Atlas.txt ./WHS-to-SIGMA_Transformations/reference_SIGMA.nii.gz ./WHS-to-SIGMA_Transformations/reference_WHS.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_InverseWarp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_Warp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_02_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_03_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS_SD_rat_atlas_v4.nii.gz ./WHS-to-SIGMA_Transformations/WHS-to-SIGMA_byANTS.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-atlas"></a></h4> <h4>In-vivo atlas</h4> <p>WHS rat brain atlas normalized in in-vivo T2 SIGMA anatomical template + List of 222 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_InVivo_Anatomical_Brain_Atlas.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-brain-meshes"></a></h4> <h4>SIGMA brain meshes</h4> <p>Rat brain mesh created using BrainNet viewers commands in matlab (<a href="https://www.nitrc.org/projects/bnv/" rel="nofollow">https://www.nitrc.org/projects/bnv/</a>).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_Anatomical_Brain_Atlas_mesh.nv </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-functional-atlas"></a></h4> <h4>SIGMA functional atlas</h4> <p>In the original publication of the SIGMA resources, we developed a functional atlas for the rat brain, using a group ICA analysis validated through a RAICAR approach. From this analysis, we identified 59 bilateral ROIs covering cortical, sub-cortical and brainstem structures that are functionally distinct. Despite having been derived from purely functional data, this atlas broadly, if not precisely, correlates with the general anatomical boundaries and many are associated with specific anatomical structures. A primary motivation for the creation of this atlas is derived from the need to perform brain segmentations which is optimized for functional MRI analysis, since the signal sources do not necessarily match typical anatomical boundaries. A similar requirement has been identified by those performing human studies, resulting in efforts to generate more diverse, multi-modal atlases.</p> <p>SIGMA rat brain functional atlas normalized in in-vivo T2 SIGMA functional template + List of 59 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_Functional_Brain_Atlas_Labels.txt SIGMA_Functional_Brain_Atlas_ListOfStructures.csv SIGMA_InVivo_Functional_Brain_Atlas.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-cttep-template"></a></h4> <h4>SIGMA CT/TEP template</h4> <p>In this version of the SIGMA resources we included a CT/TEP template built from the data that previously published (Barriere D.A. et al 2018 , Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5) and acquired on a Triumph™ PET/CT dual modality imaging platform (Gamma Medica, Inc., Northridge, CA, USA), consisting of a LabPET™ avalanche photodiode-based digital PET scanner with a 7.5 cm axial field-of-view capable of achieving an isotropic spatial resolution. A caudal injection of approximately 30 MBq of [18F]-FDG was applied followed by a static acquisition to evaluate [18F]-FDG uptake within brain. CT images were acquired from the high-resolution X-ray computed tomography (CT) modality. Images were reconstructed using the Triumph™ PET/CT software. using the following parameters: 20 iterations, span of 63, field of view of 80 mm with a final matrix resolution of 160 × 160 × 128 and a voxel size of 0.5 × 0.5 × 0.597 mm. Brain dynamic [18F]-FDG images were reconstructed using the same protocol but we generated 32 frames (10 for 5 s, 7 for 10 s, 6 for 30 sec, 6 for 120 s, 2 for 240 s and 1 for 300 s). [18F]-FDG images were reconstructed using 3-D MLEM algorithm providing 0.5 × 0.5 × 0.597 mm images. CT scans were reconstructed using the standard FBP kernel analytical reconstruction algorithms, providing an isotropic image of 512 slices with a final resolution of 0.165 µm isotropic. Both [18F]-FDG and CT data were spatially normalized to the SIGMA ex-vivo template using the previously described methods.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_InVivo_18FDG_Brain_template.nii.gz SIGMA_InVivo_CT_Brain_template.nii.gz </code></pre> <div> </div> </div> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#important-note"></a></h2> <h2>Important Note</h2> <p>SIGMA ressources are provided at the scanner resolution and are oriented in anterior commisure/posterior commisure axis. Center of the images have been set at the anterior commisure level (Bregma 0 mm). Nevertheless, users are invited to increase the resolution of the current images for using in SPM or FSL for accurate coregistration and normalization steps (we recommand x10 increasing). No manipulation of image resolution are required with ANTs. Not tested with AFNI.</p> <p>For any questions regarding the SIGMA ressource, please email the SIGMA Team (<a href="mailto:sigma.preclinical.resources@gmail.com">sigma.preclinical.resources@gmail.com</a>) or Email directly David A. Barrière (<a href="mailto:david.barriere@cnrs.fr">david.barriere@cnrs.fr</a>).</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#references"></a></h1> <h1>REFERENCES</h1> <p>Barrière, D.A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat Commun 10, 5699 (2019). <a href="https://doi.org/10.1038/s41467-019-13575-7" rel="nofollow">https://doi.org/10.1038/s41467-019-13575-7</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41467-019-13575-7.pdf" target="_blank" rel="noopener"></a></p> <p>Kleven, H. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods 20, 1822–1829 (2023). <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a></p> <p>Barrière, D.A. et al. Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications. Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5. PMID: 29323186; PMCID: PMC5765114.</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#related-works-using-the-sigma-ressources"></a></h1> <h1>RELATED WORKS USING THE SIGMA RESSOURCES</h1> <p>Grandjean J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023 Apr;26(4):673-681. doi: 10.1038/s41593-023-01286-8. Epub 2023 Mar 27. Erratum in: Nat Neurosci. 2023 Jun;26(6):1127-1128. PMID: 36973511; PMCID: PMC10493189.</p> <p>Vidal B. et al. Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging. J Neurosci Methods. 2021 May 1;355:109139. doi: 10.1016/j.jneumeth.2021.109139. Epub 2021 Mar 16. PMID: 33741345.</p> <p>Barrière D.A. et al. Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey. Br J Pharmacol. 2020 Apr;177(8):1773-1792. doi: 10.1111/bph.14934. Epub 2020 Jan 22. PMID: 31734950; PMCID: PMC7070177</p> <p>Barrière D.A. et al. Structural and functional alterations in the retrosplenial cortex following neuropathic pain. Pain. 2019 Oct;160(10):2241-2254. doi: 10.1097/j.pain.0000000000001610. PMID: 31145220.</p> <p>Magalhães, R. et al Resting-State Functional MR Imaging and Spectroscopy Study of the Dorsal Hippocampus in the Chronic Unpredictable Stress Rat Model. J Neurosci. 2019 May 8;39(19):3640-3650. doi: 10.1523/JNEUROSCI.2192-18.2019. Epub 2019 Feb 25. PMID: 30804096; PMCID: PMC6510342.</p> <p>Magalhães, R. et al The dynamics of stress: a longitudinal MRI study of rat brain structure and connectome. Mol Psychiatry. 2018 Oct;23(10):1998-2006. doi: 10.1038/mp.2017.244. Epub 2017 Dec 5. PMID: 29203852.</p>
Regulation of T7 gp2.5 Binding Dynamics by its C-terminal tail, Template Conformation and Sequence
<p>Bacteriophage T7 single-stranded DNA-binding protein (gp2.5) binds to and protects transiently exposed regions of single-stranded DNA (ssDNA) while dynamically interacting with other proteins of the replication complex. We directly visualize fluorescently labelled T7 gp2.5 binding to ssDNA at the single-molecule level. Upon binding, T7 gp2.5 reduces the contour length of ssDNA by stacking nucleotides in a force-dependent manner, suggesting T7 gp2.5 suppresses the formation of secondary structure. Next, we investigate the binding dynamics of T7 gp2.5 and a deletion mutant lacking 21 Cterminal residues (gp2.5-Δ21C) under various template tensions. Our results show that the base sequence of the DNA molecule, ssDNA conformation induced by template tension, and the acidic terminal domain from T7 gp2.5 significantly impact on the DNA binding parameters of T7 gp2.5. Moreover, we uncover a unique template-catalyzed recycling behaviour of T7 gp2.5, resulting in an apparent cooperative binding to ssDNA, facilitating efficient spatial redistribution of T7 gp2.5 during the synthesis of successive Okazaki fragments. Overall, our findings reveal an efficient binding mechanism that prevents the formation of secondary structures by enabling T7 gp2.5 to rapidly rebind to nearby exposed ssDNA regions, during lagging strand DNA synthesis.</p>
Regulation of T7 gp2.5 Binding Dynamics by its C-terminal tail, Template Conformation and Sequence
<p><sup>* </sup>To whom correspondence may be addressed: g.j.l.wuite@vu.nl<br> <sup># </sup>The first three authors should be regarded as joint first authors</p> <p><strong>Abstract</strong><br> Bacteriophage T7 single-stranded DNA-binding protein (gp2.5) binds to and protects transiently exposed regions of single-stranded DNA (ssDNA) while dynamically interacting with other proteins of the replication complex. We directly visualize fluorescently labelled T7 gp2.5 binding to ssDNA at the single-molecule level. Upon binding, T7 gp2.5 reduces the contour length of ssDNA by stacking nucleotides in a force-dependent manner, suggesting T7 gp2.5 suppresses the formation of secondary structure. Next, we investigate the binding dynamics of T7 gp2.5 and a deletion mutant lacking 21 C-terminal residues (gp2.5-Δ21C) under various template tensions. Our results show that the base sequence of the DNA molecule, ssDNA conformation induced by template tension, and the acidic terminal domain from T7 gp2.5 significantly impact on the DNA binding parameters of T7 gp2.5. Moreover, we uncover a unique template-catalyzed recycling behaviour of T7 gp2.5, resulting in an apparent cooperative binding to ssDNA, facilitating efficient spatial redistribution of T7 gp2.5 during the synthesis of successive Okazaki fragments. Overall, our findings reveal an efficient binding mechanism that prevents the formation of secondary structures by enabling T7 gp2.5 to rapidly rebind to nearby exposed ssDNA regions, during lagging strand DNA synthesis.</p> <p><strong>Data Set Description</strong><br> This data set contains the raw data and analysis code for the single-molecule study of the binding dynamics of T7 gp2.5 and its interactions with single-stranded DNA (ssDNA). The experiments utilized a custom-built setup combining dual optical trapping, confocal microscopy, and microfluidics.</p> <p><strong>Methodology</strong><br> The single-molecule experiments were performed at room temperature in a 5-channel microfluidic flow cell using a custom-built experimental setup that combines dual optical trapping, confocal microscopy, and microfluidics for single-molecule assays. Data analysis was conducted using Python, Origin, and MATLAB. Detailed methodology and instrument specifications are provided in the associated publication.</p> <p><strong>Data Set Structure</strong><br> - `Raw_Data/`: Contains raw data files in TDMS format.<br> - `Data_Analysis_Code/`: Contains the code used for data analysis and figure generation.</p> <p><strong>File Formats</strong><br> - Raw data files are in TDMS format.</p> <p><strong>Usage Notes</strong><br> Along with the raw data, we also provided the analysis code which generates the figures. Users should be aware of the required citations, license terms, and ethical considerations when using this data set.</p> <p><strong>Acknowledgments</strong><br> This work was financially supported by: ‘Crowd management: The physics of genome processing in complex environments’ of Stichting voor Fundamenteel Onderzoek der Materie, China Scholarship Council (funding No. 201704910912) and the European Union H2020 Marie-Sklowdowska Curie International Training Network AntiHelix, Grant Agreement n. 859853.</p> <p><strong>Author Contributions</strong><br> J.C-D., L X., M.T.J.H., and G.J.L.W. conceptualized the research; J.C-D., and L.X. collected data; I.H. built the combined optical trapping and confocal microscope instrument and developed the MATLAB code for the analysis of kymograph data; S.A.S. and A.v.O. provided purified wild type T7 gp2.5 and tested their biochemical activity; S-J L. provided purified gp2.5-Δ21C and tested their biochemical activity; L X. J.C-D., and M.T.J.H. analyzed the data; J.C-D., L X., M.T.J.H., and G.J.L.W. wrote the manuscript; G.J.L.W. supervised the project; the manuscript is read, revised and confirmed by all the<br> </p>
Online supplement to manuscript: "Ability of ChatGPT to generate competent radiology reports for distal radius fracture by use of RSNA template items and integrated AO classifier." Current problems in diagnostic radiology (2023).
<p>Online supplement to manuscript: </p> <p>Bosbach, Wolfram A., Jan F. Senge, Bence Nemeth, Siti H. Omar, Milena Mitrakovic, Claus Beisbart, András Horváth, Johannes Heverhagen, and Keivan Daneshvar. "Ability of ChatGPT to generate competent radiology reports for distal radius fracture by use of RSNA template items and integrated AO classifier." <em>Current problems in diagnostic radiology</em> (2023). <a href="https://doi.org/10.1067/j.cpradiol.2023.04.001">doi.org/10.1067/j.cpradiol.2023.04.001</a></p>
curatedPCaData metadata template (PRAD) tsv-file
<p>This tab-separated value table lists the clinical metadata template, which was applied to extract relevant metadata for the cohorts presented in the curatedPCaData R-package.</p>
Data Collection and Manipulation Template for FlexTool
<p>This repository includes the following files:</p> <p><strong>1) FlexTool_Data_Collection_template.xlsx: </strong>Workbook for inserting raw data, manipulating it and preparing FlexTool input sheets. The data collection and manipulation template for IRENA FlexTool 2.0 serves as a quick and standardized methodology for putting together a single-node country model in FlexTool.<br> <strong>2) FlexTool_Starter_template.xlsm: </strong>Template FlexTool input file structured to receive the input sheets, as prepared in the FlexTool_Data_Collection_template.xlsx<br> <strong>3) Data Collection Template for IRENA FlexTool.pdf</strong>: Guidelines for preparing a FlexTool model based on the Data Collection and Manipulation template. </p> <p><strong>4) OSeMOSYS and FlexTool data sharing</strong>: Instructions on how to populate the FlexTool input file from the OSeMOSYS SAND file<br> FlexTool data file population:</p> <p><strong>5) Instructions on how to populate the FlexTool Data Collection File from the OSeMOSYS Data Collection File</strong>.</p>
Figure 1 in Revisiting the ideas of trees as templates and the competition paradigm in pairwise analyses of ground-dwelling ant species occurrences in a tropical forest
Figure 1 Location of REGUA in the State of Rio de Janeiro, Brazil.
Excel template process mapping and FMEA
<p>A FMEA analysis tool with prebuild radiation oncology process steps and sub steps that can be modified based on clinical workflow. Also contains an instruction for use and a sample completed document.</p>
Graph models underlying templates for annotating a study in ecology and evolution
<p><em>Schematic representation of the graph models, resources and links to ontologies underlying a set of templates designed to annotate studies in invasion biology, and more generally ecology or evolution. The main template is the “General scoping” template, while each other box would allow a more detailed description of Dataset, Study system, Study design, and research question and hypotheses in Invasion biology. This figure was presented during the <a href="https://hiknowledgeworkshops.com/workshop-2/">June 2023 workshop of the Hi Knowledge initiative</a>. </em></p>
ScienceDex guides
Understand access before you commit
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.