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Agricultural Residue Burning Emissions 2019 v1
<p>The data provides the agricultural residue burning emission estimates over 11 agroclimatic zones of Madhya Pradesh, India, for the 2019 rabi season. It also includes supplementary information used in the emission estimation for the reader's reference. </p> <p>References:</p> <p>1. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3)https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html </p> <p>2. Gupta, P. K.; Sahai, S.; Singh, N.; Dixit, C. K.; Singh, D. P.; Sharma, C.; Tiwari, M. K.; Gupta, R. K.; Garg, S. C. Residue Burning in Rice-Wheat Cropping System: Causes and Implications. Curr. Sci. 2004, 87 (12).</p> <p>3. Jain, N.; Bhatia, A.; Pathak, H. Emission of Air Pollutants from Crop Residue Burning in India. Aerosol Air Qual. Res. 2014, 14 (1).<a href="https://doi.org/10.4209/aaqr.2013.01.0031"> https://doi.org/10.4209/aaqr.2013.01.0031</a>.</p> <p>4. Venkatramanan, V.; Shah, S.; Rai, A. K.; Prasad, R. Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India. Front. Energy Res. 2021, 8.<a href="https://doi.org/10.3389/fenrg.2020.614212"> https://doi.org/10.3389/fenrg.2020.614212</a>.</p> <p>5. Van Der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.; Morton, D. C.; Defries, R. S.; Jin, Y.; Van Leeuwen, T. T. Global Fire Emissions and the Contribution of Deforestation, Savanna, Forest, Agricultural, and Peat Fires (1997-2009). Atmos. Chem. Phys. 2010, 10 (23). <a href="https://doi.org/10.5194/acp-10-11707-2010">https://doi.org/10.5194/acp-10-11707-2010</a>.</p>
Data for - EU's bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol
<p>To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU’s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU’s “advanced bioethanol” potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector’s energy consumption. EU’s advanced bioethanol has a land footprint of 0.28 m<sup>2</sup> MJ<sup>−1</sup> for wheat straw and 0.18 m<sup>2</sup> MJ<sup>−1</sup> for maize stover. The average water footprint of advanced bioethanol is 173 L MJ<sup>−1</sup> for wheat straw and 113 L MJ<sup>−1</sup> for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO<sub>2</sub>eq MJ<sup>−1</sup> for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU’s advanced bioethanol production potential is insufficient to achieve EU’s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.</p>
High quality protein residues: Top2018 all-atom-filtered residues
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue. For the mainchain-only filtered set, see https://doi.org/10.5281/zenodo.4626149</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue‐level filtering, and the Top2018 best‐parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_fullll_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filted.csv and in USER records at the end on each .pdb file.</p> <p>All header information from the original structure has been preserved. This includes information about chains and residues no longer present in the file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned and included residues with USER records. These include self-documenting USER DOC lines as follow:<br> USER DOC Lines marked with USER DEL list residues pruned by<br> USER DOC quality filtering.<br> USER DOC Format is chain:resseq:icode:reason_for_pruning<br> USER DOC Reasons for pruning are abbreviated as 1-letter codes: bcmgoa<br> USER DOC b=bfactor, c=real space correlation, m=2Fo-Fc mapvalue<br> USER DOC g=geometry outlier, o=steric overlap, a=alternate conformations<br> USER DOC Lines marked USER INC list the uninterrupted fragments of structure<br> USER DOC still included after pruning by quality filtering<br> USER DOC Format is chain1:resseq1:icode1:chain2:resseq2:icode2:fragment_length<br> USER DOC where 1 is the first and 2 the last residue of the fragment<br> USER DOC Line marked with USER PCT gives statistics for structure completeness</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 1.0 10.5281/zenodo.5115233 Jul 19, 2021<br> Initial version</p> <p>Version 2.0<br> Removed ~6000 additional residues due to mainchain atom clashes<br> Set case of filenames to unambiguous standard: all lowercase except L</p>
High quality protein residues: Top2018 mainchain-filtered residues
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on mainchain atoms. For the full-residue filtered set, see https://doi.org/10.5281/zenodo.5115232</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue‐level filtering, and the Top2018 best‐parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. As long as the question concerns mainchain protein atoms, these files should be usable as is. There is a separate version that has been filtered on all atoms that is suitable for sidechains.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_mcfilter_60pct_complete.txt and found in top2018_pdbs_mc_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_mc_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_mc_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_mcfilter_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_mc_filted.csv and in USER records at the end on each .pdb file.</p> <p>All header information from the original structure has been preserved. This includes information about chains and residues no longer present in the file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p>Sidechain atoms beyond CB have not been considered in the filtering. However, all sidechains have been included for residues that passed the mainchain filters. DO NOT use this set of files for serious questions involving sidechains. See our all-atom filtered dataset instead.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>Mainchain atoms are defined as N, CA, C, O, CB.<br> Note that CB is included, since its ideal position is defined by the other mainchan atoms.</p> <p>All mainchain atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p><br> Filtering doumentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned and included residues with USER records. These include self-documenting USER DOC lines as follow:<br> USER DOC Lines marked with USER DEL list residues pruned by<br> USER DOC quality filtering.<br> USER DOC Format is chain:resseq:icode:reason_for_pruning<br> USER DOC Reasons for pruning are abbreviated as 1-letter codes: bcmgoa<br> USER DOC b=bfactor, c=real space correlation, m=2Fo-Fc mapvalue<br> USER DOC g=geometry outlier, o=steric overlap, a=alternate conformations<br> USER DOC Lines marked USER INC list the uninterrupted fragments of structure<br> USER DOC still included after pruning by quality filtering<br> USER DOC Format is chain1:resseq1:icode1:chain2:resseq2:icode2:fragment_length<br> USER DOC where 1 is the first and 2 the last residue of the fragment<br> USER DOC Line marked with USER PCT gives statistics for structure completeness</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9 10.5281/zenodo.4626150 Mar 21, 2021<br> Initial version</p> <p>Version 1.0 10.5281/zenodo.5115075 Jul 19, 2021<br> Split into 30, 50, 70, and 90% homology sets</p> <p>Version 2.0<br> Set case of filenames to unambiguous standard: all lowercase except L</p> <p>Version 2.01</p> <p>Added missing chain list for recommended hom70 set</p>
Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?
<p>Datasets related to publication: </p> <p>Shevchenko E, Pantsar T: Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?. <em><em>Biochem Soc Trans</em></em> 28 February 2022; 50 (1): 633–648</p> <p>https://doi.org/10.1042/bst20210837</p> <p> </p> <p> </p>
High quality protein residues: Top2018 mainchain-filtered residues - mmCIF
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University. This version contains files in mmCIF format.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on mainchain atoms.</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue-level filtering, and the Top2018 best-parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. As long as the question concerns mainchain protein atoms, these files should be usable as is. There is a separate version that has been filtered on all atoms that is suitable for sidechains.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_mcfilter_60pct_complete.txt and found in top2018_cifs_mc_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_mc_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_mc_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_mcfilter_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_mc_filted.csv and as _top2018.percent_passrate in the .cif file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p>Sidechain atoms beyond CB have not been considered in the filtering. However, all sidechains have been included for residues that passed the mainchain filters. DO NOT use this set of files for serious questions involving sidechains. See our all-atom filtered dataset instead.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>Mainchain atoms are defined as N, CA, C, O, CB.<br> Note that CB is included, since its ideal position is defined by the other mainchain atoms.</p> <p>All mainchain atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p><br> Filtering doumentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned residues and included segments in a cif data block named data_top2018_dataset. This block can be found at the end of the file.</p> <p>In the _top2018_deleted_residue loop, causes of pruning are documented. If a residues was removed due to failing the B-factor filter, a "b" will appear in the appropriate column. Otherwise, a "." will appear. Other filtering criteria are treated similarly with the following codes:<br> b - B-factor<br> c - RSCC<br> m - map value<br> g - geometry outliers<br> o - steric overlaps<br> a - alternate conformations</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9<br> Initial upload to establish DOI</p> <p>Version 1.0<br> Initial complete upload</p>
High quality protein residues: Top2018 all-atom-filtered residues - mmCIF
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue.</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue-level filtering, and the Top2018 best-parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_full_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filtered.csv and as _top2018.percent_passrate in the .cif file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned residues and included segments in a cif data block named data_top2018_dataset. This block can be found at the end of the file.</p> <p>In the _top2018_deleted_residue loop, causes of pruning are documented. If a residues was removed due to failing the B-factor filter, a "b" will appear in the appropriate column. Otherwise, a "." will appear. Other filtering criteria are treated similarly with the following codes:<br> b - B-factor<br> c - RSCC<br> m - map value<br> g - geometry outliers<br> o - steric overlaps<br> a - alternate conformations</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9<br> Initial upload to establish DOI</p> <p>Version 1.0<br> Initial full upload</p>
Data for the "Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1"
<p>Bottom-up mass spectrometry-based proteomic analysis (trypsin) was performed on four biological replicates of HIV-1 virions. These virions were isolated from HEK293T cells transfected with a HIV-1 proviral plasmid derived from the pNL4-3 molecular clone, rendered biosafe due to inactivating point mutations in both the env and vpr reading frames. There are 8 total spectra, 4 are from unlabeled aliquots of sample, and 4 are from aliquots of sample treated with glutathione to label dehydroamino acids (Spectra can be accessed on MassIVE (MSV000088220). All data was analyzed using MetaMorpheus version 0.0.319 (https://github.com/smith-chem-wisc/MetaMorpheus). Provided here are the results of this analysis.</p>
Application of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables to Interaboratory Comparison Study on Pesticide Residues in Food (ILC)
<p>The suitability of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables and related products, developed within activities of task (Multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables (including tea) and fruit juices), was evaluated by the Interaboratory Comparison Study on Pesticide Residues in Food (ILC). Test material (“Pesticide Residues in green tea”), prepared from the batch used for another proficiency test (PT), was provided by Fapas (Fera Science Ltd, York, UK).</p> <p>Data set obtains (i) information about performance characteristics of the analytical method and (ii) compilation of results of interlaboratory study.</p>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Bosnia and Herzegovina
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Bosnia and Herzegovina. The presence of unauthorised substances, residues of veterinary medicinal products or chemical contaminants in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Regulation (EC) 1881/2006 lays down the maximum limits for the presence of certain contaminants in animal products. Council Directive 96/23/EC lays down measures to monitor certain substances and residues thereof, mainly veterinary medicinal products, in live animals and animal products. Additionally, Commission Decision 97/747/EC lays down levels and frequencies of sampling for certain animal products.</p> <p>The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States and three pre-accession countries: Bosnia-Herzegovina, Montenegro, North Macedonia and Serbia.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the monitoring plan, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances or products mean substances or products prohibited under European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2020 – Veterinary Office of Bosnia and Herzegovina</p> <p>VMPR_2018 – Veterinary Office of Bosnia and Herzegovina</p>
United Kingdom results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2020 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2019 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2018 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2017 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2016 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2015 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2014 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2013 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2012 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2011 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Annexes for the 2020 EU report on pesticide residues
<p>Annex II: PRIMo rev.3.1 file containing the results of the exposure assessment </p> <p>Annex III: detailed information on the different control programmes, full list of samples exceeding the MRLs anonymised by sample code, analytical scope from the official laboratories reporting to EFSA data on pesticide residues, the HBGV and PF used in the risk assessment.</p>
Crop Residue Burning Emission Coefficients
<p>This dataset provides a comprehensive collection of crop-specific coefficients related to the burning of agricultural waste or “residue”: Emission Factors, Combustion Efficiencies, and Crop-to-Residue Ratios.</p> <p>The crop-type emission factor tables contain the corresponding reference number, author citations, location, field/lab/expert flag, with average minus one standard deviation (“Minus SD”), average (or stated value), and average plus one standard deviation (“Plus SD”) emission factor values for CO<sub>2</sub>, CO, CH<sub>4</sub>, NOx, SO<sub>2</sub>, NH<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10.0</sub>, and a notes column. There is a corresponding emission factor table reference list. There are emission factor tables for wheat, rice, sugarcane, maize, generic, and miscellaneous crops (beans, alfalfa, millet, cotton, rapeseed, sorghum, and barley).</p> <p>The combustion efficiency table contains the corresponding reference number, author citations, field/lab/expert flag, with average minus one standard deviation (“Minus SD”), average (or stated value), and average plus one standard deviation (“Plus SD”) values for wheat, maize, sugarcane, rice, and generic crops. There is a corresponding combustion efficiency table reference list. </p> <p>The crop-to-residue table contains values for wheat, maize, rice, cotton, soybean, sugarcane, alfalfa, barley, beans, and sorghum. The references are also provided.</p>
Figure 1 Scatter Plot of Standardized Residual by Standardized Predicted Value-Potential Predictability of ZPD of Children's Cognitive Development
<p>One of the essential assumptions that should be met in regression analysis is the linearity of<br> the data. The result of the analysis of variance (ANOVA) shows that regression model is linear; F<br> (1, 39) = 8.429, P <0.05 for model 1 and F (2, 38) = 8.648, P<0.05 for model 2. Moreover, Scatter<br> plot shows (Figure1) that there is no funnel shaped or crescent shaped cloud. This indicates that two<br> assumptions of linearity and homogeneity of variance have been met.</p>
North American Coastal Plain PRISMA Surface Reflectance and Mixture Residual Spectra
<p>The data available here include the training and validation spectra for creating the models for the currently unpublished manuscript “Classifying Plant Communities in the North American Coastal Plain with PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual." Spectral data contain both raw surface reflectance (SR) and spectral mixture residual spectra (MR) transformed with endmembers and code from Sousa et al.'s (2022) paper titled "The spectral mixture residual: A source of low‐variance information to enhance the explainability and accuracy of surface biology and geology retrievals." Spectra represent averaged 60 m x 60 m areas (2 x 2-pixel window) located in the Red Hills (RH), the Jones Ecological Research Center (JERC), the Ordway-Swisher Biological Station (OSBS), and the Disney Wilderness Preserve (DSNY). To maintain the confidentiality of private property information on behalf of landowners, the locations of the RH plots were intentionally obscured, considering the nature of the region. See manuscript for further details. </p>
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone. in Palaeohistology helps reveal taxonomic variability in exceptionally large temnospondyl humeri from the Upper Triassic of Krasiejów, SW Poland
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone.
Data from: Residues neighboring an SH3-binding motif participate in determining affinity and specificity in vivo
<p>In signaling networks, many protein-protein interactions are mediated by modular domains that bind short linear motifs. The motifs' sequences modulate many factors, among them affinity and specificity, or the ability to bind strongly and to bind the appropriate partners. Previous studies have proposed a trade-off between affinity and specificity, suggesting that motifs with high affinity are less capable of differentiating between domains with similar sequences and structures. Using Deep Mutational Scanning to create a mutant library of a well characterized binding motif, and protein complementation assays to measure protein-protein interactions, we tested this trade-off in vivo for the first time. We measured the binding strength and specificity of a library of mutants of a binding motif on the MAP kinase kinase Pbs2, which binds the SH3 domain of the osmosensor protein Sho1 in Saccharomyces cerevisiae. We find that many mutations in the region surrounding the binding motif modify binding strength, but that few mutations have a strong impact on specificity. Moreover, we find no systematic relationship between affinity and specificity as measured in vivo. Interestingly, all Pbs2 mutations which increase affinity or specificity are situated outside of the Pbs2 residues that interact with the canonical SH3-binding pocket, suggesting that other surfaces on Sho1 contribute to binding. We use predicted structures to propose a model of binding which involves residues neighboring the core Pbs2 motif binding outside of the canonical SH3-binding pocket, allowing affinity and specificity to be determined by a broader range of sequences than what has previously been considered.</p>
Fig. 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 3 Seasonal variation of sibling species (An. coluzzii and An. gambiae) in the study area. Abbreviations: DS, dry season; RS, rainy season
Fig. 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 2 Mortality rate of Anopheles gambiae Kisumu (laboratory susceptible strain) after 30 min exposure to cement and mud walls treated with pirimiphos-methyl in 2017 (a) and 2018 (b). The red line indicates the WHO efficacy threshold (mortality of 80%) of an insecticide
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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.