cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zhkd566kht.2
title: Calculated Dn/Ds values for genes from 372 genomes of Mycobacterium tuberculosis (Beijing genotype)

description: Dn/ds ratio was calculated as described by Nei and Gojobori. All genomes were downloaded from SRA archive NCBI then these data were mapped on the reference genome M. tuberculosis H37Rv (NC_000962.3) and held SNP-calling by samtools. The created vcf files were analyzed and calculated the number of synonymous and non synonymous SNPs for each gene in genome. After that sequences were created for each gene with detected mutations and were compared with reference genome
DOI: 10.17632/kx6x8krzbg.1
title: The effects of air exposure on the stress response of blacktip sharks, Carcharhinus limbatus Data

description: In this study, we endeavored to answer the following questions: (1.) How does air exposure affect the stress response? (2.) Do environmental and individual variables explain a significant amount of variation in this effect? (3.) Will air exposure affect release behavior and can levels of stress indicators predict release behavior? (4.) Does air exposure affect the propensity to display the nictitating membrane reflex (NMR)? Results revealed that lactate was significantly higher after 10 and 15 minutes of air exposure compared to baseline levels. Release behavior was poorer after 5, 10, and 15 minutes of air exposure compared to baseline and 15 minutes of air exposure resulted in complete impairment of the NMR. Data was collected from wild-captured blacktip sharks from the Gulf of Mexico in the Mississippi Sound. Lactate was measured in mmol/L, temperatures were measured in degrees Celcius, dissolved oxygen was measured in mg/L, glucose was measured in mg/dL, total length was measured in cm. Thermal gradient was calculated from the difference between the air and water temperatures. Id is an individual identification value assigned to each individual blacktip shark collected for this study.
DOI: 10.5281/zenodo.3964432
title: Silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for 2MgO•SiO2 and MgO•SiO2 glasses

description: Raw and processed silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for 2MgO•SiO2 and MgO•SiO2 glasses from the publication "Structural investigations of magnesium silicate glasses by <sup>29</sup>Si 2D Magic-Angle Flipping NMR" in <em>J. Non-Cryst. Solids</em>, <strong>357</strong>, 2787-2795 (2011), by M. C. Davis, K. J. Sanders, P. J. Grandinetti, S. J. Gaudio, S. Sen. Details of the csdf dataset format are given in <em>PLOS ONE,</em> 15(1): e0225953 (2020), "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package csdmpy, or other CSDM-compliant software.
DOI: 10.5281/zenodo.3964431
title: Silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for 2MgO•SiO2 and MgO•SiO2 glasses

description: Raw and processed silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for 2MgO•SiO2 and MgO•SiO2 glasses from the publication "Structural investigations of magnesium silicate glasses by <sup>29</sup>Si 2D Magic-Angle Flipping NMR" in <em>J. Non-Cryst. Solids</em>, <strong>357</strong>, 2787-2795 (2011), by M. C. Davis, K. J. Sanders, P. J. Grandinetti, S. J. Gaudio, S. Sen. Details of the csdf dataset format are given in <em>PLOS ONE,</em> 15(1): e0225953 (2020), "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package csdmpy, or other CSDM-compliant software.
DOI: 10.5281/zenodo.3247503
title: Dynamics of stardust: Modelling vibrational dissociation of [H2-HCO]+

description: Supporting information for the article.

Archives figure_*.tar.gz contain data used to generate all Figures in the manuscript.

Archive python_tools.tar.gz contains supplementary python scripts to process trajectory files, and to plot figures.

Archives runs_*.tar.gz contain raw data from CPMD calculations. They are grouped by method and excited bond.

See README for details.
DOI: 10.7910/dvn/7h6xqo/ffqxw1
title: Meng_Codebook.pdf

description: :unav
DOI: 10.7910/dvn/7h6xqo/aaetfe
title: Meng_parties_GWF.tab

description: :unav
DOI: 10.7910/dvn/7h6xqo/pgwjl0
title: Meng_Analysis.txt

description: :unav
DOI: 10.7910/dvn/7h6xqo/y4cmkj
title: Meng_outcomes.tab

description: :unav
DOI: 10.7910/dvn/7h6xqo/0zaa0g
title: Meng_Analysis.do

description: :unav
DOI: 10.7910/dvn/7h6xqo/9k5rkx
title: Meng_parties_Archigos.tab

description: :unav
DOI: 10.7910/dvn/7h6xqo/unszdi
title: Meng_parties.tab

description: :unav
DOI: 10.5285/93ff55bf-7415-44c8-8e3d-7e2f5c5ffd6d
title: Effects of VLF transmitter waves on the inner belt and slot region

description: Signals from VLF transmitters can leak from the Earth-ionosphere wave guide into the inner magnetosphere, where they propagate in the whistler mode and contribute to electron dynamics in the inner radiation belt and slot region. Observations show that the waves from each VLF transmitter are highly localised, peaking on the nightside in the vicinity of the transmitter. In this study we use ~5 years of Van Allen probe observations to construct global statistical models of the bounce-averaged pitch angle diffusion coefficients for each individual VLF transmitter, as a function of L*, Magnetic Local Time (MLT) and geographic longitude. We construct a 1D pitch-angle diffusion model with implicit longitude and MLT dependence to show that VLF transmitter waves weakly scatter electrons into the drift loss cone. We find that global averages of the wave power, determined by averaging the wave power over MLT and longitude, capture the long-term dynamics of the loss process, despite the highly localised nature of the waves in space. We use our new model to assess the role of VLF transmitters waves, hiss waves, and Coulomb collisions on electron loss in the inner radiation belt and slot region. At moderate relativistic energies, E~ keV, waves from VLF transmitters reduce electron lifetimes by an order of magnitude or more, down to the order of 200 days near the outer edge of the inner radiation belt. However, VLF transmitter waves are ineffective at removing multi-MeV electrons from either the inner radiation belt or slot region.

      Funding was provided by the NERC grant NE/P01738X/1.
description: The wave data used in this study are from Meredith et al. [2019] which uses NASA Van Allen Probe A data. Diffusion coefficients are calculated using PADIE (Glauert et al 2005). Electron pitch-angle distributions and associated decay timescales are calculated as described in Ross et al. [2019].
description: The wave data have been calibrated and quality-controlled prior to release.
DOI: 10.5519/qd.duwf6in5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (452 records)
DOI: 10.7910/dvn/vixnne/ojuvs3
title: Figure1_timetrend_1995-2012.tab

description: Database to generate Figure 1
DOI: 10.7910/dvn/vixnne/vn5zjo
title: Figure2_levelgovt_2007-2012.tab

description: Database to generate Figure 2
DOI: 10.7910/dvn/vixnne/cd5tcp
title: Figure3_parties_2007-2012.dta.tab

description: Database to generate Figure 3
DOI: 10.7910/dvn/vixnne/drm7o2
title: Tables_HighProfileCriminalViolence.do

description: Do-file to generate Table 1 and run regression models (Tables 2-4 and Appendix Table)
DOI: 10.7910/dvn/vixnne/msrxj7
title: ReadMe.pdf

description: Please read before using data
DOI: 10.7910/dvn/vixnne/egyhb8
title: Codebook_HighProfileCriminalViolence.pdf

description: Codebook associated with main data file, "Dataset_HighProfileCriminalViolence.dta"
DOI: 10.7910/dvn/vixnne/xh5zqu
title: Dataset_HighProfileCriminalViolence.tab

description: Dataset used for regression models and maps
DOI: 10.7910/dvn/vixnne/5rgmmy
title: Figure4_attacks_vs_governed.tab

description: Database to generate Figure 4
DOI: 10.7910/dvn/vixnne/e6rcco
title: Figures_HighProfileCriminalViolence.do

description: Do-file to generate graphs, based on the indicated .dta file
DOI: 10.7910/dvn/vixnne/j4eaxv
title: Figure5_electoralcycles_2006-2012.tab

description: Database to generate Figure 5
DOI: 10.25387/g3.12504422.v1
title: Supplemental Material for Keep et al., 2020

description: Figure S1: Histograms of Minor Allele Frequencies of all SNP markers per population and averaged over populations. Figure S2: Scatter plot of Pearson correlation between allele frequencies of pairs of SNP markers from a same scaffold and genomic distance between SNP markers in base pairs. Figure S3: Q-Q plots of p-values from GWAS for all available traits accounting for neither structure nor kinship, only for structure, only for kinship or for both kinship and structure. Figure S4: Pairwise phenotypic Euclidian distances plotted against pairwise Euclidian genetic distances for all available traits. Genetic Euclidian distances were calculated based on the 100 most significantly associated SNP markers to the trait as given by the GWAS. Table S1: Genebank accessions from the natural diversity of perennial ryegrass used in the study. Table S2: List of perennial ryegrass cultivars used in the study and their origin. Table S3: Hi-Plex genotyping description. Table S4: Alternative allele frequencies of 189,781 SNP markers for the natural populations and cultivars used in this study. SNP markers are referred to as follows: scaffold number (from reference genome of Byrne et al. 2015)_scaffold reference_SNP position in scaffold. The SNP from the Hi-Plex are indicated by “<i>monsterplex</i>” at the end of the SNP marker names. Table S5: Seasonal climatic conditions at the three experimental gardens over the duration of the experiments. Table S6: Adjusted means per population for all the phenotypic traits used in this study. Table S7: Phenotypic variables description and associated results from GWAS and genomic prediction analysis. Table S8: Pearson correlation coefficients between the phenotypic variables. Table S9: SNP markers significantly associated with each trait (q-value&lt;10%). For each trait, the following information is indicated: P-values, false discovery rates, SNP marker effects and variance explained by SNP markers from both a linear model which did not account for kinship (trait-SNP linear regression model) and a linear model which accounted for kinship (GWAS model). Table S10: Number of SNP jointly associated (GWAS with q-value&lt;10%) to phenotypic trait pairs. File S1: Detailed description of the phenotypic variables.
DOI: 10.25387/g3.12504422
title: Supplemental Material for Keep et al., 2020

description: Figure S1: Histograms of Minor Allele Frequencies of all SNP markers per population and averaged over populations. Figure S2: Scatter plot of Pearson correlation between allele frequencies of pairs of SNP markers from a same scaffold and genomic distance between SNP markers in base pairs. Figure S3: Q-Q plots of p-values from GWAS for all available traits accounting for neither structure nor kinship, only for structure, only for kinship or for both kinship and structure. Figure S4: Pairwise phenotypic Euclidian distances plotted against pairwise Euclidian genetic distances for all available traits. Genetic Euclidian distances were calculated based on the 100 most significantly associated SNP markers to the trait as given by the GWAS. Table S1: Genebank accessions from the natural diversity of perennial ryegrass used in the study. Table S2: List of perennial ryegrass cultivars used in the study and their origin. Table S3: Hi-Plex genotyping description. Table S4: Alternative allele frequencies of 189,781 SNP markers for the natural populations and cultivars used in this study. SNP markers are referred to as follows: scaffold number (from reference genome of Byrne et al. 2015)_scaffold reference_SNP position in scaffold. The SNP from the Hi-Plex are indicated by “<i>monsterplex</i>” at the end of the SNP marker names. Table S5: Seasonal climatic conditions at the three experimental gardens over the duration of the experiments. Table S6: Adjusted means per population for all the phenotypic traits used in this study. Table S7: Phenotypic variables description and associated results from GWAS and genomic prediction analysis. Table S8: Pearson correlation coefficients between the phenotypic variables. Table S9: SNP markers significantly associated with each trait (q-value&lt;10%). For each trait, the following information is indicated: P-values, false discovery rates, SNP marker effects and variance explained by SNP markers from both a linear model which did not account for kinship (trait-SNP linear regression model) and a linear model which accounted for kinship (GWAS model). Table S10: Number of SNP jointly associated (GWAS with q-value&lt;10%) to phenotypic trait pairs. File S1: Detailed description of the phenotypic variables.
DOI: 10.5281/zenodo.3247908
title: Movie files: Actin dynamics and the Bmp pathway drive apical extrusion of proepicardial cells.

description: The epicardium, the outer mesothelial layer enclosing the myocardium, plays key roles in heart development and regeneration. During embryogenesis it arises from the proepicardium (PE), a cell cluster that appears in the dorsal pericardium (DP) close to the venous pole of the heart. Little is known about how the PE emerges from the pericardial mesothelium. Using the zebrafish model and a combination of genetic tools, pharmacological agents and quantitative <em>in vivo</em> imaging, we reveal that a coordinated collective movement of DP cells drives PE formation. We found that BMP signaling and the actomyosin cytoskeleton promote constriction of the DP, which enabled PE cells to extrude apically. We provide evidence that cell extrusion, which has been described in the elimination of unfit cells from epithelia and the emergence of hematopoietic stem cells, is also a mechanism for PE cells to exit an organized mesothelium and fulfil the developmental fate to form a new tissue layer, the epicardium.
DOI: 10.5281/zenodo.3247909
title: Movie files: Actin dynamics and the Bmp pathway drive apical extrusion of proepicardial cells.

description: The epicardium, the outer mesothelial layer enclosing the myocardium, plays key roles in heart development and regeneration. During embryogenesis it arises from the proepicardium (PE), a cell cluster that appears in the dorsal pericardium (DP) close to the venous pole of the heart. Little is known about how the PE emerges from the pericardial mesothelium. Using the zebrafish model and a combination of genetic tools, pharmacological agents and quantitative <em>in vivo</em> imaging, we reveal that a coordinated collective movement of DP cells drives PE formation. We found that BMP signaling and the actomyosin cytoskeleton promote constriction of the DP, which enabled PE cells to extrude apically. We provide evidence that cell extrusion, which has been described in the elimination of unfit cells from epithelia and the emergence of hematopoietic stem cells, is also a mechanism for PE cells to exit an organized mesothelium and fulfil the developmental fate to form a new tissue layer, the epicardium.
DOI: 10.5284/1053674
title: Woods Mill, Milltown, Glossop, Derbyshire. Archaeological Works (OASIS ID: centrefo4-336078)

description: In July 2016, Salford Archaeology, within the Centre for Applied Archaeology at the University of Salford, was commissioned by Glossop Land Ltd to carry out programmes of archaeological works in advance of proposed developments within the  Woods Mill complex, Glossop, Derbyshire. These  works were required to further inform and support the planning application.
DOI: 10.7910/dvn/94yocv/hxzfhv
title: EBV and Breast cancer Data.tab

description: :unav
DOI: 10.7910/dvn/94yocv/ghbf8c
title: PCR 1.tif

description: Gel result for 10 cases
DOI: 10.7910/dvn/94yocv/gkoehd
title: PCR 2.tif

description: gel result for different 8 cases
DOI: 10.7910/dvn/94yocv/cylz0i
title: PCR 3.tif

description: Gel result for 7 cases
DOI: 10.7910/dvn/94yocv/o7bqep
title: PCR 4.tif

description: gel for 14 cases
DOI: 10.5517/ccdc.csd.cc20xyzw
title: CCDC 1874879: Experimental Crystal Structure Determination

description: Related Article: Ramakrishnan Suseela Meerakrishna, Suresh Snoxma Smile, Mohanakumaran Athira, Venkata Surya Kumar Choutipalli, Ponnusamy Shanmugam|2020|New J.Chem.|44|11593|doi:10.1039/D0NJ01684E
DOI: 10.5517/ccdc.csd.cc21n888
title: CCDC 1896309: Experimental Crystal Structure Determination

description: Related Article: Ramakrishnan Suseela Meerakrishna, Suresh Snoxma Smile, Mohanakumaran Athira, Venkata Surya Kumar Choutipalli, Ponnusamy Shanmugam|2020|New J.Chem.|44|11593|doi:10.1039/D0NJ01684E
DOI: 10.5517/ccdc.csd.cc21n899
title: CCDC 1896310: Experimental Crystal Structure Determination

description: Related Article: Ramakrishnan Suseela Meerakrishna, Suresh Snoxma Smile, Mohanakumaran Athira, Venkata Surya Kumar Choutipalli, Ponnusamy Shanmugam|2020|New J.Chem.|44|11593|doi:10.1039/D0NJ01684E
DOI: 10.5517/ccdc.csd.cc22h1f2
title: CCDC 1921083: Experimental Crystal Structure Determination

description: Related Article: Lukáš Smolko, Romana Smolková, Erika Samoľová, Ibrahim Morgan, Mohamad Saoud, Goran N. Kaluđerović|2020|J.Inorg.Biochem.|210|111160|doi:10.1016/j.jinorgbio.2020.111160
DOI: 10.5517/ccdc.csd.cc22h1g3
title: CCDC 1921084: Experimental Crystal Structure Determination

description: Related Article: Lukáš Smolko, Romana Smolková, Erika Samoľová, Ibrahim Morgan, Mohamad Saoud, Goran N. Kaluđerović|2020|J.Inorg.Biochem.|210|111160|doi:10.1016/j.jinorgbio.2020.111160
DOI: 10.5517/ccdc.csd.cc24jvp7
title: CCDC 1982409: Experimental Crystal Structure Determination

description: Related Article: Anna Karla dos Santos Pereira, Carlos Marrote Manzano, Douglas Hideki Nakahata, Juan Carlos Tenorio Clavijo, Douglas Henrique Pereira, Wilton Rogério Lustri, Pedro Paulo Corbi|2020|New J.Chem.|44|11546|doi:10.1039/D0NJ02009E
DOI: 10.5517/ccdc.csd.cc24jvr9
title: CCDC 1982411: Experimental Crystal Structure Determination

description: Related Article: Anna Karla dos Santos Pereira, Carlos Marrote Manzano, Douglas Hideki Nakahata, Juan Carlos Tenorio Clavijo, Douglas Henrique Pereira, Wilton Rogério Lustri, Pedro Paulo Corbi|2020|New J.Chem.|44|11546|doi:10.1039/D0NJ02009E
DOI: 10.5517/ccdc.csd.cc24ny1t
title: CCDC 1986326: Experimental Crystal Structure Determination

description: Related Article: Danh X. Ngo, Sarah A. Del Ciello, Alexandra T. Barth, Ryan G. Hadt, Robert H. Grubbs, Harry B. Gray, Brendon J. McNicholas|2020|Inorg.Chem.|59|9594|doi:10.1021/acs.inorgchem.0c00632
DOI: 10.5517/ccdc.csd.cc24ny6z
title: CCDC 1986331: Experimental Crystal Structure Determination

description: Related Article: Danh X. Ngo, Sarah A. Del Ciello, Alexandra T. Barth, Ryan G. Hadt, Robert H. Grubbs, Harry B. Gray, Brendon J. McNicholas|2020|Inorg.Chem.|59|9594|doi:10.1021/acs.inorgchem.0c00632
DOI: 10.5517/ccdc.csd.cc24wld1
title: CCDC 1992723: Experimental Crystal Structure Determination

description: Related Article: Moumita Maji, Sourav Acharya, Saptarshi Maji, Kallol Purkait, Arnab Gupta, Arindam Mukherjee|2020|Inorg.Chem.|59|10262|doi:10.1021/acs.inorgchem.0c01433
DOI: 10.5517/ccdc.csd.cc24wlf2
title: CCDC 1992724: Experimental Crystal Structure Determination

description: Related Article: Moumita Maji, Sourav Acharya, Saptarshi Maji, Kallol Purkait, Arnab Gupta, Arindam Mukherjee|2020|Inorg.Chem.|59|10262|doi:10.1021/acs.inorgchem.0c01433
DOI: 10.5517/ccdc.csd.cc24z6s4
title: CCDC 1995246: Experimental Crystal Structure Determination

description: Related Article: Wenqing Zang, Yin Wei, Min Shi|2020|Org.Lett.|22|5466|doi:10.1021/acs.orglett.0c01819
DOI: 10.5281/zenodo.3248612
title: Simulation CER160_128_POPC_512_NaCl_150mM_310K_rep1 membrane

description: <strong>Title publication:</strong> <em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em>

<strong>Year publication:</strong> 2018

<strong>DOI publication: </strong>10.1021/acscentsci.7b00582

<strong>Description:</strong> Simulation 512 POPCs membrane containing 128 ceramides (CER160)  using charmm36 at 310K.

<strong>MD engine:</strong> gromacs 2016.1

<strong>Force field</strong>: charmm36

<strong>Temperature</strong>: 310 K

<strong>Pressure:</strong> 1 bar

<strong>Simulation time</strong>: 3000 ns

<strong>Saving frequency</strong>: 100 ps

<strong>Molecular content</strong>:

POPC        512<br>
CER160    128<br>
SOL      32000<br>
NA             86<br>
CL              86

<strong>Additional </strong><strong>information</strong>:
DOI: 10.5281/zenodo.3248611
title: Simulation CER160_128_POPC_512_NaCl_150mM_310K_rep1 membrane

description: <strong>Title publication:</strong> <em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em>

<strong>Year publication:</strong> 2018

<strong>DOI publication: </strong>10.1021/acscentsci.7b00582

<strong>Description:</strong> Simulation 512 POPCs membrane containing 128 ceramides (CER160)  using charmm36 at 310K.

<strong>MD engine:</strong> gromacs 2016.1

<strong>Force field</strong>: charmm36

<strong>Temperature</strong>: 310 K

<strong>Pressure:</strong> 1 bar

<strong>Simulation time</strong>: 3000 ns

<strong>Saving frequency</strong>: 100 ps

<strong>Molecular content</strong>:

POPC        512<br>
CER160    128<br>
SOL      32000<br>
NA             86<br>
CL              86

<strong>Additional </strong><strong>information</strong>:
DOI: 10.5281/zenodo.3248619
title: Simulation CER160_128_POPC_512_TM3_10_5up_5down_NaCl_150mM_310K_rep1 membrane

description: <strong>Title publication:</strong> <em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em>

<strong>Year publication:</strong> 2018

<strong>DOI publication: </strong>10.1021/acscentsci.7b00582

<strong>Description:</strong> Simulation of a membrane containing 512 POPC and 128 ceramide (CER160) together with ten LAPTM4B-TM3 peptides using charmm36 at 310K.

<strong>MD engine:</strong> 2016.2-dev-20170105-4feb0be

<strong>Force field</strong>: charmm36

<strong>Temperature</strong>: 310 K

<strong>Pressure:</strong> 1 bar

<strong>Simulation time</strong>: 3200 ns

<strong>Saving frequency</strong>: 100 ps

<strong>Molecular content</strong>:

TM3            10<br>
POPC        512<br>
CER160     128<br>
SOL       31990<br>
NA              96<br>
CL               86

<strong>Additional </strong><strong>information</strong>:<br>
    * Membrane containing LAPTM4B TM3 peptides (Ac-AWIIPFFCYQIFDFALNMLVAITVLIY-NH2).
DOI: 10.5281/zenodo.3248620
title: Simulation CER160_128_POPC_512_TM3_10_5up_5down_NaCl_150mM_310K_rep1 membrane

description: <strong>Title publication:</strong> <em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em>

<strong>Year publication:</strong> 2018

<strong>DOI publication: </strong>10.1021/acscentsci.7b00582

<strong>Description:</strong> Simulation of a membrane containing 512 POPC and 128 ceramide (CER160) together with ten LAPTM4B-TM3 peptides using charmm36 at 310K.

<strong>MD engine:</strong> 2016.2-dev-20170105-4feb0be

<strong>Force field</strong>: charmm36

<strong>Temperature</strong>: 310 K

<strong>Pressure:</strong> 1 bar

<strong>Simulation time</strong>: 3200 ns

<strong>Saving frequency</strong>: 100 ps

<strong>Molecular content</strong>:

TM3            10<br>
POPC        512<br>
CER160     128<br>
SOL       31990<br>
NA              96<br>
CL               86

<strong>Additional </strong><strong>information</strong>:<br>
    * Membrane containing LAPTM4B TM3 peptides (Ac-AWIIPFFCYQIFDFALNMLVAITVLIY-NH2).
DOI: 10.5517/ccdc.csd.cc23spsd
title: CCDC 1960154: Experimental Crystal Structure Determination

description: Related Article: Yong Yang, Zhenyu Zhang, Xiaoyong Chang, Ya-Qiong Zhang, Rong-Zhen Liao, Lele Duan|2020|Inorg.Chem.|59|10234|doi:10.1021/acs.inorgchem.0c01364