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650 results for “molecular mechanisms”
Geometries for "X-ray Absorption Spectra for Aqueous Ammonia and Ammonium: Quantum Mechanical versus Molecular Mechanical Embedding Schemes"
<p>195 clusters of ammonia and ammonium in water, used in <em>"X-ray Absorption Spectra for Aqueous Ammonia and Ammonium: Quantum Mechanical versus Molecular Mechanical Embedding Schemes"</em></p> <p>The geometries were first used in <em>J. Am. Chem. Soc.</em> 2017, 139, 36, 12773–12783, and later in <em>J. Phys. Chem. Lett.</em> 2021, 12, 36, 8865–8871</p> <p> </p> <p> </p>
Heating by Dissipation of Energy from Absorbed Light: Molecular Mechanisms Underlying the Survival Strategy of Polar Algae
<p>The original data, which served as the source material for the scientific article on the polar alga <em>Pediastrum orientale, co</em>llected from Reindeer Lake on Spitsbergen. Consists of datasets obtained using various techniques: fluorescence microscopy (microscope_fluo), fluorescence lifetime microscopy (FLIM), high-performance liquid chromatography (HPLC), atomic force microscopy (AFM), Raman microspectroscopy (RAMAN), fluorescence lifitime spectroscopy (lifetime) and fluorescence spectroscopy (Fluo).<br><br>HPLC - Nexera LC-40 (Shimadzu, Japan)<br>FLIM - OLYMPUS IX71 confocal microscope MicroTime 200 with SymPhoTime 64 software package (PicoQuant, GmbH, Germany)<br>RAMAN - inVia Reflex confocal Raman microscope with the WiRE 5.5 software package (Renishaw, UK)<br>AFM - JPK Nanowizard 3 system with JPKSMP data processing software (Bruker, USA)<br>Lifetime - FluoTime 300 spectrometer with FluoFit Pro v 4.5.3.0 (PicoQuant, Germany)<br>Microscope_Fluo - Zeiss LSM980 confocal microscope with Airy2 and Elyra7 detectors and ZEN 3.1 software (Zeiss, Germany)<br>Fluo - OLYMPUS IX71 confocal microscope MicroTime 200 system with the spectrograph SR-163 and the Newton 970 EMCCD camera (Andor Technology)<br><br></p> <p> </p> <p> </p>
Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8
<p>Trajectory, topology and index files for TLR7 (apo), TLR7-R, TLR7-H, TLR7-G, TLR8 (apo), TLR8-R, TLR8-H, TLR8-G systems. </p>
Supplementary Table of Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharma-cology and Molecular Docking
<p>This is the supplementary tables of article named"Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharmacology and Molecular Docking"</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Molecular dynamic trajectory for the article "The Binding Mechanism between Inositol Phosphate (InsP) and the Jasmonate Receptor Complex: A Computational Study"
<p>Molecular dynamic trajectory preparation file of Jasmonate receptor complex. We set up six systems. </p> <p>Each system contains PSF file and PDB file.</p>
Supplementary material for "Molecular dynamics gives new insights into the glucose tolerance and inhibition mechanisms on β-glucosidases" (2 video files)
<p>Video S1: MD of the glucose exit in a glucose-tolerant GH1 β-Glucosidase</p> <p>Video S2: Interactions among glucose, D228, K257, and N312 in a glucose-tolerant GH1 β-glucosidase</p>
Supplementary Information - Chapter 1. Transcriptomic investigation of the molecular mechanisms underlying resistance to the neonicotinoid thiamethoxam and the pyrethroid lambda-cyhalothrin in Euschistus heros (Hemiptera: Pentatomidae)
<p><span>Laboratory-selected resistant strains of <em>Euschistus heros</em> to thiamethoxam (NEO) and lambda-cyhalothrin (PYR) were recently reported in Brazil. However, the mechanisms conferring resistance to these insecticides in <em>E. heros</em> remain unresolved. We utilized comparative transcriptome profiling and single nucleotide polymorphism (SNP) calling of susceptible and resistant strains of <em>E. heros</em> to investigate the molecular mechanism(s) underlying resistance.</span><span> </span><span>The <em>E. heros</em> transcriptome was assembled, generating 91 673 transcripts with a mean length of 720 bp and N50 of 1795 bp. Comparative gene expression analysis between the susceptible (SUS) and NEO strains identified 215 significantly differentially expressed (DE) transcripts. DE transcripts associated with the xenobiotic metabolism were all up-regulated in the NEO strain. The comparative analysis of the SUS and PYR strains identified 204 DE transcripts, including an esterase (esterase FE4), a glutathione-<em>S</em>-transferase, an ABC transporter (ABCC1) and aquaporins that were up-regulated in the PYR strain. We identified 9588 and 15 043 nonsynonymous SNPs in the PYR and NEO strains. One of the SNPs (D70N) detected in the NEO strain occurs in a subunit (α5) of the nAChRs, the target site of neonicotinoid insecticides. Nevertheless, this residue position in α5 is not conserved among insects.</span><span> </span><span>Neonicotinoid and pyrethroid resistance in laboratory-selected <em>E. heros</em> is associated with a potential metabolic resistance mechanism by the overexpression of proteins commonly involved in the three phases of xenobiotic metabolism. Together these findings provide insight into the potential basis of resistance in <em>E. heros</em> and will inform the development and implementation of resistance management strategies against this important pest.</span></p> <p><strong><span>*Published in: </span></strong><em><span>Pest Management Science</span></em><span><span> 79.12 (2023): 5349-5361</span>. <a href="https://doi.org/10.1002/ps.7745">https://doi.org/10.1002/ps.7745</a></span></p>
Experimental and Simulation data for the molecular mechanism of temperature-dependent phase separation of HSF1
<p>1. lmp_datatfile</p> <p>The documents contain the LAMMPS input datafiles of initial configuration used to run all simulations, including single chain, trimer(LZ1-3-RD), and RD systems.</p> <p>2.Experimental data.</p> <p>The documents contain original ucsf data</p>
Long-term adaptation of lymphoma cell lines to hypoxia is mediated by diverse molecular mechanisms that are targetable with specific inhibitors - Supplemental Data Tables
<p><strong>Supplemental Data Table - Metabolome.xlsx</strong> - a complete list of measured metabolites for Ramos and HBL2 cell lines. Peak area values for normoxic and hypoxia-adapted cells in triplicates. Mean log 2 fold change and adjusted P values are provided for all metabolites except those under the limits of detection.</p> <p><strong>Supplemental Data Table - Transcriptome Differential Expression.xlsx</strong> - the tables of all transcripts detected in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls. Fold change and log 2 fold change, log 2 CPM, F statistic, P value and False Discovery Rate (FDR) were calculated using EdgeR package. Last column shows if a transcript was significantly <em>up</em>- or <em>down</em>regulated or not (<em>ns</em>).</p> <p><strong>Supplemental Data Table - Transcriptome Gene Set Analysis.xlsx</strong> - the tables of the gene set enrichment analysis of Reactome pathways in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls.</p> <p><strong>Supplemental Data Table - Proteome Differential Expression.xlsx</strong> - the tables of proteins detected and differentially expressed in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls.</p> <p><strong>Supplemental Data Table - Cell lines SNV characterization.xlsx</strong> - the tables of mutations (single nucleotide variations and short indels) detected in four tested lymphpoma cell lines and corresponding primary tumor (PT) samples. AF_CTRL - allelic frequency (0..1) in healthy control DNA from the patient, AF_PT - allelic frequency in primary tumor, AF_PDCL - allelic frequency in patient-derived cell line. DP - total sequence depth, TLOD - Log odds that the variant is present in the tumor sample relative to the expected noise. Gencode and HGNC annotation columns are output of the Funcotator analysis. SNVs were detected using Mutect2 as a part of GATK 4.6.0.0 somatic variat calling pipeline.</p>
High-Speed Atomic Force Microscopy Highlights New Molecular Mechanism of Daptomycin Action
<p>Data underlying the figures in the publication “High-speed atomic force microscopy highlights new molecular mechanism of daptomycin action”, published in <em>Nat Commun, </em><strong>2020</strong>, 11, 6312. <a href="https://doi.org/10.1038/s41467-020-19710-z">https://doi.org/10.1038/s41467-020-19710-z</a></p> <p>Table of contents:</p> <p><strong>1.</strong> <strong>Movie 1</strong>; HS-AFM movie of the first minutes after exposure to sub-MIC Dap on a POPG supported membrane. Guides to the eye highlights those oligomers identifiable. Movie parameters: frame rate 33 ms; full image of 90 nm x 65 nm and 256x180 pixels; colour depth 8bit (256 values); full colour scale 4 nm.</p> <p><strong>2. </strong><strong>Movie 2</strong>; HS-AFM movie after tens of minutes after exposure to sub-MIC Dap that shows diffusing dimples on a POPG supported membrane which interact by swinging trajectories. Movie parameters: frame rate 83 ms; full image of 150nm x 150nm and 256x256 pixels; colour depth 8bit (256 values); full colour scale 4 nm.</p> <p><strong>3. </strong><strong>Movie 3</strong>; HS-AFM movie of the first minutes after exposure to over-MIC of a POPG supported membrane. A flow of material is visualized thanks to the motion of the ripples, it starts at the lm3m cubic phase (left) and ends at a tubulation (right). Movie parameters: frame rate 456 ms; full image of 400nm x 400nm and 300x300 pixels; colour depth 8bit (256 values); full colour scale 16 nm.</p> <p><strong>4. </strong><strong>Movie 4</strong>; HS-AFM movie of the cyclic accumulation of material in the pores created on TOCL/POPG supported membranes under the exposure of the outer leaflet to supplementary quantities of Dap added to the imaging solution. The process seems to eject material out of the membrane; see the material that appears next to the pore at 1.30s. Movie parameters: frame rate 260 ms; zoom of a full image of 140nm x 100nm and 256x180 pixels; colour 29 depth 8bit (256 values); full colour scale 3 nm.</p>
Transcriptomics Reveal Specific Molecular Mechanisms Underlying Transgenerational Immunity in Manduca sexta
<p class="FirstParagraph">The traditional view of innate immunity in insects is that every exposure to a pathogen triggers an identical and appropriate immune response, and that prior exposures to pathogens do not confer any protective (i.e. adaptive) effect against subsequent exposure to the same pathogen. This view has been challenged by experiments demonstrating that encounters with sub-lethal doses of a pathogen can prime the insect's immune system and thus, have protective effects against future lethal doses. Immune priming has been reported across several insect species, including the red flour beetle, the honeycomb moth, the bumblebee, and the European honeybee, among others. Immune priming can also be trans-generational where the parent's pathogenic history influences the immune response of its offspring. Phenotypic evidence of transgenerational immune priming (TGIP) exists in the tobacco moth <i>Manduca sexta</i> where first instar progeny of mothers injected with the bacterium <i>Serratia marcescens</i> exhibited a significant increase of <i>in-vivo</i> bacterial clearance. To identify the gene expression changes underlying TGIP in <i>Manduca sexta</i>, we performed transcriptome-wide, trans-generational differential gene expression analysis on mothers and their offspring after mothers were exposed to <i>S. marcescens</i>. We are the first to perform transcriptome-wide analysis of the gene expression changes associated with TGIP in this ecologically relevant model organism. We show that maternal exposure to both heat-killed and live <i>S. marcescens</i> has strong and significant trans-generational impacts on gene expression patterns in their offspring, including up-regulation of peptidoglycan recognition protein, toll-like receptor 9, and the antimicrobial peptide cecropin.</p>
Characterizing the molecular mechanisms for flipping charged peptide flanking loops across a lipid bilayer
<p>All simulation input data and analysis tools for regenerating results from the journal paper:</p> <p>S. J. Patel and R. C. Van Lehn. "Characterizing the Molecular Mechanisms for Flipping Charged Peptide Flanking Loops across a Lipid Bilayer." The Journal of Physical Chemistry B <strong>2018</strong> <em>122</em> (45), 10337-10348</p>
X-ray scattering datasets associated with the publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism"
<p>X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurement files (BKG).</p>
Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data
<p>Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data</p>
Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data
<p>Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data</p>
TCMID: Traditional Chinese Medicine integrative database for herb molecular mechanism analysis
<p><strong>ABSTRACT: </strong>Traditional Chinese Medicines Integrated Database and the description about Chinese herbs, including English and Latin names, properties, meridians, medicinal parts, herbal effect and indication. Traditional Chinese Medicine (TCM) is a system of healthcare and healing that has been practiced for thousands of years in China. It is based on a holistic approach that views the human body and its various systems as interconnected. TCM encompasses a wide range of practices, including herbal medicine, acupuncture, massage (tui na), exercise (qigong), and dietary therapy.</p> <p><strong>Instruction: </strong></p> <p>Data was cleaned and duplicates were removed.</p> <p><strong>Inspiration: </strong>The dataset was uploaded to UBRITE for "DGR_DEPOT" summer 2023 team project</p> <p><strong>Acknowledgements: </strong>Ruichao Xue 1, Zhao Fang, Meixia Zhang, Zhenghui Yi, Chengping Wen, Tieliu Shi</p> <p>TCMID: Traditional Chinese Medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013 Jan;41(Database issue):D1089-95. doi: 10.1093/nar/gks1100. Epub 2012 Nov 29. PMID: 23203875; PMCID: PMC3531123.</p> <p><strong>U-BRITE LAST UPDATED June 19, 2023</strong></p>
MD_Simulations_Molecular_mechanisms_of_inorganic-phosphate_release_from_the_core_and_barbed_end_of_actin_filaments
<p>This repository contains the models, protocols, datasets and Jupyter notebooks to reproduce the computational experiments in the paper:</p> <p>"Molecular mechanisms of inorganic-phosphate release from the core and<br> barbed end of actin filaments"</p> <p>by W. Oosterheert, F.E.C Blanc, A. Roy, A. Belyy, M.B. Sanders,, O. Hofnagel, G. Hummer, P. Bieling, S. Raunser</p>
Fig. 6 in Astragalus species: Phytochemistry, biological actions and molecular mechanisms underlying their potential neuroprotective effects on neurological diseases
Fig. 6. The antiapoptotic effect of saponins in neurological diseases. They activate the PI3K/Akt survival pathway, promote the phosphorylationdependent inactivation of Bad, which leads to a decrease in caspasedependent neuronal apoptosis. Also, they maintain mitochondria integrity through modulation of p38 and mitogen-activated protein kinase (MEK) signalling pathways, which reduces the cytochrome c release and inhibits caspasedependent apoptosis (Wu et al., 2015).
Fig. 1 in Astragalus species: Phytochemistry, biological actions and molecular mechanisms underlying their potential neuroprotective effects on neurological diseases
Fig. 1. Chemical structures of the major constituents of triterpenoid saponins identified in Astragalus radix extract (Chu et al., 2010).
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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.