This database has been designed to meet the growing need to identify fluorophore contaminants in environmental matrices. It gathers spectral fingerprints for 108 fluorescent micropollutants, including hormones (19), pharmaceuticals (41) and pesticides (48). These spectral fingerprints, covering a wide spectral range, enable contaminants to be effectively distinguished from strongly fluorescing organic matter present in environmental samples. The data were obtained by preparing individual solutions of compounds, measuring the fluorescence spectra, and processing the data to eliminate interferences. Each compound is accompanied by full metadata, and the information is available online. This database constitutes a valuable tool for environmental monitoring, offering a comprehensive resource for the identification and characterization of fluorescent pollutants in environmental matrices.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 23, 2017.Annotated database of fluorescence microscope images depicting subcellular location proteins with two interfaces: a text and image content search interface, and a graphical interface for exploring location patterns grouped into Subcellular Location Trees. The annotations in PSLID provide a description of sample preparation and fluorescence microscope imaging.

DescriptionThese files contain data from the article "Local fitness landscape of the green fluorescent protein". Raw sequencing data for this experiment is available at SRA (http://www.ncbi.nlm.nih.gov/sra) under BioProject PRJNA282342 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA282342/). Files presented here are data sets obtained at different stages of analysis (as illustrated in the file "Data_low.png"). All files are tab-separated tables with a header at first row. Some table cells may be empty (e.g. list of mutations for wild-type).Please note the mutations notation used throughout the files. It is described in details here: http://mixcr.readthedocs.org/en/latest/appendix.html#alignment-and-mutations-encoding. Briefly, all positions are zero-based (i.e. first nucleotide has index 0) and type of mutation (substitution, deletion or insertion) is indicated as the first letter of mutation description. For example, SG101A is the substitution G>A at position 101. The reference avGFP sequence is provided as “avGFP_reference_sequence.fa” file.File names and content1. Final data sets: genotypes with corresponding log-brightness valuesnucleotide_genotypes_to_brightness.tsv – processed file “barcodes_to_brightness.tsv”, with genotypes aggregated by their nucleotide sequence, and brightness information averaged across all barcodes that share the same nucleotide genotype.Columns:nMutations – list of nucleotide mutations (see above for mutations notation); empty for wild-type,aaMutations – list of amino acid mutations; empty for wild-type or genotypes with only synonymous substitutions,uniqueBarcodes – number of unique barcodes sharing the same nucleotide genotype,medianBrightness – median of log-brightness values across barcodes that share the same nucleotide genotype,std – standard deviation of log-brightness values across barcodes that share the same nucleotide genotype; empty for genotypes represented by a single barcode.amino_acid_genotypes_to_brightness.tsv – processed file “barcodes_to_brightness.tsv”, with genotypes aggregated by their amino acid sequence, and brightness information averaged across all barcodes that share the same amino acid genotype.Columns:aaMutations – list of amino acid mutations; empty for wild-type,uniqueBarcodes – number of unique barcodes sharing the same amino acid genotype,medianBrightness – median of log-brightness values across barcodes that share the same amino acid genotype,std – standard deviation of log-brightness values across barcodes that share the same amino acid genotype; empty for genotypes represented by a single barcode.2. Intermediate data set: estimated brightness values for each barcode.For details of brightness estimation please see the protocol in the original paper.barcodes_to_brightness.tsv – final data set containing aggregated, clean and filtered data on genotypes with substitutions only (no indels).Columns:barcode – molecular barcode sequence of the genotype,nMutations – list of nucleotide mutations (see above for mutations notation),aaMutations – list of amino acid mutations,brightness – log-brightness of the barcoded sequence.3. Early data set: processed raw sequencing datapopulations.zip archive contain files with names in the following form: L{k}R{m}.tsv. The files contain aggregated read counts of barcodes for each particular sorted population, where {k} is the index of sorting gate and {m} is the index of replica. For example, file L1R2.tsv contains counts for barcodes found in brightness population L1 in experimental replica R2. (see below for median sorting gate brightness values).Files with {k} = 0 (e.g. L0R1.tsv) contain results of sequencing of bacterial population before sorting.Columns:barcode - molecular barcode sequence (see protocol in original paper),count - number of occurrence of this barcode in sequences for particular sorted population,minQuality - minimal phred quality for barcode sequence.Important: please see “Normalization” section below that describes how we translated read counts into the number of cells for each barcode.genotypes.tsv – contains processed Illumina MiSeq sequencing data of GFP genotypes for each barcode (genotype to barcode correspondence).Columns:barcode – molecular barcode sequence of the genotype,minCoverage – minimal coverage of target GFP sequence by sequencing reads (see protocol in the paper),meanCoverage – mean coverage of target GFP sequence by sequencing reads,nMutations – list of nucleotide mutations (see above for mutations notation),aaMutations – list of amino acid mutations for genotypes without indels, empty string (!) for genotypes with indels.Information on data processingThe data processing workflow is outlined in the file “Data_low.png”. We processed data from Illumina MiSeq sequencing run to reconstruct full-length sequences of GFP and relate each GFP sequence to the corresponding barcode. We then analyzed Illumina HiSeq sequencing of cell populations sorted by fluorescence-activated cell sorting, for each of the four replicas of the experiment. We counted reads that each barcoded genotype has in each brightness population. We then fitted each barcode distribution with two Gaussian distributions using the values of logarithms of sorting gates medians. When aggregating information from replicas we eliminated barcodes that displayed too broad distribution across the brightness populations or had conflicts between replicas. We saved resulting filtered data into the file “barcodes_to_brightness.tsv”.NormalizationA fixed number of cells with known barcodes (AAGTTCTAAATAACAATCCC, AATACCAGTAAGGACTTAA, TATGGTACTTAATTTACAGT, TATTTACGGGTATGACTGGG) was added to every population after sorting, about 1333 cells for each barcode. These cells passed all sample preparation procedures together with the library being a control for each sample in each replica. When analysing the sequencing data, we used these controls to translate the number of reads per barcode to the number of sorted cells. Barcodes with less than three cells across the population samples were later removed at the data filtering stage.Estimation of brightnessFor some of the barcodes a bimodal distribution of cells across the fluorescence gate populations was observed. These distributions were not reproduced across experimental replicas, indicating that they represent an artifact of the experimental procedure rather than inherent genotype properties. We fitted each barcode distribution within each replica with two Gaussian distributions using actual values of logarithms of sorting gates boundaries. Thus, the resulting distributions parameters were expressed in actual brightness logarithm values. We filtered out the cases where the log-value of fluorescence of the major Gaussian component was below 0.65, or its sigma exceeded 0.4. When aggregating information from replicas we eliminated barcodes for which less than three replicas belonged to the ±0.45-neighbourhood of the median value calculated across all replicas.The following median values of brightness within sorting gates were used to estimate the brightness of the genotypes:Replica 0 (from L1 to L8): 10751, 5970, 3190, 1372, 418, 179, 81, 20,Replica 1 (from L1 to L8): 16278, 9189, 4942, 1817, 433, 179, 72, 20,Replica 2 (from L1 to L8): 7984, 5914, 3207, 1337, 428, 160, 69, 20,Replica 3 (from L1 to L8): 12989, 6864, 3522, 1377, 414, 147, 58, 20.Please see the original paper for the description of level and structure of the noise in the final estimations of log-brightness.

SUBA (http://suba.live/) is the central resource for Arabidopsis protein subcellular location data. Proteins have specific functions and locations within the plant cell. They generate or are themselves products important for plant growth and response. Protein subcellular location and the proximity relationship of proteins are important clues to function within the metabolic household. Subcellular location can be determined by fluorescent protein tagging or mass spectrometry detection in subcellular purifications and by prediction using protein sequence features. SUBA provides a subcellular data query platform, protein sequence BLAST alignment, a high confidence subcellular locations reference standards and analytic tools.

To quantify the monomeric state of the selected FPs, we followed the OSER assay protocol described previously​9​. Briefly, HeLa cells were cultured in the same condition as described above. Cells were seeded on glass-bottom 24-well plates pre-coated with Matrigel (356235, BD Biosciences) 3 hours before transfection. 500 ng of each pCytERM-FPs plasmid were transfected at 40-50% confluency using Lipofectamine 3000 (Invitrogen, USA) or Hieff Trans Liposomal Transfection Reagent (Yeasen, 40802ES02) according to the manufacturer’s protocol. The experimenter was blinded to the plasmid identities, as all plasmid vials were barcoded, and their identities were revealed only after data collection and analysis were complete. Samples were transfected and imaged in a random order, with plasmid vials pooled together and randomly selected during transfection. Each independent transfection was also randomized separately. Transfected live cells were imaged on a CSU-W1 SoRa imaging setup of Nikon Spinning-Disk Field Scanning Confocal System (Nikon, Japan) 20-24 hours after transfection. Image analysis was performed by three blinded independent researchers, using NIS Elements software, and mean values with standard errors were calculated. Positive cells selected for analysis include cells with clear nuclear structure, dead or highly stressed, and out-of-focus cells were excluded from analysis. Cells with non-spherical nuclei or condensed nuclei were counted as stressed cells and excluded from normal cells fraction.

This dataset contains data that was used for calculating quantum yields of fluorescent proteins for the publication entitled "Absolute quantum yield measurements of fluorescent proteins using a plasmonic nanocavity" by Ruhlandt et al. as well as the matlab routine for their analysis.

The subcellular location database for Arabidopsis proteins SUBA3, http://suba.plantenergy.uwa.edu.au) combines manual literature curation of largescale subcellular proteomics, fluorescent protein visualization and protein–protein interaction (PPI) datasets with subcellular targeting calls from 22 prediction programs. Overall, nearly 650 000 new calls of subcellular location for Arabidopsis proteins (TAIR10) are included. To determine as objectively as possible where a particular protein is located, we have developed SUBAcon, a Bayesian approach that incorporates experimental localization and targeting prediction data to best estimate a protein’s location in the cell.

Dataset and R source code from the article "Predicting the Oligomeric States of Fluorescent Proteins".

Below are the original western blot, gel, and microscopy images covered in this paper. The uncropped western blot and gel pictures are in PDF file, and the original microscopy photos are in zip file.

Data supporting the calculations in the paper. These include the input and output files for the Quantics program to run DD-vMCG and iMCG simulations of a GFP cluster model, as well as the database files with the quantum chemistry results. These files can be used together with Quantics to generate the data in the paper Bourne-Worster and Worth (JCP, 160, 065102, 2024). The input files are standard ascii files, grouped into directories for the different systems studied. The databases with the points calculated during the direct dynamics simulations are SQLite format with tables for geometries, energies, gradients etc. More details are in the paper. The Quantics program is a mostly Fortran code for running quantum dynamics simulations. It is open source and runs on linux workstations. It is freely available on request to the authors of the paper. For further details of the program see Comp. Phys. Comm., 248:107040–15, 2020.

This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.

These data sets can be used to run the following macros that can be found on GitHub:

1. https://github.com/molcyto/MC-Ratio-96-wells
2. https://github.com/molcyto/MC-Ratio-Petri-dish
3. https://github.com/molcyto/MC-FLIM-Petri-dish
4. https://github.com/molcyto/MC-Bleach-96-wells
5. https://github.com/molcyto/MC-Scatter5D
6. https://github.com/molcyto/MC-FLIM-96-wells

Funding:
This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands

Aircraft flights over the Chukchi and Beaufort Seas during the last half of July, 2017 collected fluorescent aerosol and meteorological data using a WIBS-4A (Droplet Measurement Technologies) instrument which measured aerosol fluorescence and an AIMMS (Aventech Research) instrument which recorded positional and meteorological variables. Text files of data recorded during flight are included. WIBS data provides single particle optical size and fluorescence characteristics for all particles sampled that were larger than ~0.5 microns in diameter. AIMMS data provides location and meteorological data used in the analysis.

625 Global import shipment records of Fluorescent Microscope with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

France Imports from Austria of Synthetic Organic Coloring Matter, Synthetic Organic Fluorescent Brightening was US$1.53 Million during 2024, according to the United Nations COMTRADE database on international trade.

Melike Berksoz, Canan Atilgan*

Faculty of Engineering and Natural Sciences, Sabanci University

*Correspondance: Canan Atilgan, Faculty of Natural Sciences and Engineering, Sabancı University, Tuzla 34956 Istanbul, Türkiye, E-mail: canan@sabanciuniv.edu

Genetically Encoded Fluorescent Biosensors (GEFBs) have become indispensable tools for visualizing biological processes in vivo. A typical GEFB is composed of a sensory domain (SD) which undergoes a conformational change upon ligand binding and a genetically fused fluorescent protein (FP). Ligand binding in the SD allosterically modulates the chromophore environment and changes its spectral properties. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable and simple due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD which effect how well the chromophore responds to conformational change in the SD. In this work, we use classical all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.

Hydrogen bond occupancies were calculated with merging_bonds.py script. Double counted hydrogen bonds where a residue acts both as acceptor and donor are merged into a single entry with merge_files.py. To run sasa.tcl, you need VMD software. Trajectories were created with NAMD2 with a dcdfrequency of 5000 timesteps (every 10 ps) and strided in a 1:100 ratio (every 1 ns=1 frame in dcd).

The main entity of this document is a structure with accession number 1gfl

The National Status and Trends (NSandT) Benthic Surveillance Fluorescent Aromatic Compounds (FAC) file reports the trace concentrations of Fluorescent Aromatic Compounds. The presence of FACs in fish liver and bile indicate exposure to toxins, such as polycyclic aromatic hydrocarbons (PAHs). The Benthic Surveillance Fluorescent Aromatic Compounds file is constructed as a horizontally formatted table.

14 Global import shipment records of Fluorescent Lamp with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Ireland Imports from Belgium of Synthetic Organic Coloring Matter, Synthetic Organic Fluorescent Brightening was US$774.24 Thousand during 2024, according to the United Nations COMTRADE database on international trade.

For the Arxiv Version of the Paper: Click here

Short Description of the Paper:

In this work, we designed a biocompatible, resource-efficient, and externally controllable experimental molecular communication (MC) system. It employs the green fluorescent protein variant "Dreiklang" (GFPD) as signaling molecule, which can be reversibly switched between two fluorescent states using light of specific wavelengths. Information transmission is facilitated by an optical transmitter and an optical eraser that can write and erase information, respectively, onto the state of GFPD, while the receiver reads the encoded state through fluorescence detection. The closed-loop configuration and extended experimental durations result in unique forms of inter-symbol interference (ISI) not observed in shorter or open-loop systems. We developed a dedicated communication scheme, incorporating blind transmission start detection, symbol-by-symbol synchronization, and an adaptive threshold detection supporting higher-order modulation. Moreover, we conducted the longest MC experiment to date, both with respect to the number of bit transmitted as well as the duration of the transmission, thereby setting a novel benchmark for long-term MC experiments.

Data and Code:

We have published our experimental data and the Python code for synchronization and detection here on Zenodo and in an accompanying GitHub repository under the CC BY and MIT licenses, respectively. The data is shared here in two forms: i) as a zip folder containing the raw data sorted by appearance in the paper (experiment_files.zip), i.e., sorted by the paper figure numbers, ii) as a SQLite database (mmtb.db). The SQLite database can be easily integrated into the code provided on GitHub. The GitHub repository also contains step-by-step instructions on how to install the code package. Researchers are welcome to develop their own synchronization and detection schemes using our dataset.

If you have any question or suggestions for improvements, feel free to contact us.

• Louis Wolf
  • Email: louis.wolf@fau.de
• Maike Scherer
  • Email: maike.scherer@fau.de
• Lukas Brand
  • Email: lukas.brand@fau.de

Further References:

This work was supported by DFG Project 290825040. For more information visit: Institute of Digital Communication, Institute of Bioprocess Engineering, Institute of Biochemistry, and Institute of Microbiology.

This work is also associated to the research training group 2950: Synthetic Molecular Communication Across Different Scales: From Theory to Experiments (SyMoCADS).