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871 results for “escherichia coli”
Data for the "Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli"
<p>This dataset contains raw and processed NMR data used in the publication.</p>
Figure 4 in Antimicrobial activity of noni fruit essential oil on Escherichia coli O157:H7 and Salmonella Enteritidis
Figure 4. The GC chromatogram of noni EO: 1. α-pinene; 2. camphene; 3. Methyl ester; 4. 2- heptanone; 5. Caprylic acid.
Figure 1 in Antimicrobial activity of noni fruit essential oil on Escherichia coli O157:H7 and Salmonella Enteritidis
Figure 1. The effect of noni EO on E. coli O157:H7 and S. Enteritidis using the direct spreading- plate method on the MIC value of noni EO towards both pathogens.
Figure 3 in Antimicrobial activity of noni fruit essential oil on Escherichia coli O157:H7 and Salmonella Enteritidis
Figure 3. The survival of E. coli O157:H7 and S. Enteritidis as affected by noni EO in TBS after a treatment for 16 hours.
Figure 2 in Antimicrobial activity of noni fruit essential oil on Escherichia coli O157:H7 and Salmonella Enteritidis
Figure 2. The effect of noni EO on E. coli O157:H7 and S. Enteritidis using the broth dilution method in TBS to determine the MBC value of noni EO against both pathogens.
45,671 Escherichia coli genomes
<h1>45,671 <em>Escherichia coli</em> genomes</h1> <p>This upload contains 45,671 high-quality <em>E. coli</em> assemblies collected from multiple sources with emphasis on improving coverage of commensal <em>E. coli</em> and adding more sequences from underrepresented countries.</p> <p>The assemblies are compressed using the <a href="https://github.com/refresh-bio/agc">Assembled Genomes Compressor AGC</a> which achieves a >10x better compression ratio over plain gzip and allows quick retrieval of one or more sequences. Instructions for decompressing the files are provided below.</p> <h2>Usage</h2> <h3>Extracting the AGC archive</h3> <ul> <li>Install AGC version 3.0 or newer from bioconda or by downloading a precompiled binary from <a href="https://github.com/refresh-bio/agc/releases">https://github.com/refresh-bio/agc/releases</a>.</li> <li>Run `agc listset 45k_E_coli_genomes.agc > 45k_E_coli_genomes_filenames.txt` to list the assemblies in the archive.</li> <li>To extract a single assembly, run `agc getset -p -o assembly_name.fa 45k_E_coli_genomes.agc assembly_name.fa`.</li> <li>To extract all assemblies, run `cat 45k_E_coli_genomes_filenames.txt | xargs -I {} agc getset -o {} -p 45k_E_coli_genomes.agc {}` <ul> <li><strong>warning</strong> extracting the whole archive this way will take ~240GB disk space.</li> <li><strong>note</strong> remember to use the `-p` toggle with agc to reduce the runtime.</li> <li><strong>parallelise</strong> the extraction by running `parallel -j <number of threads> 'agc getset -o {} -t 1 -p 45k_E_coli_genomes.agc {}' < 45k_E_coli_genomes_filenames.txt`.</li> </ul> </li> </ul> <h3>Metadata</h3> <p>The file `45k_E_coli_metadata.tsv` details which multilocus sequence type (ST) and clonal complex (CC) the assemblies belong to, assigned using the ecoli#1 scheme in <a href="https://github.com/EnzoAndree/FastMLST">fastmlst v0.0.15</a>, the phylogroup the ST belongs to according to <a href="https://doi.org/10.1099/mgen.0.000499">Horesh et al. (2021)</a>, and the presence of at least 17 of the 19 pks island genes (pks+) and/or the clbS gene encoding the colibactin self-resistance protein ClbS which were determined using the <a href="https://github.com/tmaklin/clbtype">clbtype</a> scripts.</p> <h3>Distribution</h3> <p>You are free to use the assemblies, provided that the sources are cited accordingly.</p> <h3>Citation</h3> <p>This collection was first published as part of the study "Geographical variation in the incidence of colorectal cancer and urinary tract cancer is associated with population exposure to colibactin-producing <em>Escherichia coli</em>" in <em>Lancet Microbe</em> on 5 December 2024, doi: <a href="https://doi.org/10.1016/j.lanmic.2024.101015">10.1016/j.lanmic.2024.101015</a>.</p> <h2>Methods</h2> <h3>Definition of <em>Escherichia coli</em></h3> <p>In the context of these files, <em>E. coli</em> is defined as all assemblies belonging to the same 97% pangenome average nucleotide identity (panANI) cluster which is comparable to the more traditional definition of a bacterial species as 95% ANI clusters. This definition is consistent with the one provided in <a href="https://gtdb.ecogenomic.org/">GTDB Release 09-RS220 (24th April 2024)</a>.</p> <p>The 97%-panANI clusters were defined by running <a href="https://github.com/tmaklin/panaani">panaani v0.1.0</a> on the quality filtered source data referenced below.</p> <h3>Source data</h3> <p>Assemblies from the following studies were considered for inclusion:</p> <ul> <li>661k genomes collection from <a href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001421https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001421">Blackwell et al. 2021</a></li> <li>Avian <em>E. coli</em> <a href="https://www.nature.com/articles/s41467-021-20988-w">genomes</a></li> <li>Bin-assembled <em>E. coli</em> from <a href="https://doi.org/10.1016/j.cell.2022.10.011">Bangladesh</a>, <a href="https://doi.org/10.1016/j.cell.2022.11.023">Finland</a>, <a href="https://www.nature.com/articles/s41467-024-49591-5">Pakistan</a>, <a href="https://www.nature.com/articles/s41586-019-1560-1">UK</a>, <a href="https://www.nature.com/articles/s41467-023-36135-6">Zimbabwe</a></li> <li>Carbapenemase producing <em>E. coli</em> from <a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200774">Norwegian travellers</a></li> <li><em>E. coli</em> genomes from <a href="../records/12528310">a previous index</a></li> <li><em>E. coli</em> isolated from inpatients in <a href="https://link.springer.com/article/10.1186/s13756-018-0361-x">Tanzania</a></li> <li><em>E. coli</em> from various sources in <a href="https://www.nature.com/articles/s41564-022-01079-y">Kenya</a></li> <li><em>E. coli</em> from a one health study in <a href="https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(23)00208-2/fulltext">Ghana</a></li> <li><em>E. coli</em> from bloodstream infections in <a href="https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000863">Nigeria</a></li> <li>Enteropathogenic <em>E. col</em><em>i </em>from <a href="https://www.nature.com/articles/s41564-018-0217-4#Sec10">Sub-Saharan Africa and South Asia</a></li> <li>Enteropathogenic <em>E. coli</em> isolated from cattle in <a href="https://www.sciencedirect.com/science/article/pii/S0168160522000265#s0050">South Africa</a></li> <li>ESBL positive <em>E. coli</em> from <a href="https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.752883/full">Benin</a></li> <li>ESBL producing <em>E. coli</em> and <em>Klebsiella</em> from rats in <a href="https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.00150/full">Guinea</a></li> <li>ESBL producing <em>E. coli</em> from European soldiers deployed in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198576/">Mali</a></li> <li>ESBL producing<em> E. coli</em> from <a href="https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.001035">Malawi</a></li> <li>GTDB representative genomes from <a href="https://data.gtdb.ecogenomic.org/releases/release214/">Release 214</a></li> <li>Livestock <em>E. coli</em> from <a href="https://journals.asm.org/doi/full/10.1128/mbio.02693-18">England</a></li> <li>Metagenome-assembled genome from <a href="https://www.cell.com/cell/fulltext/S0092-8674(23)00597-4">Hadza hunter-gatherers</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/chicken-gut/v1.0/">chicken gut v1.0</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/cow-rumen/v1.0/">cow rumen v1.0</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/human-gut/v2.0.1/">human gut v2.0.1</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/human-oral/v1.0/">human oral v1.0</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/human-vaginal/">human vaginal v1.0</a></li> <li>Mgnify <a href="https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/pig-gut/">pig gut v1.0</a></li> <li>One health study in the <a href="https://www.sciencedirect.com/science/article/pii/S2352771423000381">US</a></li> <li><em>Salmonella</em> genomes from water in <a href="https://www.nature.com/articles/s41598-022-08200-5">Lake Victoria</a></li> </ul> <h3>Quality filtering</h3> <p>An assembly met the criteria for high-quality if the following conditions were met:</p> <ul> <li><a href="https://github.com/Ecogenomics/CheckM">checkm v1.2.2</a> completeness >=90%</li> <li><a href="https://github.com/Ecogenomics/CheckM">checkm v1.2.2</a> contamination <= 10%</li> <li><a href="https://github.com/grp-bork/gunc">gunc v1.0.5</a> chimerism analysis score "pass"</li> </ul>
Escherichia coli DNA replication study: processed alignment data
<p>Genomes are replicated by large protein complexes called replisomes. In bacterial DNA replication, two replisomes replicate the DNA starting from the same origin site and proceeding in opposite directions. Understanding their movement in vivo has been challenging. We used quantitative genome sequencing to characterize the dynamics of bacterial replisomes at 5 different temperatures (17, 22, 27, 32 and 37 °C) in exponential growth (3 replicates) or in stationary phase (one experiment at 17, 27 and 37 °C).</p> <p>The data deposited here give the coordinates of the sequence reads (deposited under the BioProject PRJNA772106) covering the Escherichia coli str. K-12 substr. MG1655 complete genome (accession number U00096.3).</p> <p>The file archive contains data files for each sample, at nucleotide resolution and binned in intervals of 10,000 base pairs. It also contains a C program to perform the binning and a README summarising how the alignment was done. <em>Please note that once uncompressed, the data will take 5 Gb of disks space in total.</em></p>
Transcriptome-wide meta-analysis of codon usage in Escherichia coli
<p>Data generated by the CUBseq pipeline on Escherichia coli RNA-seq data.</p>
In vivo screening of Lrp-type transcription factors in Escherichia coli
<p>This dataset contains the raw data that lie at the basis of the results discussed in <strong>Chapter 4: <em>In vivo</em> screening of Lrp-type transcription factors in <em>Escherichia coli</em></strong><strong> </strong>of the PhD thesis of Amber Bernauw. The README.txt file provides more information on the different data files.</p>
Comparison of heterologous β-alanine-responsive biosensors in Escherichia coli
<p>This dataset contains the raw data that lie at the basis of the results discussed in <strong>Chapter 6: Comparison of heterologous β-alanine-responsive biosensors in <em>Escherichia coli</em> </strong>of the PhD thesis of Amber Bernauw. The README.txt file provides more information on the different data files.</p>
Design of a redox-proficient Escherichia coli for screening terpenoids and modifying cytochrome P450s
<p>20 Terpenoid scaffold.zip: Raw GCMS data for titer comparisons. Required to reproduce Figure 2b.</p> <p>20 Terpenoid scaffold_representative GCMS.zip: Representative GCMS data for 20 terpenoid scaffolds produced by E. coli MEV15 and 20. Required to reproduce Supplementary Figure 3.</p> <p>Characterization of LRD04 derivatives.zip: Raw NMR and GCMS data for 19 ent-kaurenoid derivatives described in this manuscript. Required to reproduce Supplementary Figures 13-33.</p> <p>LRD production optimization.zip: Raw GCMS data for pLRD construct screening with different IPTG concentration. Required to reproduce Supplementary Tables 9-36.</p> <p>LRD scaffolds.zip: Raw GCMS data for LRD production after optimization. Required to reproduce Figure 5 and Supplementary Tables 9-36.</p> <p>Pathway screening.zip: Raw GCMS data for screening biosynthetic pathways of LRD scaffold paired with 64 CYPs. Each GCMS dataset is consisted of 1 pathway producing only LRD scaffold, 64 pathways producing LRD scaffold and different CYPs, and 2 alkane-series standards acquired before and after analyzing the 65 pathways. Required to reproduce Figure 6 and Supplementary Tables 9-36.</p> <p>Redox array characterization 1.zip: Raw GCMS data for comparison of modified terpenoid production in the presence of different redox enzymes. Required to reproduce Figure 3a.</p> <p>Redox array characterization 1_scaffold.zip: Raw GCMS data for comparison of terpenoid scaffold production in the presence of different redox enzymes. Required to reproduce Supplementary Figure 4.</p> <p>Redox array characterization 2.zip: Raw GCMS data for comparison of modified terpenoid production with or without an additional copy of fldA/fpr. Required to reproduce Figure 3b and Supplementary Figure 5.</p> <p>Terpenoid inducer optimization.zip: Raw LCMS and GCMS data for optimizing 01a and 03a production in E. coli MEV20. Required to reproduce Figure 4 and Supplementary Figures 6 and 7.</p> <p>terpenoids_code.zip: Jupyter Notebook used for analyzing GNN results and metabolomic analysis (also available at <a href="https://github.com/gengminlin/terpenoids">https://github.com/gengminlin/</a><a href="https://github.com/gengminlin/GNN-and-Metabolomics-Analysis-for-LRD">GNN-and-Metabolomics-Analysis-for-LRD</a>) </p> <p> </p>
Data from: Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis
Open the record for dataset details and reuse information.
Genomic and phenotypic evolution of Escherichia coli in a novel citrate-only resource environment
Evolutionary innovations allow populations to colonize new ecological niches. We previously reported that aerobic growth on citrate (Cit+) evolved in an Escherichia coli population during adaptation to a minimal glucose medium containing citrate (DM25). Cit+ variants can also grow in citrate-only medium (DM0), a novel environment for E. coli. To study adaptation to this niche, we founded two sets of Cit+ populations and evolved them for 2500 generations in DM0 or DM25. The evolved lineages acquired numerous parallel mutations, many mediated by transposable elements. Several also evolved amplifications of regions containing the maeA gene. Unexpectedly, some evolved populations and clones show apparent declines in fitness. We also found evidence of substantial cell death in Cit+ clones. Our results thus demonstrate rapid trait refinement and adaptation to the new citrate niche, while also suggesting a recalcitrant mismatch between E. coli physiology and growth on citrate.
Infezioni da Escherichia coli verocitotossici: fattori di rischio e prevenzione
<p>The goal of this video is to highlight the risk factors of <em>E. coli </em>pathogenic strains transmission such as VTEC to humans, providing the consumers with the knowledge and skills to prevent VTEC infections, particularly focusing it on food safety. <em>Escherichia coli</em> pathogenic strains called Verotoxin-producing <em>Escherichia coli</em> or Shiga toxin-producing <em>Escherichia coli</em> (VTEC/STEC) are a significant food-borne public health hazard in Europe. In fact, VTEC was the fourth most commonly reported zoonosis in the EU in 2017, with 6073 confirmed human cases. VTEC infections have been associated with a wide range of symptoms from uncomplicated diarrhea to hemolytic-uremic syndrome (HUS). HUS is a serious disease that affect children, the elderly and immunosuppressed individuals. The most of the human infections are linked to 5 serogroups, called top five (O157, O26, O103, O145, and O111). In recent years in Apulia region (Southern Italy), there has been an increase of the number of VTEC infections and in 2013 the largest European epidemic outbreak caused by <em>E. coli </em>O26 exploded (Germinario et al., 2016); in 2016 and 2018 there were also 3 deaths affecting pediatric patients. In all cases the source of infection has never been identified. The Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata with funding from the Italian Ministry of Health has conducted numerous research projects on VTEC both for the development of new diagnostic protocols and for epidemiological investigations in order to assess the risk in the various food and environmental matrices (RC IZSPB 02/13, RC IZSPB 03/16, RC IZSPB 02/17). VTEC monitoring and surveillance programs are necessary for risk assessment purposes and to prevent and reduce the transmission of VTEC to consumers. All operators of food sector must work in an integrated way and food control systems must be applied at all stages of the food chain to ensure food safety.</p>
Environment dependent costs and benefits of recombination in independently evolved populations of Escherichia coli
Understanding of the causes by which reproductive isolation arises remains limited. We examine the role of adaptation in driving reproductive isolation among 12 Escherichia coli populations evolved in two different environments. We found that, regardless of whether parents were selected in the same or different environments, the average fitness of recombinants was lower than the expected, consistent with a prevailing influence of incompatibility between independently accumulated mutations. Exceptions to this pattern occurred among recombinants of some parents evolved in different environments. These recombinants were less fit than expected in the selective environment of one parent, but more fit than expected in the selective environment of the other parent. Our results indicate that both parallel and divergent adaptation can quickly lead to intrinsic genetic barriers contributing to the initial stages of speciation and show that these barriers can be complex, for example, depending on the environment in which recombinant offspring are tested.
A Test of the Repeatability of Measurements of Relative Fitness in the Long-Term Evolution Experiment with Escherichia coli
<p>Experimental studies of evolution using microbes have a long tradition, and these studies have increased greatly in number and scope in recent decades. Most such experiments have been short in duration, typically running for weeks or months. A venerable exception, the long-term evolution experiment (LTEE) with <i>Escherichia coli</i> has continued for 30 years and 70,000 bacterial generations. The LTEE has become one of the cornerstones of the field of experimental evolution, in general, and the BEACON Center for the Study of Evolution in Action, in particular. Science laboratories and experiments usually have finite lifespans, but we hope that the LTEE can continue far into the future. There are practical issues associated with maintaining such a long-term experiment. One issue, which we address here, is whether key measurements made at one time and place are reproducible, within reasonable limits, at other times and places. This issue comes to the forefront when one considers moving an experiment like the LTEE from one lab to another. To that end, the Barrick lab at the University of Texas at Austin, measured the fitness values of samples from the 12 LTEE populations at 2,000, 10,000, and 50,000 generations and compared the new data to data previously obtained at Michigan State University. On balance, the datasets agree very well. More generally, this finding shows the value of simplicity in experimental design, such as using a chemically defined growth medium and appropriately storing samples from microbiological experiments. Even so, one must be vigilant in checking assumptions and procedures given the potential for uncontrolled factors (e.g., water quality) to affect outcomes. This vigilance is perhaps especially important for a trait like fitness, which integrates all aspects of organismal performance and may therefore be sensitive to any number of subtle environmental influences.</p>
Whole-cell Escherichia coli lactate biosensor for monitoring mammalian cell cultures during biopharmaceutical production-- dataset
<p>Raw data for Goers et al, (2017), Biotech Bioeng. doi:10.1002/bit.26254.</p> <p>Each graph including the supplementary figures is a separate tab.</p>
Quantification of phosphorylated metabolites, organic acids, and intermediates of the TCA cycle using capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) following treatment of Escherichia coli with ciprofloxacin
<p>Capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) was used to quantify phosphorylated metabolites, organic acids, and intermediates of the TCA cycle of Escherichia coli treated with ciprofloxacin, BTP-001 (a novel antimicrobial peptide), and a combination of the two . Metabolite extracts were analyzed with a Xevo TQ-XS triple quadrupole mass spectrometer (Waters, USA).</p><p>Samples were gathered from E. coli cultures grown in batch cultivations using 1 liter bioreactors. Briefly, intracellular metabolites were extracted by cycling samples between −20 °C EtOH and N2 (<i>l</i>) in three consecutive freeze–thaw cycles, with vortexing every 10 min during the thawing phase. Filters were removed and the cell debris was pelleted (4500 rcf, 10 min, -9 °C). The supernatants were transferred to a new tube, snap frozen in N2 (<i>l</i>), and lyophilized. Lyophilized extracts were reconstituted in 500 µL cold Milli-Q H2O and cleared by spin-filtration with a 10 kDa molecular cutoff (20817 rcf, 10 min, 0 °C). A mix of 80 µL centrifuged sample and 20 µL 13C-labeled ISTD extract from yeast was sent to analysis. </p><p>Data processing and absolute quantification was performed as earlier described using the TargetLynx application manager of MassLynx v 4.1 (Waters) to interpolate calibration curves made with appropriate dilutions of analytical grade standards (Sigma-Aldrich). The response factor of the corresponding U13C-isotopologues were used to correct the standard and sample extract response factors. Extract concentrations were normalized to the CDW, which was calculated from interpolation of the OD600 vs. CDW (g/L) curve. </p><p>Further statistical analysis in MetaboAnalyst v 5.0 replaced missing values with 1/5 of the minimum value of the respective metabolite. An unpaired T-test with unequal variance determined differential enriched metabolites with a false discovery rate (FDR) < 0.05 which are presented as log2 fold-change compared to control. </p>
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
<p><strong><span>Background</span></strong></p> <p><span>Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in <em>Escherichia</em> <em>coli</em>, regarded as a sentinel organism.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced <em>E</em>. <em>coli</em> isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock.</span></p> <p><strong><span>Findings</span></strong></p> <p><span>We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.</span></p>
Disinfectant efficacy on mixed biofilms comprising Escherichia coli and spoilage microorganisms
<p>This study aimed to investigate the impact of temperature and the presence of other microorganisms on the susceptibility of STEC to biocides. Mature biofilms were formed at both 10°C and 25°C. An inoculum of planktonic bacteria comprising 10<sup>6</sup> CFU/ml of spoilage bacteria and 10<sup>3</sup> CFU/ml of a single <em>E. coli</em> strain (O157, O111, O103, and O12) was used to form mixed biofilms. The following bacterial combinations were tested: T1: <em>Carnobacterium piscicola</em> + <em>Lactobacillus bulgaricus</em> +STEC, T2: <em>Comamonas koreensis</em> + <em>Raoultella terrigena </em>+ STEC, and T3: <em>Pseudomonas aeruginosa</em> + <em>C. koreensis</em> + STEC. Tested biocides included quaternary ammonium compounds (Quats), sodium hypochlorite (Shypo), sodium hydroxide (SHyd), hydrogen peroxide (HyP), and BioDestroy®-organic peroxyacetic acid (PAA). Biocides were applied to 6-day-old biofilms. Minimum Bactericidal Concentrations (MBC) and Biofilm Eradication Concentrations (BEC) were determined. Planktonic cells and single-species biofilms exhibited greater susceptibility to sanitizers (P < 0.0001). <em>Lactobacillus</em> and <em>Carnobacterium</em> were more susceptible than the rest of the tested bacteria (P < 0.0001). Single species biofilms formed by <em>E. coli</em> O111, O121, O157, and O45 showed resistance (100%) to Shypo sanitizer (200 ppm) at 25°C. From the most effective to the least effective, sanitizer performance on single-species biofilms was PAA > Quats > HyP > SHyd > Shypo. In multi-species biofilms, spoilage bacteria within T1, T2, and T3 biofilms showed elevated resistance to SHyd (30%), followed by quats (23.25%), HyP (15.41%), SHypo (9.70%), and BioDestroy® (3.42%) (P < 0.0001). Within T1, T2, and T3, the combined STEC strains exhibited superior survival to Quats (23.91%), followed by HyP (19.57%), SHypo (18.12%), SHyd (16.67%), and BioDestroy® (4.35%) (P < 0.0001). O157:H7-R508 strains were less tolerant to Quats and Shypo when combined with T2 and T3 (P < 0.0001). O157:H7 and O103:H2 strains in mixed biofilms T1, T2, and T3 exhibited higher biocide resistance than the weak biofilm former, O145:H2 (P < 0.0001). The study shows that STEC within multi-species biofilms' are more tolerant to disinfectants.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.