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47 results for “Chemotaxis”
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Source code and simulation datasets for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'
<p>Source code and simulation data files for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'</p> <p>The zipped folder 'SimulationCode.zip' contains Matlab source code files which have been used to generate the simulations in the paper: fixed attractant gradient, axisymmetric agar-plate like migration and transient attractant source. The file 'main.m' controls all simulations run with initial conditions specified in the files with suffix '_ic'. The file 'PDEsolver.m' specifies the finite difference solver used so solve the model PDEs, while 'FourthOrderFD.m' creates the matrices that are required<br> for the finite differnce solver. The chosen scheme is of fourth order accuracy.</p> <p>The zipped folder 'SimulationData.zip' contains Matlab data files generated by running the simulation code. The results correspont to the figures in the paper.</p>
Figure 3 in Chemotaxis of Caenorhabditis elegans Toward Volatile Organic Compounds from Stropharia rugosoannulata Induced by Amino Acids
Figure 3: Chemotaxis (percent attracted) and mortality (percent of attracted worms dead) in the groups supplemented with L-phenylalanine or L-tryptophan and the control without amino acids. The error bars indicate standard deviation. The statistical differences were analyzed using one-way ANOVA, *P <0.05, **P <0.01.
Figure 2 in Chemotaxis of Caenorhabditis elegans Toward Volatile Organic Compounds from Stropharia rugosoannulata Induced by Amino Acids
Figure 2: GC-MS total ion chromatography of different samples. A: L-phenylalanine alone, strain 1.202 alone and strain cultured on water agar plus L-phenylalanine, benzaldehyde was increased and 1-Octen-3-ol was newly produced from strain 1.2052 cultures added L-phenylalanine; B: strain 1.202 alone, L-tyrosine alone and strain cultured on water agar plus L-tyrosine, benzaldehyde was decreased and 1-Octen-3-ol and indole were newly produced were produced from strain 1.2052 cultures added L-tyrosine.
Figure 1 in Chemotaxis of Caenorhabditis elegans Toward Volatile Organic Compounds from Stropharia rugosoannulata Induced by Amino Acids
Figure 1: Chemotaxis (percent attracted) of Caenorhabditis elegans toward Stropharia rugosoannulata stain 1.2052 cultured on water agar supplemented with amino acids. Controls are phenylalanine or tyrosine alone and strain 1.2052 alone. The error bars indicate standard deviation. The statistical differences were analyzed using one-way ANOVA, *P<0.05, **P<0.01.
Data from: Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis
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Data for: Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model
<p>Chemotactic bacteria not only navigate chemical gradients but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real-time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.</p>
Zebrafish larvae exploration and aversive chemotaxis dataset
<p>This dataset contains recordings of larval zebrafish behavior. The full details are described in the paper "A lexical approach for identifying behavioral action sequences". </p> <p>The experiment investigates zebrafish larvae behavior in free swimming and aversive chemotaxis conditions. In each experiment, 12 larvae (7 dpf) are placed in 12 rectangular wells. Ten min-long videos were recorded at 160 Hz with an exposure time of 1 ms, and a pixel size of 70 µm using a ViewWorks camera (Basler acA2040-180km) controlled by the Hiris software (R&D Vision, Nogent sur Marne, http://www.rd-vision.com/r-d-vision-eng).</p> <p>The fish are tracked using a custom-made software, Zebrazoom (https://zebrazoom.org/). The algorithm begins by locating all the wells and by extracting the background of the video. ZebraZoom first applies a series of actions to detect the animal in each well: i) contours of head and entire body are detected using active contours, ii) the center of the head is identified as the center of mass of the head contour and the tip of the tail is detected using both the curvature along the body contour and distance to the center of the head. The midline is then identified between the left and right borders of the body contour. For each animal, the difference in pixel intensity between subsequent frames enables the automated detection of bout start and end. Then, for each bout, the algorithm calculates the head position, head direction and the tail angle from which kinematic parameters are subsequently estimated: number of oscillations, instantaneous tail beat frequency, maximum amplitude for each tail bend, bout speed, bout duration, and bout distance. Tunable parameters in the tracking algorithm were optimized to detect small amplitude forward bouts occurring frequently during exploration. In order to validate our algorithm, we manually inspected validation videos where the head direction and tail position were superimposed on the raw image when a bout is detected, allowing to check both the tracking and bout detection quality.</p> <p>The dataset contains MATLAB files which can be read using the Python code uploaded along with the dataset. The codebase also includes a Cython implementation of the BASS algorithm. The ReadMe for using the Python code to analyze the larval zebrafish dataset and for using BASS is included with the code. </p>
Data from: A 3D-printed capillary tube holder for high-throughput chemotaxis assays
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Data for: Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model
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Zebrafish larvae exploration and aversive chemotaxis dataset
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data_set_deAnna_et_al_2020_chemotaxis_dispersion
<p>Data set associated to the publication "Chemotaxis under flow disorder shapes microbial dispersion in porous media". It includes the following:</p> <p>i) images of bacterial distribution in a microfluidics porous medium replica while displaced by a continuously injected front of a solute triggering microbial chemotaxis towards positive gradients (attractant).</p> <p>ii) images of bacterial distribution in a microfluidics porous medium replica while displaced by a continuously injected front of a solute triggering microbial chemotaxis towards negative gradients (repellent).</p> <p>iii) images of a continuously injected front of fluorescent tracer.</p> <p>iv) images of a chemotaxis assay to characterize positive/negative chemotaxis towards/from a chemoattractant/repellent: no flow conditions, steady and homogeneous chemical gradient.</p>
Roles of the ClC chloride channel CLH-1 in food-associated salt chemotaxis behavior of C. elegans
<p>The ability of animals to process dynamic sensory information facilitates foraging in an ever changing environment. However, molecular and neural mechanisms underlying such ability remain elusive. The ClC anion channels/transporters play a pivotal role in cellular ion homeostasis across all phyla. Here we find a ClC chloride channel is involved in salt concentration chemotaxis of <em>C. elegans</em>. Genetic screening identified two altered-function mutations of <em>clh-1</em> that disrupt experience-dependent salt chemotaxis. Using genetically encoded fluorescent sensors, we demonstrate that CLH-1 contributes to regulation of intracellular anion and calcium dynamics of salt-sensing neuron, ASER. The mutant CLH-1 reduced responsiveness of ASER to salt stimuli in terms of both temporal resolution and intensity, which disrupted navigation strategies for approaching preferred salt concentrations. Furthermore, other ClC genes appeared to act redundantly in salt chemotaxis. These findings provide insights into the regulatory mechanism of neuronal responsivity by ClCs that contribute to modulation of navigation behavior.</p>
Data and Code for "Drift velocity of bacterial chemotaxis in dynamic chemical environments"
<p>Datasets and code required to recreate all results from "Drift velocity of bacterial chemotaxis in dynamic chemical environments"</p>
Roles of the ClC chloride channel CLH-1 in food-associated salt chemotaxis behavior of C. elegans
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Low pH amplifies chemotaxis toward urea in Helicobacter pylori
<p>The attached data is for the plots in the manuscript "<span>Low pH amplifies chemotaxis toward urea in <em>Helicobacter pylori"</em></span></p>
raw data statistics article "Chemotaxis of Tuta absoluta to tomato plants exposed to methyl jasmonate and conspecific injuries"
<p>raw data statistics article "Chemotaxis of Tuta absoluta to tomato plants exposed to methyl jasmonate and conspecific injuries"</p>
ARID1A loss shapes immunosuppression tumor microenvironment via NF-kB induced MDSC chemotaxis to promote prostate Cancer metastasis [RNA-seq II]
GEO Series GSE213251. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic Profile of Microglia Following Inflammation-Sensitized Hypoxic-Ischemic Brain Injury in Neonatal Rats Reveals Strong Contribution to Neutrophil Chemotaxis and Activation
GEO Series GSE294909. Rattus norvegicus. 23 samples. Type: Expression profiling by high throughput sequencing.
TRAIL-induced cytokine production via NFKB2 pathway promotes neutrophil chemotaxis and immune suppression in triple negative breast cancers [MDA-MB-231]
GEO Series GSE271120. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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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
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