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54 results for “information flow”
Supplementary data to accompany Information flow, cell types and stereotypy in a full olfactory connectome
<p>Supplemental file 1</p> <p>Layers assigned by the probabilistic graph traversal model. bodyId refers to neurons’ unique ID in ne- uPrint. layer mean contains the mean layer after 10,000 iterations of the main model (Figure 2). layer - olf mean and layer th mean contain the mean layers from running the traversal model with ORNs and THN/HRNs, respectively (Figure S2).</p> <p>S1 hemibrain neuron layers.csv</p> <p>Supplemental file 2</p> <p>Sensory meta-information related to each glomerulus. Columns: glomerulus (canonical name for one of the 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli), laterality (whether the glomerulus receives bilateral or only unilateral innervation from ALRNs), expected cit (a citation that describes the expected number of RNs in this glomerulus), expected RN female 1h (number of expected RNs in one hemi- sphere), expected RN female SD (standard deviation in the expected number of RNs), missing (qualitative assessment of glomeruli truncation), RN frag (if the RNs in that glomerulus are fragmented), receptor (the OR or IR expressed by cognate ALRNs (Bates et al., 2020; Task et al., 2020)), odour scenes (the general ‘odour scene(s)’ which this glomerulus may help signal (Mansourian and Stensmyr, 2015; Bates et al., 2020)), key ligand(the ligand that excites the cognate ALLRN or receptor the most, based on pooled data from multiple studies (Mu ̈nch and Galizia, 2016)), valence (the presumed valence of this odour chan- nel (Badel et al., 2016)). Exists as hemibrain glomeruli summary in our R package hemibrainr.</p> <p>S2 hemibrain olfactory information.csv</p> <p>Supplemental file 3</p> <p>File listing all identified antennal lobe receptor neurons (ALRNs) in the hemibrain, including information shown in neuPrint. See above for column explanations. Exists as rn.info in our R package hemibrainr.</p> <p>S3 hemibrain ALRN meta.csv</p> <p>Supplemental file 4</p> <p>All the hemibrain neurons we have classed as antennal lobe local neurons (ALLNs). See above for column explanations. Exists as alln.info in our R package hemibrainr.</p> <p>S4 hemibrain ALLN meta.csv</p> <p>Supplemental file 5</p> <p>All the hemibrain neurons we have classed as antennal lobe projection neurons (ALPNs). See above for column explanations. In addition, across dataset cluster refers to the clustering with left and right FAFB PNs; is canonical indicates whether that ALPN is one of the well studied “canonical” uPNs. Exists as pn.info in our R package hemibrainr.</p> <p>40</p> <p>S5 hemibrain ALPN meta.csv</p> <p>Supplemental file 6</p> <p>All the hemibrain neurons we have classed as third-order olfactory neurons (TOONs) including lateral horn neurons (LHNs), as well as wedge projection neurons (WEDPNs), lateral horn centrifugal neurons (LHCENT) and other projection neuron classes (Figure 1). See above for column explanations. Exists as ton.info in our R package hemibrainr.</p> <p>S6 hemibrain TOON meta.csv</p> <p>Supplemental file 7</p> <p>All the hemibrain neurons we have classed as neurons that descend to the ventral nervous system (DNs). See above for column explanations. Exists as dn.info in our R package hemibrainr.</p> <p>S8 hemibrain DN meta.csv</p> <p>Supplemental file 8</p> <p>The root point in hemibrain voxel space, for each hemibrain neuron. This is either the location of the soma, or the tip of a severed cell body fibre tract, where possible. Exists as hemibrain somas in our R package hemibrainr.</p> <p>S8 hemibrain root points.csv</p> <p>Supplemental file 9</p> <p>The start points for different neuron compartments. Nodes downstream of this position in the 3D structure of the neuron indicated with bodyid, belong to the compartment type designated by Label. A product of running flow centrality on hemibrain neurons, exists as hemibrain splitpoints in our R package hemi- brainr.</p> <p>S9 hemibrain compartment startpoints.csv</p> <p>Supplemental file 10</p> <p>3D triangle mesh for the hemibrain surface as a .obj file. This mesh was generated by first merging individual ROI meshes from neuPrint and then filling the gaps in between in a semi-manual process. It also exists as hemibrain.surf in our R package hemibrainr.</p> <p>S10 hemibrain raw.obj</p> <p>Supplemental file 11</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALRN presynapses.</p> <p>41</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>S11 hemibrain AL glomeruli meshes RN-based.zip</p> <p>Supplemental file 12</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALPN presynapses.</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>These meshes are also available as hemibrain al.surf in our R package hemibrainr. S12 hemibrain AL glomeruli meshes PN-based.zip</p>
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
CCG Programme overview: information flows
<p>Schematic representation of the CCG Programme and related information flows across Output Areas (OA) and Work Streams (WS), with expected outcomes and interactions with external partner countries and international partner organisations. </p>
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Information flows around agricultural best management practices in central Pennsylvania
<p>This dataset was collected between February and April 2019, to assess the information network of agricultural Best-Management Practices (BMPs) in central Pennsylvania, a sub-region of the Chesapeake Bay watershed.</p> <p>It contains information flows (or "messages") relating to 16 specific BMPs, including:</p> <ul> <li>the BMP it relates to (e.g. riparian buffers, manure management planning, no-till, cover-cropping, etc.);</li> <li>the source and target of the information (actors);</li> <li>the kind of message (e.g. funding, regulation, technical assistance, etc.);</li> <li>the weight (strength) of messages (only for those received by farmers directly).</li> </ul> <p>Over 3900 messages/information flows were recorded, involving 57 actors.</p> <p>This data was used to conduct the study "Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania".</p>
Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Complex Lava Tube Networks Developed Within the 1792-93 Lava Flow Field on Mount Etna (Italy): Insights for hazard assessment Supporting Informations: Maps and sections of the lava tubes
<div> <div> <div> <p>This supporting information for the above paper submitted to Frontiers in Earth Science - Volcanology, comprises Table 1, as well as the maps and sections of the 8 lava tubes analyzed in this paper, that are located within the 1792-93 lava flow field at Etna volcano. The methods used for the new surveys of the lava tubes are also explained.</p> </div> </div> </div>
Dataset for "Di-synaptic specificity of serial information flow for conditioned fear"
<p>Dataset for the publication "Di-synaptic specificity of serial information flow for conditioned fear"</p>
iWanDroid: Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps
<p>The repo contains the tool iWanDroid: Artifacts for the paper Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps, accepted at ISSRE 2023.</p> <p>Android hybrid apps augment native apps with web and inter-language communication capabilities. These apps facilitate the integration of web components, including JavaScript, into native apps. Besides, they allow a two-way communication where JavaScript can utilize functionality shared by the native side (Java). However, due to operational differences between Java and JavaScript, the semantics of this communication are complex. Tracking information flows via this communication channel, i.e., between these heterogeneous platforms, becomes intricate.</p> <p>Multiple approaches have been proposed to analyze hybrid apps. However, most of them focus on specific classes of web-induced vulnerabilities or provide rudimentary tracking of specific information flows via this communication channel. This work proposes a demand-driven analysis to comprehensively track information flow violations from the native side to JavaScript and vice-versa. To this end, our framework selectively creates data flow summaries of the shared native-side code based on its usage in the corresponding JavaScript code. We demonstrate the efficacy of our approach by applying it to various benchmarks and large-scale apps.</p> <p> </p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Supplementary information for: The artefactual branch effect and phylogenetic conflict: Species delimitation with gene flow in mangrove pit vipers (Trimeresurus purpureomaculatus-erythrurus complex)
<p>Mangrove pit vipers of the <em>Trimeresurus</em> <em>purpureomaculatus</em>-<em>erythrurus</em> complex are the only species of viper known to naturally inhabit mangroves. Despite serving integral ecological functions in mangrove ecosystems, the evolutionary history, distribution, and species boundaries of mangrove pit vipers remain poorly understood, partly due to overlapping distributions, confusing phenotypic variations, and the lack of focused studies. Here, we present the first genomic study on mangrove pit vipers and introduce a robust hypothesis-driven species delimitation framework that considers gene flow and phylogenetic uncertainty in conjunction with a novel application of a new class of speciation-based delimitation model implemented through the program Delineate. Our results showed that gene flow produced phylogenetic conflict in our focal species and substantiated the artefactual branch effect where highly admixed populations appear as divergent nonmonophyletic lineages arranged in a stepwise manner at the basal position of clades. Despite the confounding effects of gene flow, we were able to obtain unequivocal support for the recognition of a new species based on the intersection and congruence of multiple lines of evidence. This study demonstrates that an integrative hypothesis-driven approach predicated on the consideration of multiple plausible evolutionary histories, population structure/ differentiation, gene flow, and the implementation of a speciation-based delimitation model can effectively delimit species in the presence of gene flow and phylogenetic conflict.</p>
Understanding Cultural Information Flow: A Qualitative study of a small social group
<p>This study uses qualitative data to explore closely adoption and onward transmission. Interview data were collected in relation to housework regarding the perceived behaviour of participants’ parents, of themselves, and of their (grand)children (N=14). Observational data was also collected from the members of one of the families for triangulation purposes. Following the six-phase process of thematic analysis, we extracted three main themes: individual learning, horizontal transmission, and vertical transmission. While only few instances were categorised as ‘individual learning’, the instances of horizontal and vertical transmission were numerous, and included the transmission of dichotomous traits. Thus, we explored why some cultural variants outcompete others and how certain learning and teaching mechanisms lead to increased stability and longevity. Vertical congruence was observed during the transmission of a dichotomous cultural trait: transmitters who acquired two variants of the same trait (one horizontally and one vertically) chose to vertically transmit onward to learners – i.e., their (grand)children – the variant which they had previously acquired vertically (i.e., from their own parents or grandparents), even if they exhibited both variants themselves. Horizontal congruence was observed even in sibling-to-sibling transmission, as the transmitter (older sibling) transmitted onward to the learner (younger sibling) the cultural variants which he/she had previously acquired from a peer (even if these were in competition with vertically acquired alternatives). Finally, we discuss the effects of the mother/wife-transmitter, who can weaken the effects of the vertical-congruence effect in the case of her spouse and enhance them in the case of their children.</p>
FlyWire Information Flow Ranks
<p>This repository contains the results of the information flow ranl caluclations used in the analysis around Figure 6 in the manuscript "Neuronal wiring diagram of an adult brain".</p> <p>We applied the information flow model from Schlegel et al. [1] starting from different sets of neurons as indicated by the filename. For each seed set, we ran the model 10,000 times and took the average for each neuron (layer_mean in the feather files). </p> <p>The feather files can be read using pandas (Python): df = pd.read_feather(<path>)</p> <p><strong><a href="https://elifesciences.org/articles/66018" target="_blank" rel="noopener">[1] Schlegel et al., Information flow, cell types and stereotypy in a full olfactory connectome. Elife (2021).</a></strong></p>
Collective flow of circadian clock information in honeybee colonies (Data)
<p>This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".</p> <p><strong> </strong></p> <p>Paper: Collective flow of circadian clock information in honeybee colonies</p> <p><strong> </strong></p> <p>Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.</p> <p><strong> </strong></p> <h2>Description of included files</h2> <p>All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.</p> <p><strong> </strong></p> <h3><strong>mean_velocity_2016.csv and mean_velocity_2019.csv</strong></h3> <p>The mean velocity for each bee and age is averaged over 10-minute time windows.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Mean euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> </ul> <p> </p> <h3><strong>velocity_2088_2019.csv and velocity_5101_2019.csv</strong></h3> <p>The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> </ul> <p> </p> <h3><strong>cosinor_2016.csv and cosinor_2019.csv</strong></h3> <p>A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_reject: P-value of a F-test for model validity.</p> </li> <li> <p>r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality.</p> </li> <li> <p>p_hom: P-Value of F-test of variance homogeneity of cosinor fit.</p> </li> <li> <p>dw: Durbin-Watson statistic for the independence of the residuals.</p> </li> <li> <p>p_runs: P-value of runs test of independence of residuals.</p> </li> <li> <p>RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model</p> </li> <li> <p>SSPE: Pure error sum of squares of cosinor fit.</p> </li> <li> <p>bee_id: Numeric unique identifier per individual bee.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> <li> <p>date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12.</p> </li> <li> <p>n_data_points: Number of data points per cosinor fit.</p> </li> <li> <p>data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock.</p> </li> <li> <p>night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock.</p> </li> <li> <p>ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary.</p> </li> <li> <p>fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit.</p> </li> <li> <p>ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value.</p> </li> <li> <p>ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value.</p> </li> <li> <p>ci_mesor_lower: Confidence interval lower bound for the mesor fit value.</p> </li> <li> <p>ci_mesor_upper: Confidence interval upper bound for the mesor fit value.</p> </li> <li> <p>ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value.</p> </li> <li> <p>ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value.</p> </li> </ul> <p><strong> </strong></p> <h3><strong>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</strong></h3> <p>The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0: Numeric unique identifier per individual bee.</p> </li> <li> <p>bee_id1: Numeric unique identifier per individual bee.</p> </li> <li> <p>interaction_start: Timestamp indicating interaction start time point.</p> </li> <li> <p>interaction_end: Timestamp indicating interaction end time point.</p> </li> <li> <p>x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee0: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee1: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee0: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee1: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee1: Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> </ul> <p>Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>:</p> <ul> <li> <p>x_trans_focal_bee0: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee0: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee0: Translated and rotated theta relative to hive.</p> </li> <li> <p>x_trans_focal_bee1: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee1: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee1: Translated and rotated theta relative to hive.</p> </li> <li> <p>overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p>amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee0: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee1: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h3><strong>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</strong></h3> <p>A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates.</p> <p>As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0</p> </li> <li> <p>bee_id1</p> </li> <li> <p>interaction_start</p> </li> <li> <p>interaction_end</p> </li> <li> <p>x_pos_start_bee0</p> </li> <li> <p>y_pos_start_bee0</p> </li> <li> <p>theta_start_bee0</p> </li> <li> <p>x_pos_start_bee1</p> </li> <li> <p>y_pos_start_bee1</p> </li> <li> <p>theta_start_bee1</p> </li> <li> <p>x_pos_end_bee0</p> </li> <li> <p>y_pos_end_bee0</p> </li> <li> <p>theta_end_bee0</p> </li> <li> <p>x_pos_end_bee1</p> </li> <li> <p>y_pos_end_bee1</p> </li> <li> <p>theta_end_bee1</p> </li> <li> <p>vel_change_bee0</p> </li> <li> <p>rel_change_bee0</p> </li> <li> <p>vel_change_bee1</p> </li> <li> <p>rel_change_bee1</p> </li> <li> <p>age_bee0</p> </li> <li> <p>phase_bee0</p> </li> <li> <p>amplitude_bee0</p> </li> <li> <p>r_squared_bee0</p> </li> <li> <p>p_value_bee0</p> </li> <li> <p>age_bee1</p> </li> <li> <p>amplitude_bee1</p> </li> <li> <p>r_squared_bee1</p> </li> <li> <p>p_value_bee1</p> </li> <li> <p>phase_bee1</p> </li> </ul> <p> </p> <h3><strong>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</strong></h3> <p>Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>bee_id: Numeric unique identifier per individual bee which is a node in the tree.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place.</p> </li> <li> <p>x_pos: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node.</p> </li> <li> <p>age: Age in days of node bee. Can be NaN if the bee has no associated death date.</p> </li> <li> <p>is_root: Bool indicating if node is root of tree.</p> </li> <li> <p>depth: Depth of node in tree. E.g. depth of root is 0.</p> </li> <li> <p>is_leaf: Bool indicating if node is leaf of tree.</p> </li> <li> <p>n_children: Number of children of the subtree of the node.</p> </li> <li> <p>parent: Numeric bee_id of parent node.</p> </li> <li> <p>tree_id: Numeric unique identifier of tree.</p> </li> <li> <p>time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction.</p> </li> <li> <p>path_id: Numeric unique identifier of path.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p> r_squared: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p> phase: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h2>Software used to acquire and analyze the data:</h2> <p><a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a></p> <p><a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a></p> <p> </p>
Growing Malaria Parasites at a Critical Shaking Speed Mimicking Physiological Flow Reveals New Phenotypes for Invasion Ligands, Supporting Information
<p>This repository contains the dataset and analysis scripts associated with the upcoming publication titled <em>Growing malaria parasites at a critical shaking speed that mimics physiological flow conditions reveals new phenotypes for EBA and RH invasion ligands.</em> The repository includes a comprehensive collection of data and scripts used to generate all the plots, along with videos showing how the red blood cells behave in culture media for different shaking speeds in the different shaking vessels. The growth assay data used for this experiment was collected in four batches, labelled GA1 to 4:</p> <ul> <li><strong>GA1</strong>: compares the different knockout lines. </li> <li><strong>GA2</strong>: compares different hematocrits. </li> <li><strong>GA3</strong>: compares different growth vessels.</li> <li><strong>GA4</strong>: contains more repeats of lines from GA1.</li> </ul> <p>This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights. Movies S1 to S4 show a top and side view of each type of plate/plask used on an orbital shaker as the shaking speed is increased.</p>
Supporting information for "Kinetics of CN(v=1) reactions with butadiene isomers at low temperature by cw-Cavity Ringdown in a pulsed Laval flow with theoretical modelling of rates and entrance channel branching
<p>This file contains the master equation inputs for all the reactions studied, as well as all the details on stationary points and VRC-TST fluxes necessary to reproduce the simulations. </p>
Supplementary information for: The artefactual branch effect and phylogenetic conflict: Species delimitation with gene flow in mangrove pit vipers (Trimeresurus purpureomaculatus-erythrurus complex)
Open the record for dataset details and reuse information.
Data from: The utility of information flow in formulating discharge forecast models: a case study from an arid snow-dominated catchment
Open the record for dataset details and reuse information.
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