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294 results for “infection model”
Supplementary material for journal article "Challenge dose titration in a Mycobacterium bovis infection model in goats"
<p>Supplementary Figure and Table to Journal article. Figure shows daily rectal temperature of each animal after inoculation. Table 1 shows number and volume of pulmonary lesions for each animal as detected by computed tomography imaging. Table 2 gives details about scoring used at clinical examination.</p>
Dataset - A lung-on-chip model reveals an essential role for alveolar epithelial cells in controlling bacterial growth during early M. tuberculosis infection
<p>Description of the sub-folders<br> Name, type of data, corresponding Figure in the manuscript<br> 3D view of the LoC model - .tiff image stack, Figure 1.</p> <p>Bacterial Growth Rate Data - .tiff image stacks, .csv files and MATLAB code to extract the fluorescence intensity over time, Figure 2, Figure 2 - figure supplement 2, Figure 2 - figure supplement 4, Figure 3, Figure 3 - figure supplement 2, Figure 4.</p> <p>AT Characterization - .tiff image stacks and MATLAB code to extract the number and volume of lamellar bodies from the stack of confocal images, Figure 1, Figure 1 - figure supplement 1, Figue 1 - figure supplement 2.</p> <p>AT Infection in LoC model - .tiff image stacks, Figure 2 - figure supplement 1.</p> <p>AT Infection in vivo - .tiff image stacks, Figure 1 - figure supplement 3.</p> <p>Simulations of in vivo infections - .dat files of growth rates in macrophages for the WT and ESX-1 deficient populations and MATLAB code to simulate an infection from this data, Figure 4.</p> <p> </p>
When does antimicrobial resistance increase bacterial fitness? Effects of dosing, social interactions and frequency dependence on the benefits of AmpC β-lactamases in broth, biofilms and a gut infection model.
<p><span>One of the longstanding puzzles of antimicrobial resistance is why the frequency of resistance persists at intermediate levels.<span> </span>Theoretical explanations for the lack of fixation of resistance include cryptic costs of resistance or negative frequency-dependence but are seldom explored experimentally. <span> </span><em>β</em>-lactamases, which detoxify penicillin-related antibiotics, have well-characterized frequency-dependent dynamics driven by cheating and cooperation.<span> </span>However, bacterial physiology determines whether <em>β</em>-lactamases are cooperative and we know little about the sociality or fitness of <em>β</em>-lactamase producers in infections.<span> </span>Moreover, media-based experiments constrain how we measure fitness, and ignore important parameters such as infectivity and transmission among hosts.<span> </span>Here, we investigated the fitness effects of broad-spectrum AmpC <em>β</em>-lactamases in <em>Enterobacter cloacae</em> in broth, biofilms and gut infections in a model insect. <span> </span>We quantified frequency- and dose-dependent fitness using cefotaxime, a third-generation cephalosporin.<span> </span>We predicted that infection dynamics would be similar to those observed in biofilms, with social protection extending over a wide dose range.<span> </span>We found evidence for the sociality of <em>β</em>-lactamases in all contexts with negative frequency-dependent selection ensuring the persistence of wild-type bacteria although cooperation was less prevalent in biofilms, contrary to predictions.<span> </span>While competitive fitness in gut infections and broth had similar dynamics, incorporating infectivity into measurements of fitness in infections<em> </em>significantly affected conclusions. <span> </span>Resistant bacteria had reduced infectivity which limited the fitness benefits of resistance to infections challenged with low antibiotic doses and having low initial frequencies of resistance. <span> </span>The fitness of resistant bacteria in more physiologically tolerant states (in biofilms, in infections) could be constrained by the presence of wild-type bacteria, high antibiotic doses and limited availability of <em>β</em>-lactamases.<span> </span>One conclusion is that increased tolerance of <em>β</em> -lactams does not necessarily increase selection pressure for resistance.<span> </span>Overall, both cryptic fitness costs and frequency-dependence curtailed the fitness benefits of resistance in this study.<span> </span></span></p> <p><span> </span></p>
Results: Modeling the impact of the Omicron infection wave in Germany
<p># Modeling the impact of the Omicron infection wave in Germany</p> <p>This repository contains results of a modelling study regarding the spread of SARS-CoV-2 VOC "Omicron" in Germany.</p> <p>## Columns and values</p> <p>| column name | type | description EN | description DE |<br> | -- | -- | -- | -- |<br> | `infectious_period_both` | int | Mean infectious period (in days) for both variants | Mittlere Infektiositaetsperiode fuer beide Varianten (in Tagen) |<br> | `omicron_latent_period` | int | Mean latent period of VOC Omicron (in days) | Mittlere Latenzzeit der VOC Omikron (in Tagen) |<br> | `booster_reach` | str | Reach of the booster campaign | Reichweite der Auffrischkampagne |<br> | `booster_VE` | str | Vaccine efficacy of the booster vaccination | Impfeffektivitaet der Auffrischimpfung |<br> | `contact_reduction_scenario_id` | int | ID of the contact reduction scenario | ID des Kontaktreduktionsszenarios |<br> | `contact_reduction_strength` | float | prefactor with which the contact modulation f(t) is multiplied | Vorfaktor, mit der die Kontaktmodulation f(t) waehrend der Kontaktreduktionsperiode skaliert wird |<br> | `contact_reduction_start` | date (ISO 8601) | Date when contact reduction begins | Beginn der Kontaktreduktion |<br> | `contact_reduction_end` | date (ISO 8601) | Date when contact reduction ends | Ende der Kontaktreduktion |<br> | `relative_risk_hospitalization` | float | Relative risk (RR) of hospitalization after infection with Omicron as compared to infection with Delta | Relatives Risiko (RR) der Hospitalisierung nach Infektion mit Omicron gegenueber Infektion mit Delta |<br> | `relative_risk_icu` | float | Relative risk (RR) of ICU admission after infection with Omicron as compared to infection with Delta | Relatives Risiko (RR) der Intensivpflichtigkeit nach Infektion mit Omicron gegenueber Infektion mit Delta |<br> | `value_type` | str | Wich value is shown in the `value` column | Art des Wertes in der Spalte `value` |<br> | `date` | date (ISO 8601) | Date associated with the modeling result given in column `value` | Datum assoziiert mit dem Modellergebnis des Wertes in der Spalte `value` |<br> | `value` | int | Model result (rounded to nearest integer) | Modellergebnis (gerundet auf ganze Zahl) |</p> <p>Additional columns regarding combinations of plausible scenarios (rounded to nearest integer) and 180 stochastic simulations per parameter combination:</p> <p>| column name | type | description EN | description DE |<br> | -- | -- | -- | -- |<br> | `95_PI_lower` | int | 95% PI lower bound (rounded to nearest integer) | Untere Schranke des 95% PIs (gerundet auf ganze Zahl) |<br> | `50_PI_lower` | int | 50% PI lower bound (rounded to nearest integer) | Untere Schranke des 50% PIs (gerundet auf ganze Zahl) |<br> | `median` | int | median (rounded to nearest integer) | Median (gerundet auf ganze Zahl) |<br> | `50_PI_upper` | int | 50% PI upper bound (rounded to nearest integer) | Obere Schranke des 50% PIs (gerundet auf ganze Zahl) |<br> | `95_PI_upper` | int | 95% PI upper bound (rounded to nearest integer) | Obere Schranke des 95% PIs (gerundet auf ganze Zahl) |</p> <p>### Values: `booster_reach`</p> <p>| value | description EN | description DE |<br> | -- | -- | -- |<br> | `md` | medium booster campaign reach (80% of those that received full vaccination in 2021 receive booster vaccination) | Medium, 80% derjenigen, die in 2021 vollstaendig geimpft wurden, erhalten eine Auffrischimpfung |<br> | `hi` | high booster campaign reach (100% of those that received full vaccination in 2021 receive booster vaccination) | Hoch, 100% derjenigen, die in 2021 vollstaendig geimpft wurden, erhalten eine Auffrischimpfung |<br> | `hi-and-90perc-2dose` | 100% receive booster vaccination and vaccine uptake of first immunization suddenly increases to 90% in Jan 2022 | 100% erhalten Auffrischimpfung und Impfquote der Erstimmunisierung erreicht schnell 90% im Januar 2022 | </p> <p><br> ### Values: `booster_VE`</p> <p>| value | description EN | description DE |<br> | -- | -- | -- |<br> | `lo` | low booster vaccine efficacy (assumption: booster protects as well against infection as 2nd dose) | Niedrige Impfeffektivitaet (Auffr. schuetzt genauso gut vor Infektion wie 2 Dosen) |<br> | `hi` | high booster vaccine efficacy (assumption: booster protects as well against infection as against symptomatic disease) | Hohe Impfeffektivitaet (Auffr. schuetzt genauso gut vor Infektion wie vor symptomatischer Erkrankung) |</p> <p>### Values: `value_type`</p> <p>| value | description EN | description DE |<br> | -- | -- | -- |<br> | `inc` | incidence (absolute number of new reported cases per day) | Inzidenz (Zahl der gemeldeten Neuinfektion an diesem Tag, absolut) |<br> | `hsp` | hospitalization incidence (absolute number of new hospital admissions per day) | Hospitalisierungsinzidenz (Zahl der gemeldeten Neuhospitalisierungen an diesem Tag, absolut) |<br> | `icu` | total number of patients in ICUs | Gesamtzahl der intensivpflichtigen Patient:innen |</p> <p>## License</p> <p>Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).<br> </p>
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
A model of within-host interactions between host resources, macroparasite infection and immune response
<p>This project was designed to mathematically investigate the of different host parasite-mitigation strategies on host condition. The R code herein comprises:</p> <ul> <li>An ODE model of within-host interactions between a macroparasite (e.g. helminth) infection, host resource levels and host immune response, and the consequent effects on host condition. In brief, resources are ingested and utilised by the host, leading to inceased condition. The host is infected by a parasite, which matures and establishes within the host; both age stages cause harm to the host, decreasing host condition. The presence of the parasite stimulates an immune response, which can either target larval or adult parasites (a resistance strategy), or ameliorate the harm they cause (a tolerance strategy). The host can also reduce resource intake in order to also reduce ingestion of parasite infective stages (an avoidance strategy). Resistance responses have an associated immunopathology, in that the immune response also harms the host.</li> <li>Code to plot model trajectories over time.</li> <li>Code to plot multiple trajectories as a heat map, in which the x-axis is time and the y-axis is a parameter representing the host investment in its parasite-mitigation strategy.</li> <li>Code to calculate the optimum host investment for each strategy, over various sets of parameter values, as determined by maximising mean host condition over a given timeframe, and to plot the output.</li> <li>The same are also provided for an ODE model in which the total immune response is allocated between the two resistance responses and tolerance (a combined strategy). The optimisation code optimises both the total investment in immune repsonse, and how much is allocated to the three different individual strategies.</li> </ul> <p>The model and results are described in detail in the associated manuscript. We also provide here the simulated datasets in which the optimum host investments were calculated over a range of different parameter values, as these take several hours to run on a standard desktop computer..</p>
Figure 2 in A Seinhorst Model Determined the Host-Parasite Relationships of Meloidogyne javanica Infecting Fenugreek cv. UM202
Figure 2: Effect of increasing nematode population densities (from 0.125 on the left to 128 J2s g-1 soil on the right) of M. javanica on the growth of fenugreek cv. UM-202, showing a reduction in plant growth. Symptoms of nematode attack (a marked reduction of plant growth) were evident at the P level of 8 J2s g-1 soil. However, the tolerance limits (T) of fenugreek plant shoot length i were 1.3 J2s g-1 soil.
Figure 3 in A Seinhorst Model Determined the Host-Parasite Relationships of Meloidogyne javanica Infecting Fenugreek cv. UM202
Figure 3: Relationship between initial population densities (Pi) of M. javanica and relative shoot length (A) and relative shoot dry weights (B) of fenugreek cv. UM-202, grown in pots under glasshouse conditions for 90 days. Each point represents the average of four replicated plants. Lines represent the predicted function calculated by fitting the Seinhorst model to data using the SeinFit program. Statistics for fitted models of shoot length and shoot dry weight were R2 = 0.90, sum of squares (SS) = 0.12; and R2 = 0.92, SS = 0.072, respectively.
Figure 1 in A Seinhorst Model Determined the Host-Parasite Relationships of Meloidogyne javanica Infecting Fenugreek cv. UM202
Figure 1: Scanning electron microscopy (SEM) images of the perineal pattern of M. javanica, which show a rounded to flattened dorsal arch and conspicuous lateral lines that separate the dorsal and ventral regions of the patterns. (A) A close view of the distinct lateral line in a perineal pattern distinguishes this species from other Meloidogyne spp. (B) An inner area was marked by coarsely broken striae and contained the vulva and anus.
Data from: Hidden variable models reveal the effects of infection from changes in host survival
<p class="MsoNormal">The impacts of disease on host vital rates can be demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual effects of infectious disease from population-level measurements of survival when longitudinal studies are not possible. Our approach <span>seeks to explain temporal deviations in population-level survival after introducing a disease causative agent when disease prevalence cannot be directly measured by coupling survival and epidemiological models. We tested this approach using an experimental host system (<em>Drosophila melanogaster</em>) with multiple distinct pathogens to validate the ability of the hidden variable model to infer per-capita disease rates. We then applied the approach to a disease outbreak in harbor seals (<em>Phoca vituline</em>) that had data on observed strandings but no epidemiological data. We found that our hidden variable modeling approach could successfully detect the per-capita effects of disease from monitored survival rates in both the experimental and wild populations. Our approach may prove useful for detecting epidemics from public health data in regions where standard surveillance techniques are not available and in the study of epidemics in wildlife populations, where longitudinal studies can be especially difficult to implement.</span></p>
Results: Modeling the impact of the Omicron infection wave in Germany
<pre>Results: Modeling the impact of the Omicron infection wave in Germany This repository contains results of a modeling study regarding the spread of SARS-CoV-2 VOC "Omicron" in Germany. ## Columns and values | column name | type | description EN | description DE | | -- | -- | -- | -- | | `infectious_period_both` | int | Mean infectious period (in days) for both variants | Mittlere Infektiositaetsperiode fuer beide Varianten (in Tagen) | | `omicron_latent_period` | int | Mean latent period of VOC Omicron (in days) | Mittlere Latenzzeit der VOC Omikron (in Tagen) | | `booster_reach` | str | Reach of the booster campaign | Reichweite der Auffrischkampagne | | `booster_VE` | str | Vaccine efficacy of the booster vaccination | Impfeffektivitaet der Auffrischimpfung | | `contact_reduction_scenario_id` | int | ID of the contact reduction scenario | ID des Kontaktreduktionsszenarios | | `contact_reduction_strength` | float | prefactor with which the contact modulation f(t) is multiplied | Vorfaktor, mit der die Kontaktmodulation f(t) waehrend der Kontaktreduktionsperiode skaliert wird | | `contact_reduction_start` | date (ISO 8601) | Date when contact reduction begins | Beginn der Kontaktreduktion | | `contact_reduction_end` | date (ISO 8601) | Date when contact reduction ends | Ende der Kontaktreduktion | | `relative_risk_hospitalization` | float | Relative risk (RR) of hospitalization after infection with Omicron as compared to infection with Delta | Relatives Risiko (RR) der Hospitalisierung nach Infektion mit Omicron gegenueber Infektion mit Delta | | `relative_risk_icu` | float | Relative risk (RR) of ICU admission after infection with Omicron as compared to infection with Delta | Relatives Risiko (RR) der Intensivpflichtigkeit nach Infektion mit Omicron gegenueber Infektion mit Delta | | `value_type` | str | Wich value is shown in the `value` column | Art des Wertes in der Spalte `value` | | `date` | date (ISO 8601) | Date associated with the modeling result given in column `value` | Datum assoziiert mit dem Modellergebnis des Wertes in der Spalte `value` | | `value` | int | Model result (rounded to nearest integer) | Modellergebnis (gerundet auf ganze Zahl) | Additional columns regarding combinations of plausible scenarios (rounded to nearest integer) and 180 stochastic simulations per parameter combination: | column name | type | description EN | description DE | | -- | -- | -- | -- | | `95_PI_lower` | int | 95% PI lower bound (rounded to nearest integer) | Untere Schranke des 95% PIs (gerundet auf ganze Zahl) | | `50_PI_lower` | int | 50% PI lower bound (rounded to nearest integer) | Untere Schranke des 50% PIs (gerundet auf ganze Zahl) | | `median` | int | median (rounded to nearest integer) | Median (gerundet auf ganze Zahl) | | `50_PI_upper` | int | 50% PI upper bound (rounded to nearest integer) | Obere Schranke des 50% PIs (gerundet auf ganze Zahl) | | `95_PI_upper` | int | 95% PI upper bound (rounded to nearest integer) | Obere Schranke des 95% PIs (gerundet auf ganze Zahl) | ### Values: `booster_reach` | value | description EN | description DE | | -- | -- | -- | | `md` | medium booster campaign reach (80% of those that received full vaccination in 2021 receive booster vaccination) | Medium, 80% derjenigen, die in 2021 vollstaendig geimpft wurden, erhalten eine Auffrischimpfung | | `hi` | high booster campaign reach (100% of those that received full vaccination in 2021 receive booster vaccination) | Hoch, 100% derjenigen, die in 2021 vollstaendig geimpft wurden, erhalten eine Auffrischimpfung | | `hi-and-90perc-2dose` | 100% receive booster vaccination and vaccine uptake of first immunization suddenly increases to 90% in Jan 2022 | 100% erhalten Auffrischimpfung und Impfquote der Erstimmunisierung erreicht schnell 90% im Januar 2022 | ### Values: `booster_VE` | value | description EN | description DE | | -- | -- | -- | | `lo` | low booster vaccine efficacy (assumption: booster protects as well against infection as 2nd dose) | Niedrige Impfeffektivitaet (Auffr. schuetzt genauso gut vor Infektion wie 2 Dosen) | | `hi` | high booster vaccine efficacy (assumption: booster protects as well against infection as against symptomatic disease) | Hohe Impfeffektivitaet (Auffr. schuetzt genauso gut vor Infektion wie vor symptomatischer Erkrankung) | ### Values: `value_type` | value | description EN | description DE | | -- | -- | -- | | `inc` | incidence (absolute number of new reported cases per day) | Inzidenz (Zahl der gemeldeten Neuinfektion an diesem Tag, absolut) | | `hsp` | hospitalization incidence (absolute number of new hospital admissions per day) | Hospitalisierungsinzidenz (Zahl der gemeldeten Neuhospitalisierungen an diesem Tag, absolut) | | `icu` | total number of patients in ICUs | Gesamtzahl der intensivpflichtigen Patient:innen | ## License Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).</pre>
A model of within-host interactions between host resources, macroparasite infection and immune response
Open the record for dataset details and reuse information.
Data from: Hidden variable models reveal the effects of infection from changes in host survival
Open the record for dataset details and reuse information.
"A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset
<p>Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats and FORTRAN code.</p>
PINK1 deficiency rewires early immune responses in a mouse model of Parkinson's disease triggered by intestinal infection
<p>Parkinson’s disease is characterized by a period of non-motor symptoms, including gastrointestinal dysfunction, preceding motor deficits by decades. This long prodrome is suggestive of peripheral immunity involvement in the initiation of disease. We previously developed a model system in PINK1 KO mice displaying PD-like motor symptoms at late stages following intestinal infections. Herein, we map the initiating immune events at the site of infection in this model. Using single-cell RNAseq, we demonstrate that peripheral myeloid cells are the earliest highly dysregulated immune cell type in PINK1 KO infected mice followed by an aberrant T cell response shortly after. We elucidate an increased propensity for antigen presentation mediated by myeloid-CD8+ T cell interaction. PINK1 KO activated myeloid cells acquire a proinflammatory profile inducing cytotoxic T cell responses. Together, our study provides the first evidence that PINK1 is a key regulator of immune functions in the gut underlying early PD-related disease mechanisms.</p>
Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
Dataset for the manuscript: Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages.
<p>Dataset for the manuscript:</p> <p>Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages. preprint, bioRxiv, 2022. DOI: 10.1101/2022.05.06.490883</p> <p>Each zip file contains the raw computational data (as a gzip compressed tarball), YAML input files, as well as Python plotting scripts. The Python plotting scripts have dependencies on the packages: <em>tarfile</em>, <em>multiprocessing</em>, <em>numpy</em>, <em>scipy</em>, and <em>matplotlib</em>. Note that the Python plotting scripts plot directly from the gzip compressed tarballs.</p> <p>The corresponding code can be found on GitHub: https://github.com/Ruth-Bowness-Group/CAModel</p>
Comprehensive large-scale datasets for 26 viral families for fine-tuning BERT-infect models
<p>These datasets were constructed in the paper "Hidden Challenges in Evaluating Spillover Risk of Zoonotic Viruses using Machine Learning Models" (doi: https://doi.org/10.1101/2024.04.25.591033). The details were also described in the git-hub (https://github.com/Junna-Kawasaki/BERT-infect_2024).</p> <ul> <li>The compressed files, such as ${virus}.tar.xz, contain fasta and genbank files.</li> </ul>
On modelling airborne infection risk
<div> <div> <div> <p>Airborne infection risk analysis is usually performed for enclosed spaces where susceptible indi- viduals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiolog- ical model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet, and behavioural parameters of the individual. We calculate the time-dependent risk to be infected for two scenarios. We show how the epidemic infection risk de- pends on the viral latent period and the event time, the time infection occurs. Infection risk follows the dynamics of the infected population. As the latency period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher its risk of infection for equal exposure time to the mode of transmission is.</p> </div> </div> </div>
Dataset - Dynamic persistence of intracellular bacterial communities of uropathogenic Escherichia coli in a human bladder-chip model of urinary tract infections
<p>Dataset for manuscript posted at biorxiv: https://doi.org/10.1101/2021.01.03.42483 and in revision for eLife. Data corresponding to each main figure and its associated figure supplements are in seperate .zip folders.</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.