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64 results for “plague”
Figure 3 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 3. Discharge of the Bottwar stream (blue line) since construction of the barriers (daily means measured in the lower course of the stream, obtained from the "Umweltinformationssystem (UIS) der LUBW Landesanstalt für Umwelt Baden-Württemberg"). The horizontal hatched line corresponds to the stream discharge at which the 1st, flow-based barrier (B1) was effective to exclude signal crayfish during the in-situ assessment of barrier efficacy (date highlighted by vertical line). The red ticks beside the time axis indicate days with a stream discharge lower than 0.95 x this threshold, i.e., conditions at which barrier functionality has been presumably compromised by low stream flow. Please note the marked increase in duration and intensity of extreme low-flow conditions since 2018.
Figure 2 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 2. Distribution of native and invasive crayfish species in the study area from 2011 to 2020, as indicated by monitoring data of the Fisheries Research Station (2011–2014) and the intensive crayfish surveys in 2017 and 2020 (this study; Table S2 and Figure S2). The signal crayfish distribution prior to 2013 remains unknown, as indicated by the question mark in the top left panel. Dots represent the exclusion barriers implemented in 2014 (numbered in direction of upstream signal crayfish spread; fill color indicates barrier functionality with pink = flow-based and yellow = waterfall-based). Detail maps in the lower panels show the fine-scale signal crayfish distribution at B1.
Figure 1 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 1. Overview of the study area and the known crayfish distribution in 2014 (A, B), and location and pictures of the three exclusion barriers (modified pipe culverts; panel C; please note the rotated map); fill color indicates barrier functionality with pink = flow-based and yellow = waterfall-based barriers. Blue arrows indicate the direction of stream flow. The red arrow in A denotes the study area.
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Plague transforms positive effects of precipitation on prairie dogs to negative effects
Fig. 2. Relationship between visual count changes in prairie dogs (Cynomys spp.) and annual precipitation (cm) on plots without plague management and with plague management by treating burrows with deltamethrin dust for flea control. Population change (λ) was indexed by visual counts conducted in mid-summer of adults plus juveniles, and annual precipitation was cumulative during the 12-month period prior to the typical date of the second count (interval of 1 July-30 June). Visual counts are presented as treated in the analysis (re-scaled λ, natural log transformed), although the repeated measures analysis retained the pairings of treatments that cannot be illustrated here. Points above the dashed line indicate population increases; points below the dashed line indicate population declines with points on the zero-line indicating population collapse to 0 animals.
Fig. 1 in Plague transforms positive effects of precipitation on prairie dogs to negative effects
Fig. 1. Study sites in the western United States where the influence of precipitation on prairie dog population change was evaluated on paired plots with and without deltamethrin treatment to control the flea vectors of plague. Prairie dog sketch by D. Crawford.
Fig. 6 in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 6. Dendrogram of flea species showing two main groups (AB) of flea community based on farm and forest habitats.
Fig. 5 in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 5. Plot showing the predicted effect of locality and season on the probability of flea infestation, based on the final best-fitting generalized linear model with a binomial function. The analysis aimed to identify the factors that strongly influence flea infestation. The strongest predictors of flea infestation were plague persistent localities and short rain seasons. The probability of infestation on these predictors was found to be statistically significant (p <0.05), suggesting a higher likelihood of flea infestation in this locality and season. The bars are 95% confidence interval of the effects.
Fig. 4 in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 4. Plots showing predicted effect of rodent traits on flea abundance, based on final best fitting generalized linear mixed model with a negative-binomial function. The plots (a) indicates that rodent weight increased with flea abundance. The gray shade in the plots represents the strength and direction of the correlation, with the width of the shade indicating the 95% confidence interval (CI) around the estimated effect. Furthermore, plot (b) indicates that male rodents are more likely to have higher flea abundance compared to female rodents, but this association was not statistically significant. The bars are 95% confidence intervals of the effects.
Fig. 3 in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 3. Flea abundance for different flea species across habitat types in each locality and rodent species.
Fig. 2 in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 2. Flea abundance in the (a) localities, (b) habitats and (c) seasons. Error bars represent the standard error. There were no statistically significant differences that were observed.
Fig. 1. A in Flea infestation of rodent and their community structure in frequent and non-frequent plague outbreak areas in Mbulu district, northern Tanzania
Fig. 1. A map of Mbulu district indicating the two study localities, Endeshi-Arri (Persistent locality and Mongahay (non-persistent locality), along with the two study habitats (Farmland and forests) in each locality.
Plague Doctor
Used for La Pest Tower Defence Game https://hellspawngames.itch.io/la-pest-tower-defence Source: Objaverse 1.0 / Sketchfab
Source code and datasets used to link new waves of plague outbreaks in medieval Europe to climate fluctuations affecting the reservoirs of the disease in Asia.
<p>The zipfile contains the project directory which includes the source code and datasets used in the paper on <strong>Climate-driven introduction of the Black Death and successive plague reintroductions into Europe</strong>, as published in <em>Proceedings of the National Academy of Sciences</em> (PNAS). Access the paper at http://www.doi.org/pnas.1412887112</p> <p>If you are not familiar with Clojure, Leiningen, and its project directory format, see http://clojure.org/getting_started for one of the IDE's to run the clojure code in, and use http://leiningen.org/ as the project / dependency manager.</p>
3D Plague Doctor Mask (Sketchfab Changed it)
For some reason my Plague doctor mask was replaced with my model for SCP 049 with no animation. I have no idea why this happened and I no longer have the mask fbx, so please injoy this still image of 049 Source: Objaverse 1.0 / Sketchfab
Datasets and code for "Mapping the plague through natural language processing"
<p>This project investigates the performance of various NLP libraries and geocoding services for the semi-automated generation of quantitative datasets from narrative texts. We provide the original files, several intermediate data products as well as the final plague datasets. Please note that some of the steps in this process were done manually, thus some of the scripts cannot be run completely. </p> <p>This work is based on two plague treatises:</p> <p>- Sticker, G. 1908 <em>Abhandlungen aus der Seuchengeschichte und Seuchenlehre. Band 1: Die Pest</em>. Giessen, A. Töpelmann.</p> <p>- Biraben, J.-N. 1975 <em>Les hommes et la peste en France et dans les pays européens et méditerranéens</em>. Paris, Mouton.</p> <p>The final geocoded, plague datasets are:</p> <p><strong>- plague_sticker_v1.csv</strong></p> <p><strong>- plague_biraben_v1.csv</strong></p> <p>A data dictionary is available as</p> <p><strong>plague_datadict.xlsx</strong></p> <p>Other files:</p> <table> <tbody> <tr> <td>file name</td> <td>content</td> </tr> <tr> <td>sticker_OCR_orig.txt</td> <td>Original OCR text</td> </tr> <tr> <td>sticker_OCR.txt</td> <td>Original OCR text without parenthesis (author names)</td> </tr> <tr> <td>sticker_textprep.rds</td> <td>Original OCR text with further preparations</td> </tr> <tr> <td>sticker_goldstandard_annotated_1.tsv</td> <td>manual annotations file 1</td> </tr> <tr> <td>sticker_goldstandard_annotated_2.tsv</td> <td>manual annotations file 2</td> </tr> <tr> <td>sticker_goldstandard_annotated_consensus.tsv</td> <td>consenus annotation file</td> </tr> <tr> <td>sticker_standard_toponyms.csv</td> <td>Gold standard for toponym recognition. Contains the tokenization, the start/end character respective to the OCR text (orig and without parenthesis) and whether a token is a location or other</td> </tr> <tr> <td>sticker_comparison_NER.rds</td> <td>Comparison of NER performance</td> </tr> <tr> <td>sticker_comparison_geocoding.rds</td> <td>Comparison of Geocoding performance</td> </tr> </tbody> </table> <p> </p>
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