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Supplementary material 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

SourceAttributionModel.fskx

Figure 6 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 6 Bar plot of number of human cases of Salmonella infection attributed to different sources. Subfigure A shows the result for the baseline data 2004–2007 (the so-called defaultSimulation in Table 2) and Subfigure B the results for themonitoring data 2010/2011 (the so-called SimulationTable3 in Table 2).

Figure 4 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 4 Starting points for the Markov chains of Parameter scenario 3 and their effect on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0.18) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points the Markov chains do not converge within 30,000 iterations for the parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} or \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_3\), \(q_2\) and \(q_3 \end{equation*} \end{varwidth} \end{document} , where types 2 and 3 correspond to S. enterica serotype Enteritidis PT 14b and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour). The error bars of the predicted source attribution are large (see the bar plot).

Figure 2 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 2 Starting points for the Markov chains of Parameter scenario 1, their effects on the convergence behaviour and the model predictions. The starting points are evenly spaced in the lower fifth of the space of possible starting points (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge quickly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) \end{equation*} \end{varwidth} \end{document} and \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).

Figure 3 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 3 Starting points for the Markov chains of Parameter scenario 2 and their effects on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge slowly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) and \(q_3\) \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).

Figure 5 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 5 Model-data fit when setting \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M_j \end{equation*} \end{varwidth} \end{document} =1 and using different parameterizations. Each point in the figure corresponds to one bacterial subtype. Subfigure A shows a consistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_j \sim uniform(0,30000) \end{equation*} \end{varwidth} \end{document} . I.e. the logarithm of the number of cases as found in the data corresponds well to the logarithm of predicted number of cases. Subfigure B shows an inconsistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a\sim uniform(0,20) \end{equation*} \end{varwidth} \end{document} . Here, the model systematically underestimates the number of cases for the subtypes as the points gather well below the identity line.

Figure 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008

Figure 1 The posterior distributions for the fifth entry in the list of Salmonella subtypes (q5), which is S. enterica serotype Enteritidis PT 21, as a function of the possible values of q5. The shown posterior distributions are calculated by the Gibbs-Sampler software OpenBUGS using the Bayes DB model presented in Jabin et al. (2019) for the monitoring data and a sample size of 1e5. Subfigure A) shows the posterior distribution calculated for \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,0.2) \end{equation*} \end{varwidth} \end{document} . The chosen limits of the uniform distribution lead to a cut off in the posterior distribution. When enlarging the interval defining the prior distribution to \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,1) \end{equation*} \end{varwidth} \end{document} , then a complete posterior distribution is produced by OpenBUGS (see Subfigure B)).

CAT, TD, ADCP data for CAT research paper to be published in Journal of Water Resource Research

<p>This file contain experiment data obtained in Huangcai reservoir, Hunan, China in September, 2020. The field experiment aimed to reconstruct multi-layer current field using three-dimensional inversion technique to visualize spatiotemporal current structure.</p>

opencc-zeroNov 2021View details →

Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171

ChickenEgg-SCNM

Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171

Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.

Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171

Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .

Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171

Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".

Figure 3 from: Stauch KLN, Wincheski RJ, Albers J, Black TE, Reichert MS, Abramson CI (2021) Limited evidence for learning in a shuttle box paradigm in crickets (Acheta domesticus). Journal of Orthoptera Research 30(2): 155-161. https://doi.org/10.3897/jor.30.65172

Figure 3 Proportion of mean time spent on correct side of the apparatus for male crickets during each 60 s interval. Male N values: Baseline = 10, Yoked = 21, Master = 21. The bars on the data points represent the standard errors. The solid line at 0.5 indicates chance.

Figure 2 from: Stauch KLN, Wincheski RJ, Albers J, Black TE, Reichert MS, Abramson CI (2021) Limited evidence for learning in a shuttle box paradigm in crickets (Acheta domesticus). Journal of Orthoptera Research 30(2): 155-161. https://doi.org/10.3897/jor.30.65172

Figure 2 Proportion of mean time spent on correct side of the apparatus for female crickets during each 60 s interval. Female N values: Baseline = 18, Yoked = 30, and Master = 30. The bars on the data points represent the standard errors. The solid line at 0.5 indicates chance.

Figure 1 from: Stauch KLN, Wincheski RJ, Albers J, Black TE, Reichert MS, Abramson CI (2021) Limited evidence for learning in a shuttle box paradigm in crickets (Acheta domesticus). Journal of Orthoptera Research 30(2): 155-161. https://doi.org/10.3897/jor.30.65172

Figure 1 The shuttle box apparatus with propeller controller in the upper left-hand corner. In this image, one of the plexiglass covers (A) has been removed so that the shock grid can be seen more easily. In the experiments, one cricket was placed into the compartment labeled A and one cricket was placed into the compartment labeled B. A piece of filter paper was placed under the left-hand side of the shuttle box to illustrate the midpoint for the two halves of the compartment. Once the cricket was inside the compartment, the plexiglass was placed over the compartment to ensure that the cricket maintained contact with the grid.

Figure 3 from: Hershberger WL (2021) Substrate-borne vibrations used during acoustic communication and the existence of courtship songs in some species of the genus Anaxipha (Saussure) (Orthoptera: Trigonidiidae: Trigonidiinae). Journal of Orthoptera Research 30(2): 185-191. https://doi.org/10.3897/jor.30.70990

Figure 3 Mean number of drumming bouts performed during different portions of calling songs in A. tinnulacita showing standard error of the means. The occurrence of drumming was variable from one calling song to the next. Consistently, more drumming bouts were given during the first 40 percent and the last 20 percent of the major echemes of calling song (n = 14 songs, 1218 drumming bouts total).

Figure 3 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605

Figure 3 Performance experiments. Survival and specific growth rates of grasshoppers from the long-term lab colony no-choice diet experiments. A. The specific growth rates for each diet treatment. Diamonds indicate the mean and bolded lines indicate the median. Boxes are +/- 25%, lines represent minimum and maximum values excluding extreme values, and dots indicate data points > 1.5 farther from the box edge than the interquartile range. Lower case letters indicate differences from Mann-Whitney post-hoc analyses. B. The proportion of grasshoppers surviving through time on each diet treatment. Most diet treatments did not have individuals die until the 5th day of the experiment, and most treatments except 7p:35c had minimal deaths (although there were no significant differences among treatments). C. Proportion of grasshoppers molting to adults over time. Most of the diets saw increases in molting from days 5–7, except diet treatment 7p:35c, which was delayed and had the least number of grasshoppers successfully molt (significantly different from all other treatments).

Figure 2 from: Hershberger WL (2021) Substrate-borne vibrations used during acoustic communication and the existence of courtship songs in some species of the genus Anaxipha (Saussure) (Orthoptera: Trigonidiidae: Trigonidiinae). Journal of Orthoptera Research 30(2): 185-191. https://doi.org/10.3897/jor.30.70990

Figure 2 Percent usage of different length drumming bouts during calling songs in A. tinnulacita. (n = 24 songs; 1,503 tapping bouts; error bars are ±SD of the means).

Figure 6 from: Hershberger WL (2021) Substrate-borne vibrations used during acoustic communication and the existence of courtship songs in some species of the genus Anaxipha (Saussure) (Orthoptera: Trigonidiidae: Trigonidiinae). Journal of Orthoptera Research 30(2): 185-191. https://doi.org/10.3897/jor.30.70990

Figure 6 Audio spectrograms of courtship songs of four species of Anaxipha showing drumming bouts (below 2 kHz) and tegminal sounds (above 4 kHz). Low frequencies (below 2 kHz) were enhanced to better show the low-frequency drumming bouts. A.Anaxipha exigua, dark areas below 1 kHz are drumming bouts; B.Anaxipha tinnulacita, fine, dark lines after 1:05 (min:sec) are the drumming bouts; C.Anaxipha tinnulenta, the dark areas after 5 sec and below 2 kHz are the drumming bouts; D.Anaxipha thomasi, the compact dark areas below 2 kHz are the drumming bouts. The clustered, individual taps between drumming bouts are sounds made by the insect jumping repeatedly and rapidly between echemes during the courtship display. Note that the time scales for each courtship song are different, as each species courtship song length is different and variable.

Figure 2 from: Kasalo N, Deranja M, Adžić K, Sindaco R, Skejo J (2021) Discovering insect species based on photographs only: The case of a nameless species of the genus Scaria (Orthoptera: Tetrigidae). Journal of Orthoptera Research 30(2): 173-184. https://doi.org/10.3897/jor.30.65885

Figure 2 Example of AI enhancement of Scaria sp. body details compared to original photography in the background. A. Upper part of the head, showing an eye, a scapus, and a pedicel; B. Ovipositor; C. Hind tarsus; D. Ventral and tegminal sinuses; E. Bottom part of the head, showing the mouthparts. Photo credit: Roberto Sindaco.