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Fig. 3 in Additions to the genus Neostasina Rheims & Alayón (Araneae: Sparassidae: Sparianthinae)

Fig. 3. Neostasina bani sp. nov., A−B: holotype, ♂ (MNHNPEJM), C−D: paratype, ♀ (MNHNPEJM). A. Habitus, dorsal view. B. Habitus, ventral view. C. Habitus, dorsal view. D. Habitus, ventral view. Scale bars = 2 mm.

Fig. 6 in Additions to the genus Neostasina Rheims & Alayón (Araneae: Sparassidae: Sparianthinae)

Fig. 6. Neostasina demaco sp. nov., holotype, ♀ (MNHNPEJM). A. Habitus, dorsal view. B. Habitus, ventral view. C. Epigyne, ventral view. D. Vulva, dorsal view. Scale bars: A−B = 2 mm; C−D = 0.5 mm.

Fig. 1 in Additions to the genus Neostasina Rheims & Alayón (Araneae: Sparassidae: Sparianthinae)

Fig. 1. Neostasina aceitillar sp. nov., A−B: paratype, ♀ (MNHNPEJM), C−D: holotype, ♀ (MNHNPEJM). A. Habitus, dorsal view. B. Habitus, ventral view. C. Epigyne, ventral view. D. Vulva, dorsal view. Scale bars: A−B = 2 mm; C−D = 0.5 mm.

Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing

This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".

opencc-by-4.0Apr 2022View details →

Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication   Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-useJulia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau11 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany 2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada 3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, GermanyDOI: https://doi.org/10.1007/s10533-022-00943-7 This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.The repository includes the following files:<ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul> <ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>

Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options

Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options Based on the <a href="https://zenodo.org/record/5774988#.YqwqYDJByUk">pre-built Sector-coupled Euro-Calliope model</a> developed by Bryn Pickering This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following: <ol> <li>Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> )</li> <li>Run the model including only those scenarios that relate to the power sector and SPORES</li> </ol>   Main and parallel batches of SPORES To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (<code>spores_model_run.py</code>) or any of the "parallel batches" of SPORES (e.g., <code>excl_bio</code> and <code>max_bio</code>, which generate SPORES while minimising and, respectively, maximising bioenergy deployment).   Strength of the anchoring to extremes of the decision space To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the <code>euro_calliope/spores.yaml</code> override file. More precisely, you need to change the <code>excl_score</code> parameter in the objective function at the end of the file: <pre><code class="language-bash">max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1}</code></pre> A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space. Weight-assignment method Finally, to change the weight-assignment method, you need to modify the <code>euro_calliope/eurospores/model.yaml</code> file. More precisely, the <code>scoring_method</code> parameter, which can be one of the following: <code>integer</code>, <code>relative_deployment</code>, <code>random</code> or <code>evolving_average</code>. <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre>   Hard-coded changes to be aware of The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (<code>euro_calliope/eurospores/electricity-demand.csv</code>). In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions. If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as <code>__electricity-demand.csv</code>. Summary of results from the paper The folder <code>paper_summary_results</code> features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.

Additional Material for Debiasing architectural decision-making: a workshop-based training approach

This additional material was created to enable full replication of the experiment described in the paper: "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: Debiasing architectural decision-making: a workshop-based training approach". The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture). The folder contains the following files: Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment. Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it. Debiasing-workshop-slides.pdf - slides used during the debiasing workshop. Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop. Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.

opencc-zeroJun 2022View details →

dryad40/100

Additive genetic variation, but not temperature, influences warning signal expression in Amata nigriceps moths (Lepidoptera: Arctiinae)

Many aposematic species show variation in their colour patterns even though selection by predators is expected to stabilise warning signals towards a common phenotype. Warning signal variability can be explained by trade-offs with other functions of colouration, such as thermoregulation, that may constrain warning signal expression by favouring darker individuals. Here, we investigated the effect of temperature on warning signal expression in aposematic Amata nigriceps moths that vary in their black and orange wing patterns. We sampled moths from two flight seasons that differed in the environmental temperatures and also reared different families under controlled conditions at three different temperatures. Against our prediction that lower developmental temperatures would reduce the warning signal size of the adult moths, we found no effect of temperature on warning signal expression in either wild or laboratory-reared moths. Instead, we found sex- and population-level differences in wing patterns. Our rearing experiment indicated that ~70% of the variability in the trait is genetic but understanding what signalling and non-signalling functions of wing colouration maintain the genetic variation requires further work. Our results emphasise the importance of considering both genetic and plastic components of warning signal expression when studying intraspecific variation in aposematic species.

Code and Additional Files for the Manuscript "The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand"

These scripts were used in the"The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand" manuscript. The scripts are ordered for ease of use. It also contains intermediate files and files necessary for the mapping. Table S1 containing the metadata is available in the supplementary information of the manuscript.

Fig. 8 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 8. Presence of i56 sequence varieties in Spongilla lacustris (Linnaeus, 1759) at different collecting sites. Black lines on the map represent watersheds between the catchment areas and cross hairs mark the actual sampling site. Seven sequence varieties were found, designated z-242T, z-242G, y-290G, y-290A, y-24G, w-377G and w-377T. Pie charts indicate fraction of the varieties at a site, and circle area corresponds to number of specimens sequenced from the site.

Fig. 7 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 7. Networks of i56 varieties in Swedish Spongilla lacustris (Linnaeus, 1759). Colours indicate catchment area. Seven varieties are designated; z-242T, z-242G, t-24G, y-290G, y-290A, w-377G and w-377T, see Table 3. A. PopART integer neighbor-joining network (described in Leigh & Bryant 2015). Pie chart size proportional to the number of specimens found of the variety. Hatches indicate number of substitutions between varieties, sites with indels are excluded; thus, no differences are picked up between z-242T and z-242G. B. SplitsTree neighbor network based on HKY85 distances.

Fig. 6 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 6. Neighbor-joining trees based on HKY85 distances. A. coxI dataset. B. 28S dataset. Sequences with a specimen field number (starting with P052- or P059-) are from the present study, remaining sequences are from GenBank with the accession number in the label.

Fig. 5 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 5. Eunapius fragilis (Leidy, 1851). A. Habitus (P059-170829-4). B. Spiculae, m = megasclere (P059-170830-13), g = gemmulosclere (P059-170902-1). C–D. Estimated spicula size distribution within and between specimens, P059-170802-10 (solid line), P059-170830-2 (dashed) and P059-170830- 4 (dotted). Marks on the x-axis represent spiculae measured. C. Megascleres. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019). D. Gemmuloscleres.

Fig. 3 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 3. Ephydatia fluviatilis (Linnaeus, 1759). A. Habitus (P059-211231-1). B. Spiculae, m = megasclere (P059-170902-2), g = gemmulosclere (P059-170902-1). C. Estimated megasclere size distribution within and between specimens, P059-170902-1 (solid line), P059-170902-2 (dashed), P059-170902-3 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019).

Fig. 4 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 4. Ephydatia muelleri (Lieberkühn, 1856). A. Habitus (P059-170829-4). B. Spiculae, m = megascleres (P059-170830-13), g = gemmuloscleres (P059-130828-18). C. Estimated megasclere size distribution within and between specimens, P059-130829-3 (solid line), P059-170830-13 (dashed) and UP-16-1-2 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019).

Fig. 1 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 1. Sampling sites and species found in main catchment areas in Sweden. Black lines represent watersheds between catchment areas, from the Swedish Meteorological and Hydrological Institute SVAR database (Westman et al. 2017). Crosses are sites visited but where no sponge was found, dots are sites where sponges were collected. The neighbouring pie icon indicates the species present at that site: blue (top right) is Spongilla lacustris (Linnaeus, 1759), yellow (bottom right) Eunapius fragilis (Leidy, 1851), black (bottom left) Ephydatia fluviatilis (Linnaeus, 1759) and green (top left) Ephydatia muelleri (Lieberkühn, 1856).

Fig. 2 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden

Fig. 2. Spongilla lacustris (Linnaeus, 1759). A–B. Habitus. A. Specimen growing under a pontoon (P059-171011-6). B. Specimen growing on anchor chain, with finger–like projections (P059-170802- 48). C–D. Spiculae. C. Megasclere (P059-171008-4). D. Microsclere (P059-171008-1). E–F. Estimated spicula size distribution within and between specimens, P059-130831-1 (solid line), P059-170802- 8 (dashed) and P059-171008-4 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019). E. Megascleres. F. Microscleres.

opencc-by-4.0Nov 2021View details →

Additively Manufactured Titanium 'Alloy-Alloy Composites' and Site-Specific Property Design

Paper links: Journal of Materials Characterization: https://doi.org/10.1016/j.matchar.2021.111577 Research Gate (free preprint): https://bit.ly/3xy1Ldm Video written and produced by Alec Davis and Jacob Kennedy. Research was conducted at the University of Manchester and Cranfield University, UK, by Jacob Kennedy, Alec Davis, Armando Caballero, Michael White, Jon Fellowes, Ed Pickering, and Phil Prangnell. This work was supported by grants: NEWAM (EPSRC EP/R027218/1), Lightform (EPSRC EP/R001715/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1).

opencc-by-4.0Aug 2022View details →

Additional Sponge Rheology Data for "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics"

These plots further support the general conclusions of the manuscript "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics". These data were collected on a Kinexus rheometer and the sponges were from a distinct shipment of sponges than those presented in the main manuscript.