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Figure 1 in A replacement name for Chrysolina mikhailovi Lopatin, 2011 (Coleoptera: Chrysomelidae: Chrysomelinae), primary junior homonym of Ch. mikhailovi Ge & Daccordi, 2011
Figure 1. Holotype of Chrysolina mikhailovi Ge & Daccordi, 2011. A. Habitus; B–D. Aedeagus. Scale bars: A = 2.0 mm; B–D = 1.0 mm.
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.) in Cartilago Teniformis And Its Derivatives: Additional Information On The Basic Composition And Evolution Of The Skull
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.)
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21<sup>st</sup> century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards.</p> <p>In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes.</p> <p>Our abstract reads:</p> <p>“The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.</p> <p> </p> <p><strong>Citation.</strong></p> <p>Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script.</p> <p>Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
PML-V2 China staple crop (maize, wheat, rice) evapotranspiration, gross primary production and yield over 2003-2018
<p>This dataset provides the yearly evapotranspiration (ET), gross primary production (GPP) and yield of the three staple crops (i.e., maize, wheat and rice) of China from 2003 to 2018. ET and GPP are estimated by the Penman-Monteith-Leuning version 2 model (PML-V2 model), and crop yield is the product of GPP and harvest index. The units of ET, GPP and yield are mm year<sup>-1</sup>, g C m<sup>-2</sup> year<sup>-1</sup> and kg ha<sup>-1</sup>, respectively.</p> <p>The PML-V2 model is calibrated and validated against the observed ET and GPP at EC sites for each crop type. The PML_V2 model uses CMFD meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 crop evapotranspiration, gross primary production datasets.</p> <p>The data format is tiff, the spatiotemporal resolution is yearly and 0.0125°, and the time span is 2003-2018. The file name is "crop_variable_year.tif". For example, a file named Maize_ETsum_2003.tif corresponds to the total ET of maize in 2003.</p>
Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"
<p>This repository provides the code and data for the study "Hotspots and drivers of compound marine heatwave and low net primary production extremes". All processed data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch. </p>
Figs 56–58 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 56–58. Leptochiton (L.) permodestus, holotype C8005/T3044: (56 & 57) lateral views; (58) dorsal view. Scale bar = 1 mm.
Fig. 55 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Fig. 55. Leptochiton (L.) permodestus, holotype C8005/T3044: anterio-ventral view. Scale bar = 1 mm.
Figs 53, 54 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 53, 54. Chaetopleura (C.) natalensis, holotype D4842/T20: (53) fragments of perinotum with the enlarged area (A); (54) radula.
Figs 48–50 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 48–50. Leptochiton (L.) meiringae, holotype D265/T3046: (48) head and II valves view; (49) fragments of valve VII; (50) tail valve. Scale bar = 1 mm.
Fig. 38 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Fig. 38. Leptochiton (Parachiton) indecorus, holotype D7408/T14: spicules of girdle with edge of perinotum.
Figs 44, 45 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 44, 45. Leptochiton (L.) meiringae, holotype D265/T3046: (44) dorsal view; (45) ventral view. Scale bar = 1 mm.
Figs 42, 43. Ischnochiton kilburni, holotype G8789 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 42, 43. Ischnochiton kilburni, holotype G8789/T2393: (42) head and II valves view; (43) VII and tail valves view. Scale bar = 1 mm.
Figs 29–31. Ischnochiton hewitti, holotype B7413 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 29–31. Ischnochiton hewitti, holotype B7413/T2848: (29) head valve; (30) valve III; (31) tail valve. Scale bar = 1 mm.
Figs. 20, 21 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs. 20, 21. Leptochiton (L.) dispersus, holotype C4658/T3047: (20) edge of perinotum with the enlarged areas (A & B); (21) radula.
Figs 18, 19 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 18, 19. Leptochiton (L.) dispersus, holotype C4658/T3047: (18) valve VII; (19) tail valve. Scale bar = 1 mm.
Figs 16,17 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 16,17. Leptochiton (L.) dispersus, holotype C4658/T3047: (16) valve V; (17) valve VI. Scale bar = 1 mm.
Figs 13–15 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 13–15. Leptochiton (L.) dispersus, holotype C4658/T3047: (13) head valve; (14) valve III; (15) valve IV. Scale bar = 1 mm.
Figs 36, 37 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Figs 36, 37. Leptochiton (Parachiton) indecorus, holotype D7408/T14: (36) lateral view; (37) ventral view. Scale bar = 1 mm.
Fig. 9 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora
Fig. 9. Lepidochitona (L.) dicksae, holotype V6152/T1539: valves VI and VII attached to the body, in alcohol.
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