Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals

Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C. Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M). The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153. The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C. Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs. We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world. The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is Global Planting Suitability of Wheat.zip. This package consists of 1 folder, i.e., SR1.5&2.0. This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly Wheat_SR15.tif and Wheat_SR20.tif. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists. Reference: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Socioeconomic and climate change are both essential factors affecting the global cultivation distributions of crops. However, the role of socioeconomic factors in the prediction of future crop cultivation distribution under climate change has been rarely explored. Here, we proposed the MaxEnt-SPAM approach assuming that environmental conditions are the fundamental factors determining whether land is suitable for cultivating wheat, and socioeconomic factors are the driving forces that influence farmers’ crop choices. In short, the distribution of wheat cultivation depends on the maximization of potential revenue as well as satisfying wheat planting suitability. The proposed MaxEnt-SPAM approach for estimating the cultivation distribution of wheat in three combined Representative concentration pathway (RCP) -Shared socioeconomic pathway (SSP) scenarios, i.e., RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3). The steps were as follows: (1) Estimate wheat planting suitability under future RCP scenarios by the MaxEnt model. (2) Estimate farmers' crop choices under future SSP scenarios using Time series- Backpropagation (TS-BP) models. (3) Estimate global wheat cultivation distribution based on the SPAM model. Based on major known datasets on the distribution of wheat cultivation, the proposed MaxEnt-SPAM approach was carefully validated by comparing its prediction results with those known datasets. Satisfactory accuracy was achieved. It indicates that the predictive accuracy of the proposed approach could be over 85%, with a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Based on the above idea and approach, a grid (0.5 degree × 0.5 degree) global wheat cultivation distribution under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios were predicted. The results indicate that RCP8.5-SSP3 might be the most favorable for wheat cultivation. Moreover, socioeconomic development significantly restricts the potential distribution of wheat cultivation. The estimated wheat cultivation areas considering the effects of socioeconomic development average account for 77% of the potential wheat distribution determined by climatic factors under the selected RCP-SSP scenarios. Socio-economic development seems to benefit wheat cultivation in Africa. Our findings demonstrate the influence of socioeconomic factors on crop distribution from the perspective of the market economy, highlighting the necessity of coupling socioeconomic factors and climate change to accurately predict crop cultivation distribution. We argue that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable complement to currently available products. This wheat cultivation distribution prediction data is one of the few products to take into account both climate change and the drive for socio-economic development. We believe that it can provide a product that is more consistent with the logic of crop cultivation distribution than those that only consider climate change impacts. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to allow us to better understand the dynamics and distribution of global wheat cultivation distribution under different climate change and socio-economic development paths in the future. These data can potentially provide support for relevant research. Such as but not limited to earth system simulation, and agricultural sciences. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Datasets and the Maxent-SPAM approach code are stored in a zip package, that SPAM_MaxEnt.zip. This package consists of 2 folders (code, and data) shown as follows. code: This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information. The exampleData contains 1. h_pri.tif: prior data 2. h_res.tif: global C3 crop cultivation proportion Run the main programme: cross_entroy.m data: This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format. 1 Global distribution of the long-term wheat-cultivation area fraction: This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated. r2s1f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario r4s2f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario r8s3f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario 2 Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution: This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:{1, 2, 3}, 1 wheat cultivation existed but was predicted to be unsuitable to plant wheat; 2 presented a reduction in the wheat cultivation area compared to the land's suitability; 3 presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat. com_suit_fra126.tif: the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6 com_suit_fra245.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5 com_suit_fra385.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5 3 Differences in the proportion of long-term wheat cultivation: This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability. r2s1_f.tif: Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario r4s2_f.tif: Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario r8s3_f.tif: Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario References: For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014). Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026. You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003. You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.00

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE&macr; = 11.1d) is better than daily fusion datasets (RMSE&macr; = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE&macr; of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation

Morphology evolution of TiO2 nanotubes with additional reducing agent: Evidence of oxygen release

In order to explore wider application of porous anodic alumina and anodic TiO2 nanotubes (ATNTs), the formation mechanism research of porous anodic materials plays a more significant role. Traditional field-assisted dissolution theory has been put into question and oxygen bubble mold has been accepted gradually. However, it is difficult to prove oxygen release. In this work, we present a method to demonstrate the presence of oxygen in the pores of nanotubes. A kind of water soluble reducing agent (NH4H2PO2) was added into electrolyte containing NH4F. Cavities exists not only between the double walls of nanotubes but also in the inner walls, which are different from normal nanotubes. The morphology evolution of nanotubes results from the reaction between NH4H2PO2 and oxygen. Therefore, the release of oxygen during the formation of ATNTs was further proved. Keywords: Anodic TiO2 nanotubes, Reducing agent, Morphology evolution, Anodizatio

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation

Waves Propagating in Nano-Layered Phononic Crystals with Flexoelectricity, Microstructure, and Micro-Inertia Effects

The miniaturization of electronic devices is an important trend in the development of modern microelectronics information technology. However, when the size of the component or the material is reduced to the micro/nano scale, some size-dependent effects have to be taken into account. In this paper, the wave propagation in nano phononic crystals is investigated, which may have a potential application in the development of acoustic wave devices in the nanoscale. Based on the electric Gibbs free energy variational principle for nanosized dielectrics, a theoretical framework describing the size-dependent phenomenon was built, and the governing equation as well as the dispersion relation derived; the flexoelectric effect, microstructure, and micro-inertia effects are taken into consideration. To uncover the influence of these three size-dependent effects on the width and midfrequency of the band gaps of the waves propagating in periodically layered structures, some related numerical examples were shown. Comparing the present results with the results obtained with the classical elastic theory, we find that the coupled effects of flexoelectricity, microstructure, and micro-inertia have a significant or even dominant influence on the waves propagating in phononic crystals in the nanoscale. With increase in the size of the phononic crystal, the size effects gradually disappear and the corresponding dispersion curves approach the dispersion curves obtained with the conventional elastic theory, which verify the results obtained in this paper. Thus, when we study the waves propagating in phononic crystals in the micro/nano scale, the flexoelectric, microstructure, and micro-inertia effects should be considered

Anti-Virulence Strategy of Novel Dehydroabietic Acid Derivatives: Design, Synthesis, and Antibacterial Evaluation

Anti-virulence strategies are attractive and interesting strategies for controlling bacterial diseases because virulence factors are fundamental to the infection process of numerous serious phytopathogenics. To extend the novel anti-virulence agents, a series of dehydroabietic acid (DAA) derivatives decorated with amino alcohol unit were semi-synthesized based on structural modification of the renewable natural DAA and evaluated for their antibacterial activity against Xanthomonas oryzae pv. oryzae (Xoo), Xanthomonas axonopodis pv. citri (Xac), and Pseudomonas syringae pv. actinidiae (Psa). Compound 2b showed the most promising antibacterial activity against Xoo with an EC50 of 2.7 &mu;g mL&minus;1. Furthermore, compound 2b demonstrated remarkable control effectiveness against bacterial leaf blight (BLB) in rice, with values of 48.6% and 61.4% for curative and protective activities. In addition, antibacterial behavior suggested that compound 2b could suppress various virulence factors, including EPS, biofilm, swimming motility, and flagella. Therefore, the current study provided promising lead compounds for novel bactericides discovery by inhibiting bacterial virulence factors

DataSheet_3_Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis.zip

BackgroundThyroid associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on individuals and society. The etiology of TAO is complicated and poorly understood. Thus, the goal of this study was to use bioinformatics to look into the pathogenesis of TAO and to identify the optimum feature genes (OFGs) and immune infiltration patterns of TAO.MethodsFirstly, the GSE58331 microarray data set was utilized to find 366 differentially expressed genes (DEGs). To find important modular genes, the dataset was evaluated using weighted gene coexpression network analysis (WGCNA). Then, the overlap genes of major module genes and DEGs were further assessed by applying three machine learning techniques to find the OFGs. The CIBERSORT approach was utilized to examine immune cell infiltration in normal and TAO samples, as well as the link between optimum characteristic genes and immune cells. Finally, the related pathways of the OFGs were predicted using single gene set enrichment analysis (ssGSEA).ResultsKLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 were the six best feature genes that were employed to create a nomogram with high predictive performance. The immune cell infiltration investigation revealed that the development of TAO may include memory B cells, T cell follicular helper cells, resting NK cells, macrophages of type M0, macrophages of type M1, resting dendritic cells, active mast cells, and neutrophils. In addition, ssGSEA results found that these characteristic genes were closely associated with lipid metabolism pathways.ConclusionIn this research, we found that KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 are intimately associated with the development and progression of TAO, as well as with lipid metabolism pathways.</p

DataSheet_4_Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis.zip

BackgroundThyroid associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on individuals and society. The etiology of TAO is complicated and poorly understood. Thus, the goal of this study was to use bioinformatics to look into the pathogenesis of TAO and to identify the optimum feature genes (OFGs) and immune infiltration patterns of TAO.MethodsFirstly, the GSE58331 microarray data set was utilized to find 366 differentially expressed genes (DEGs). To find important modular genes, the dataset was evaluated using weighted gene coexpression network analysis (WGCNA). Then, the overlap genes of major module genes and DEGs were further assessed by applying three machine learning techniques to find the OFGs. The CIBERSORT approach was utilized to examine immune cell infiltration in normal and TAO samples, as well as the link between optimum characteristic genes and immune cells. Finally, the related pathways of the OFGs were predicted using single gene set enrichment analysis (ssGSEA).ResultsKLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 were the six best feature genes that were employed to create a nomogram with high predictive performance. The immune cell infiltration investigation revealed that the development of TAO may include memory B cells, T cell follicular helper cells, resting NK cells, macrophages of type M0, macrophages of type M1, resting dendritic cells, active mast cells, and neutrophils. In addition, ssGSEA results found that these characteristic genes were closely associated with lipid metabolism pathways.ConclusionIn this research, we found that KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 are intimately associated with the development and progression of TAO, as well as with lipid metabolism pathways.</p

DataSheet_5_Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis.zip

BackgroundThyroid associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on individuals and society. The etiology of TAO is complicated and poorly understood. Thus, the goal of this study was to use bioinformatics to look into the pathogenesis of TAO and to identify the optimum feature genes (OFGs) and immune infiltration patterns of TAO.MethodsFirstly, the GSE58331 microarray data set was utilized to find 366 differentially expressed genes (DEGs). To find important modular genes, the dataset was evaluated using weighted gene coexpression network analysis (WGCNA). Then, the overlap genes of major module genes and DEGs were further assessed by applying three machine learning techniques to find the OFGs. The CIBERSORT approach was utilized to examine immune cell infiltration in normal and TAO samples, as well as the link between optimum characteristic genes and immune cells. Finally, the related pathways of the OFGs were predicted using single gene set enrichment analysis (ssGSEA).ResultsKLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 were the six best feature genes that were employed to create a nomogram with high predictive performance. The immune cell infiltration investigation revealed that the development of TAO may include memory B cells, T cell follicular helper cells, resting NK cells, macrophages of type M0, macrophages of type M1, resting dendritic cells, active mast cells, and neutrophils. In addition, ssGSEA results found that these characteristic genes were closely associated with lipid metabolism pathways.ConclusionIn this research, we found that KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 are intimately associated with the development and progression of TAO, as well as with lipid metabolism pathways.</p