Image_2_Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.jpg

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.</p

Image_3_Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.jpeg

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.</p

Image_1_Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.jpeg

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.</p

Does public pension impact migrant workers’ nutrition: evidence from urban pension insurance in China

ABSTRACT: Based on the data of “employment and food demand of urban migrant workers”, this paper empirically analyzed the impact of urban pension insurance on the nutritional intake structure of migrant workers. The results showed that participating in urban pension insurance can change the nutritional intake structure of migrant workers. Additionally, fat and protein replace carbohydrate as the main nutrition sources for migrant workers. After controlling the income and labor intensity of migrant workers and other factors, urban pension insurance has a positive effect on the intake of fat and protein of migrant workers for they increase by 13.5% and 8.8% respectively. There is no significant effect on the intake of carbohydrates of migrant workers. The calorie intake of migrant workers increases by 6.8% accounting for the change of nutritional intake structure. Endogenous and robustness tests showed that the above conclusions are robust. Heterogeneity analysis showed that there is no significant difference in the effect of urban pension insurance on calorie intake of migrant workers in different income levels and age groups.</div

Understanding the Degradation Mechanisms of Pt Electrocatalysts in PEMFCs by Combining 2D and 3D Identical Location TEM

The evolution of Pt nanoparticles in proton-exchanged membrane fuel cells is monitored before and after electrochemical potential cycling, using 2D and 3D identical location aberration-corrected transmission electron microscopy. This work demonstrates that 2D images might be a challenge to interpret due to the 3D nature of the carbon support. Thus, it is critical to combine both 2D and 3D observations to be able to fully understand the mechanisms associated with the durability of Pt catalyst nanoparticles. In particular, this investigation reveals that the mechanism of particle migration followed by coalescence is operative mainly across short distances (<0.5 nm). This work also shows that new Pt particles appear on the carbon support, as the result of Pt dissolution, followed by the formation of clusters, which grow by Ostwald ripening. This mechanism of Ostwald ripening is also responsible for changes in shape and particle growth, which later may result in coalescence

sj-pdf-1-jba-10.1177_08853282221101148 – Supplemental Material for A printability study of multichannel nerve guidance conduits using projection-based three-dimensional printing

Supplemental Material, sj-pdf-1-jba-10.1177_08853282221101148 for A printability study of multichannel nerve guidance conduits using projection-based three-dimensional printing by Haibing Li, Kang Yu, Peng Zhang, Yensong Ye and Qiang Shu in Journal of Biomaterials Applications</p

Understanding the Degradation Mechanisms of Pt Electrocatalysts in PEMFCs by Combining 2D and 3D Identical Location TEM

The evolution of Pt nanoparticles in proton-exchanged membrane fuel cells is monitored before and after electrochemical potential cycling, using 2D and 3D identical location aberration-corrected transmission electron microscopy. This work demonstrates that 2D images might be a challenge to interpret due to the 3D nature of the carbon support. Thus, it is critical to combine both 2D and 3D observations to be able to fully understand the mechanisms associated with the durability of Pt catalyst nanoparticles. In particular, this investigation reveals that the mechanism of particle migration followed by coalescence is operative mainly across short distances (<0.5 nm). This work also shows that new Pt particles appear on the carbon support, as the result of Pt dissolution, followed by the formation of clusters, which grow by Ostwald ripening. This mechanism of Ostwald ripening is also responsible for changes in shape and particle growth, which later may result in coalescence

Understanding the Degradation Mechanisms of Pt Electrocatalysts in PEMFCs by Combining 2D and 3D Identical Location TEM

The evolution of Pt nanoparticles in proton-exchanged membrane fuel cells is monitored before and after electrochemical potential cycling, using 2D and 3D identical location aberration-corrected transmission electron microscopy. This work demonstrates that 2D images might be a challenge to interpret due to the 3D nature of the carbon support. Thus, it is critical to combine both 2D and 3D observations to be able to fully understand the mechanisms associated with the durability of Pt catalyst nanoparticles. In particular, this investigation reveals that the mechanism of particle migration followed by coalescence is operative mainly across short distances (<0.5 nm). This work also shows that new Pt particles appear on the carbon support, as the result of Pt dissolution, followed by the formation of clusters, which grow by Ostwald ripening. This mechanism of Ostwald ripening is also responsible for changes in shape and particle growth, which later may result in coalescence

DataSheet_3_Characteristics of Pyroptosis-Related Subtypes and Novel Scoring Tool for the Prognosis and Chemotherapy Response in Acute Myeloid Leukemia.pdf

Acute myeloid leukemia (AML) is usually associated with poor prognosis and low complete remission (CR) rate due to individual biological heterogeneity. Pyroptosis is a special form of inflammatory programmed cell death related to the progression, treatment response, and prognosis of multiple tumors. However, the potential connection of pyroptosis-related genes (PRGs) and AML still remains unclear. We described the genetic and transcriptional alterations of PRGs in 151 AML samples and presented a consensus clustering of these patients into two subtypes with distinct immunological and prognostic characteristics. Cluster A, associated with better prognosis, was characterized by relatively lower PRG expression, activated immune cells, higher immune scores in the tumor microenvironment (TME), and downregulation of immunotherapy checkpoints. Subsequently, a PRG score was constructed to predict overall survival (OS) of AML patients by using univariate and multivariate Cox regression analysis, and its immunological characteristics and predictive capability were further validated by 1,054 AML samples in external datasets. Besides an immune-activated status, low-PRG score cohorts exhibited higher chemotherapeutic drug sensitivity and significant positive correlation with the cancer stem cell (CSC) index. Combined with age, clinical French-American-British (FAB) subtypes, and PRG score, we successfully constructed a nomogram to effectively predict the 1-/3-/5-year survival rate of AML patients, and the predictive capability was further validated in multiple external datasets with a high area under the curve (AUC) value. The various transcriptomic analysis helps us screen significant pyroptosis-related signatures of AML and provide a new clinical application of PRG scores in predicting the prognosis and benefits of treatment for AML patients.</p

Understanding the Degradation Mechanisms of Pt Electrocatalysts in PEMFCs by Combining 2D and 3D Identical Location TEM

The evolution of Pt nanoparticles in proton-exchanged membrane fuel cells is monitored before and after electrochemical potential cycling, using 2D and 3D identical location aberration-corrected transmission electron microscopy. This work demonstrates that 2D images might be a challenge to interpret due to the 3D nature of the carbon support. Thus, it is critical to combine both 2D and 3D observations to be able to fully understand the mechanisms associated with the durability of Pt catalyst nanoparticles. In particular, this investigation reveals that the mechanism of particle migration followed by coalescence is operative mainly across short distances (<0.5 nm). This work also shows that new Pt particles appear on the carbon support, as the result of Pt dissolution, followed by the formation of clusters, which grow by Ostwald ripening. This mechanism of Ostwald ripening is also responsible for changes in shape and particle growth, which later may result in coalescence