Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding...Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.展开更多
Ag nanocubes-reduced graphene oxide（AgNCs-rGO） nanocomposite was successfully prepared by an in situ synthesis method, in which AgNCs were loaded onto the surface of rGO during the formation of AgNCs in an ethylene ...Ag nanocubes-reduced graphene oxide（AgNCs-rGO） nanocomposite was successfully prepared by an in situ synthesis method, in which AgNCs were loaded onto the surface of rGO during the formation of AgNCs in an ethylene glycol solution. Characterization by X-ray diffraction, UV-Vis spectroscopy, and scanning electron microscopy indicated the successful preparation of the AgNCs-rGO nanocomposite. Most importantly, the AgNCs-rGO nanocomposite exhibited excellent electrocatalytic activity for the reduction of H2O2, leading to a high-performance non-enzymatic H2O2 sensor with a linear detection range and detection limit of approximately 0.1 mmol/L to 70 mmol/L（r=0.999） and 0.58 μmol/L, respectively. Our present work provides a new and highly efficient method for fabricating high-performance electrochemical sensors.展开更多
基金This work was supported by the National Key R&D Program of China(Grant No.2017YFE0129700)the National Natural Science Foundation of China(Key Program)(Grant No.11932013)+4 种基金the National Natural Science Foundation of China(Grant No.61673224)the Tianjin Natural Science Foundation for Distinguished Young Scholars(Grant No.18JCJQJC46100)the Tianjin Science and Technology Plan Project(Grant No.18ZXJMTG00260)based upon work from COST Action CA18106,supported by COST(European Cooperation in Science and Technology)supported by grants PICT 2017-3208 and UBACYT 20020170100192BA(Argentina)。
文摘Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.
基金Supported by the National Natural Science Foundation of China（No.61673191）.
文摘Ag nanocubes-reduced graphene oxide（AgNCs-rGO） nanocomposite was successfully prepared by an in situ synthesis method, in which AgNCs were loaded onto the surface of rGO during the formation of AgNCs in an ethylene glycol solution. Characterization by X-ray diffraction, UV-Vis spectroscopy, and scanning electron microscopy indicated the successful preparation of the AgNCs-rGO nanocomposite. Most importantly, the AgNCs-rGO nanocomposite exhibited excellent electrocatalytic activity for the reduction of H2O2, leading to a high-performance non-enzymatic H2O2 sensor with a linear detection range and detection limit of approximately 0.1 mmol/L to 70 mmol/L（r=0.999） and 0.58 μmol/L, respectively. Our present work provides a new and highly efficient method for fabricating high-performance electrochemical sensors.
