In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a b...In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.展开更多
In this article,the unit of total distance in Fig.2B was misused.In fact,the unit of total distance exported by the system was"pixels".After correction,the data in Fig.2B were not changed.The Fig.2B should h...In this article,the unit of total distance in Fig.2B was misused.In fact,the unit of total distance exported by the system was"pixels".After correction,the data in Fig.2B were not changed.The Fig.2B should have appeared as shown below.展开更多
Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic i...Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.展开更多
The potential use ofcomposted wood fibre waste （WFW） for the cultivation of bacterial antagonists of Sclerotinia minor was examined with the result that a mix of millet seed （20% w/w） and WFW, suitably amended wit...The potential use ofcomposted wood fibre waste （WFW） for the cultivation of bacterial antagonists of Sclerotinia minor was examined with the result that a mix of millet seed （20% w/w） and WFW, suitably amended with nutrients, proved to be an ideal matrix for the growth of some of these bacteria. Densities in terms ofcfu＇s ranged from 8.5 IOgl0 cfu/g dw to 10.5 logl0 cfu/g dw ullder sterile conditions after 14 days incubation. Lower population densities of the antagonists were achieved under non-sterile conditions in the compost： millet mix of between 7.9-9.3 logm cfu/g dw at the same period. However, when applied in a pot （glasshouse） trial to protect against S. minor, the millet seed appeared to stimulate the growth of this pathogen resulting in a high incidence of attack of lettuce plants after 2-3 weeks. Although the percentage of healthy seedlings increased following application of compost mix grown antagonists （at a rate of 5% v/v） when compared to the control treatment, these values were not statistically significant （p〉0.05） in most cases. Therefore, the use of millet seeds cannot be recommended as a nutrient supplement for the bacterial antagonist cultivation, if to be subsequently used to control fungal pathogens in the field.展开更多
基金Project supported by National Natural Science Foundation of China（ Grant No. 20373040）
文摘In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.
文摘In this article,the unit of total distance in Fig.2B was misused.In fact,the unit of total distance exported by the system was"pixels".After correction,the data in Fig.2B were not changed.The Fig.2B should have appeared as shown below.
基金supported in part by the National Natural Science Foundation of China,Nos.81927804(to GL),82260456(to LY),U21A20479(to LY)Science and Technology Planning Project of Shenzhen,No.JCYJ20230807140559047(to LY)+3 种基金Key-Area Research and Development Program of Guangdong Province,No.2020B0909020004(to GL)Guangdong Basic and Applied Research Foundation,No.2023A1515011478(to LY)the Science and Technology Program of Guangdong Province,No.2022A0505090007(to GL)Ministry of Science and Technology,Shenzhen,No.QN2022032013L(to LY)。
文摘Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.
文摘The potential use ofcomposted wood fibre waste （WFW） for the cultivation of bacterial antagonists of Sclerotinia minor was examined with the result that a mix of millet seed （20% w/w） and WFW, suitably amended with nutrients, proved to be an ideal matrix for the growth of some of these bacteria. Densities in terms ofcfu＇s ranged from 8.5 IOgl0 cfu/g dw to 10.5 logl0 cfu/g dw ullder sterile conditions after 14 days incubation. Lower population densities of the antagonists were achieved under non-sterile conditions in the compost： millet mix of between 7.9-9.3 logm cfu/g dw at the same period. However, when applied in a pot （glasshouse） trial to protect against S. minor, the millet seed appeared to stimulate the growth of this pathogen resulting in a high incidence of attack of lettuce plants after 2-3 weeks. Although the percentage of healthy seedlings increased following application of compost mix grown antagonists （at a rate of 5% v/v） when compared to the control treatment, these values were not statistically significant （p〉0.05） in most cases. Therefore, the use of millet seeds cannot be recommended as a nutrient supplement for the bacterial antagonist cultivation, if to be subsequently used to control fungal pathogens in the field.
