Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these...Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these limitations through the development of a novel machine learning framework,Multiplex Image Machine Learning(MIML).This architecture uniquely combines label-free cell images with biomechanical property data,harnessing the vast,often underutilized biophysical information intrinsic to each cell.By integrating both types of data,our model offers a holistic understanding of cellular properties,utilizing cell biomechanical information typically discarded in traditional machine learning models.This approach has led to a remarkable 98.3%accuracy in cell classification,a substantial improvement over models that rely solely on image data.MIML has been proven effective in classifying white blood cells and tumor cells,with potential for broader application due to its inherent flexibility and transfer learning capability.It is particularly effective for cells with similar morphology but distinct biomechanical properties.This innovative approach has significant implications across various fields,from advancing disease diagnostics to understanding cellular behavior.展开更多
Manufacturers of chemicals are responsible for setting up a list of tools, including labels and safety data sheets, in order to provide adequate information about dangerous properties being labels and safety data shee...Manufacturers of chemicals are responsible for setting up a list of tools, including labels and safety data sheets, in order to provide adequate information about dangerous properties being labels and safety data sheets the main instruments for the immediate advice about dangerous substances and preparations for general public and workers. While correct labelling gives the possibility to general public to recognise the risks arising from handling and use of dangerous chemicals, safety data sheets are provided for professionals in order to allow safe handling and storage of dangerous chemicals in work places. Information contained in safety data sheets are also designed to suggest safety measures to be taken for the protection of workers as well as precaution measures and adequate actions to be taken in the case of accident. This project has critically revised the information contained in a list of safety data sheets of active ingredients provided for plant protection products, in order to assess the quality and the consistency of the data contained. Reported data have been then compared to published data. Considerable deficiencies/mistakes/inconsistencies have been found in the data reported along the safety data sheets of the examined substances, showing an urgent need of improving the enforcement related to a systematic recognition in this field as well as training of people involved in compilation of safety data sheets by producer side.展开更多
文摘Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these limitations through the development of a novel machine learning framework,Multiplex Image Machine Learning(MIML).This architecture uniquely combines label-free cell images with biomechanical property data,harnessing the vast,often underutilized biophysical information intrinsic to each cell.By integrating both types of data,our model offers a holistic understanding of cellular properties,utilizing cell biomechanical information typically discarded in traditional machine learning models.This approach has led to a remarkable 98.3%accuracy in cell classification,a substantial improvement over models that rely solely on image data.MIML has been proven effective in classifying white blood cells and tumor cells,with potential for broader application due to its inherent flexibility and transfer learning capability.It is particularly effective for cells with similar morphology but distinct biomechanical properties.This innovative approach has significant implications across various fields,from advancing disease diagnostics to understanding cellular behavior.
文摘Manufacturers of chemicals are responsible for setting up a list of tools, including labels and safety data sheets, in order to provide adequate information about dangerous properties being labels and safety data sheets the main instruments for the immediate advice about dangerous substances and preparations for general public and workers. While correct labelling gives the possibility to general public to recognise the risks arising from handling and use of dangerous chemicals, safety data sheets are provided for professionals in order to allow safe handling and storage of dangerous chemicals in work places. Information contained in safety data sheets are also designed to suggest safety measures to be taken for the protection of workers as well as precaution measures and adequate actions to be taken in the case of accident. This project has critically revised the information contained in a list of safety data sheets of active ingredients provided for plant protection products, in order to assess the quality and the consistency of the data contained. Reported data have been then compared to published data. Considerable deficiencies/mistakes/inconsistencies have been found in the data reported along the safety data sheets of the examined substances, showing an urgent need of improving the enforcement related to a systematic recognition in this field as well as training of people involved in compilation of safety data sheets by producer side.
