The technique of imaging or tracking objects outside the field of view(FOV)through a reflective relay surface,usually called non-line-of-sight(NLOS)imaging,has been a popular research topic in recent years.Although NL...The technique of imaging or tracking objects outside the field of view(FOV)through a reflective relay surface,usually called non-line-of-sight(NLOS)imaging,has been a popular research topic in recent years.Although NLOS imaging can be achieved through methods such as detector design,optical path inverse operation algorithm design,or deep learning,challenges such as high costs,complex algorithms,and poor results remain.This study introduces a simple algorithm-based rapid depth imaging device,namely,the continuous-wave time-offlight range imaging camera(CW-TOF camera),to address the decoupled imaging challenge of differential scattering characteristics in an object-relay surface by quantifying the differential scattering signatures through statistical analysis of light propagation paths.A scalable scattering mapping(SSM)theory has been proposed to explain the degradation process of clear images.High-quality NLOS object 3D imaging has been achieved through a data-driven approach.To verify the effectiveness of the proposed algorithm,experiments were conducted using an optical platform and real-world scenarios.The objects on the optical platform include plaster sculptures and plastic letters,while relay surfaces consist of polypropylene(PP)plastic boards,acrylic boards,and standard Lambertian diffusers.In real-world scenarios,the object is clothing,with relay surfaces including painted doors and white plaster walls.Imaging data were collected for different combinations of objects and relay surfaces for training and testing,totaling 210,000 depth images.The reconstruction of NLOS images in the laboratory and real-world is excellent according to subjective evaluation;thus,our approach can realize NLOS imaging in harsh natural scenes and advances the practical application of NLOS imaging.展开更多
Colorectal cancer(CRC)remains a leading cause of cancer-related mortality,with liver metastasis posing a significant therapeutic challenge.Within the“seed and soil”paradigm,disrupting both tumor cells and their supp...Colorectal cancer(CRC)remains a leading cause of cancer-related mortality,with liver metastasis posing a significant therapeutic challenge.Within the“seed and soil”paradigm,disrupting both tumor cells and their supportive microenvironment is essential to suppress disease progression.Here,we utilized single-cell tran-scriptomics of clinical CRC samples identified NOX4+(NADPH oxidase 4 positive)cancer-associated fibroblasts(CAFs)and CXCR4+(C-X-C motif chemokine receptor 4 positive)/GPX4+(glutathione peroxidase 4 positive)tumor cells as critical drivers of metastasis.Consequently,a dual-targeted nanosystem was thus devised to induce ferroptosis in tumor cells and reprogram CAFs.This strategy integrates a ferroptosis inducer encapsulated within the cancer cell membrane and a CXCR4-NOX4 inhibitor loaded onto a hybrid membrane composed of cancer cells and CAFs,thereby achieving dual synergistic effects:ferroptotic eradication of malignant cells and induc-tion of CAFs quiescence.In orthotopic,liver metastasis,and patient-derived tumor xenograft humanized immune mouse models,these nanoparticles significantly suppressed tumor growth,mitigated immunosuppressive signaling,and augmented antitumor immune responses,while maintaining favorable biocompatibility.These findings highlight the potential of simultaneously targeting ferroptosis in tumor cells and CAFs reprogramming in the tumor microenvironment to overcome liver metastasis of CRC.展开更多
Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature(Tg ) of GexSe1-xglass transition remains an open challenge. In this paper, a predicti...Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature(Tg ) of GexSe1-xglass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-xglass system is presented by a machine learning method named feature selection based two-stage support vector regression(FSTS-SVR). Firstly, Pearson correlation coefficient(PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error(RMSE) and mean absolute percentage error(MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.展开更多
基金National Key Research and Development Program of China(2023YFC3321600)Special Project for Research and Development in Key Areas of Guangdong Province(2023ZDZX1044)+1 种基金Zhuhai Multimodal Intelligent Vision Engineering Technology Research Center(2320004002292)Zhuhai Basic and Applied Basic Research Foundation(2220004002937)。
文摘The technique of imaging or tracking objects outside the field of view(FOV)through a reflective relay surface,usually called non-line-of-sight(NLOS)imaging,has been a popular research topic in recent years.Although NLOS imaging can be achieved through methods such as detector design,optical path inverse operation algorithm design,or deep learning,challenges such as high costs,complex algorithms,and poor results remain.This study introduces a simple algorithm-based rapid depth imaging device,namely,the continuous-wave time-offlight range imaging camera(CW-TOF camera),to address the decoupled imaging challenge of differential scattering characteristics in an object-relay surface by quantifying the differential scattering signatures through statistical analysis of light propagation paths.A scalable scattering mapping(SSM)theory has been proposed to explain the degradation process of clear images.High-quality NLOS object 3D imaging has been achieved through a data-driven approach.To verify the effectiveness of the proposed algorithm,experiments were conducted using an optical platform and real-world scenarios.The objects on the optical platform include plaster sculptures and plastic letters,while relay surfaces consist of polypropylene(PP)plastic boards,acrylic boards,and standard Lambertian diffusers.In real-world scenarios,the object is clothing,with relay surfaces including painted doors and white plaster walls.Imaging data were collected for different combinations of objects and relay surfaces for training and testing,totaling 210,000 depth images.The reconstruction of NLOS images in the laboratory and real-world is excellent according to subjective evaluation;thus,our approach can realize NLOS imaging in harsh natural scenes and advances the practical application of NLOS imaging.
基金supported by the Guangdong Province Natural Sci-ence Foundation(2024A1515012061)the Key Clinical Research Proj-ect of Southern Medical University(LC2019ZD011)+4 种基金the Guangdong Basic and Applied Basic Research Foundation(2022A1515110270)the Natural Science Foundation of Jiangsu Province(BK20240466)the China Postdoctoral Science Foundation(2024M763181)the Excellent Postdoctoral Program in Jiangsu Province(2024ZB076)the Guangdong Provincial Key Laboratory of Digital Medicine and Biome-chanics(2023B110008).
文摘Colorectal cancer(CRC)remains a leading cause of cancer-related mortality,with liver metastasis posing a significant therapeutic challenge.Within the“seed and soil”paradigm,disrupting both tumor cells and their supportive microenvironment is essential to suppress disease progression.Here,we utilized single-cell tran-scriptomics of clinical CRC samples identified NOX4+(NADPH oxidase 4 positive)cancer-associated fibroblasts(CAFs)and CXCR4+(C-X-C motif chemokine receptor 4 positive)/GPX4+(glutathione peroxidase 4 positive)tumor cells as critical drivers of metastasis.Consequently,a dual-targeted nanosystem was thus devised to induce ferroptosis in tumor cells and reprogram CAFs.This strategy integrates a ferroptosis inducer encapsulated within the cancer cell membrane and a CXCR4-NOX4 inhibitor loaded onto a hybrid membrane composed of cancer cells and CAFs,thereby achieving dual synergistic effects:ferroptotic eradication of malignant cells and induc-tion of CAFs quiescence.In orthotopic,liver metastasis,and patient-derived tumor xenograft humanized immune mouse models,these nanoparticles significantly suppressed tumor growth,mitigated immunosuppressive signaling,and augmented antitumor immune responses,while maintaining favorable biocompatibility.These findings highlight the potential of simultaneously targeting ferroptosis in tumor cells and CAFs reprogramming in the tumor microenvironment to overcome liver metastasis of CRC.
基金supported by the National Key R&D Program of China (2017YFB0701500 and 2017YFB0701600)the National Natural Science Foundation of China (51602187, U1630134, 11874254 and 51622207)the Shanghai Municipal Education Commission (14ZZ099 and QD2015028)
文摘Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature(Tg ) of GexSe1-xglass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-xglass system is presented by a machine learning method named feature selection based two-stage support vector regression(FSTS-SVR). Firstly, Pearson correlation coefficient(PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error(RMSE) and mean absolute percentage error(MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
