Due to advances in modern medicine,liver transplantation has revolutionised the prognosis of many previously incurable liver diseases.This progress has largely been due to advances in immunosuppressant therapy.However...Due to advances in modern medicine,liver transplantation has revolutionised the prognosis of many previously incurable liver diseases.This progress has largely been due to advances in immunosuppressant therapy.However,despite the judicious use of immunosuppression,many liver transplant recipients still experience complications such as rejection,which necessitates diagnosis via invasive liver biopsy.There is a clear need for novel,minimally-invasive tests to optimise immunosuppression and improve patient outcomes.An emerging biomarker in this‘‘precision medicine’‘liver transplantation field is that of donorspecific cell free DNA.In this review,we detail the background and methods of detecting this biomarker,examine its utility in liver transplantation and discuss future research directions that may be most impactful.展开更多
European Space Aqency(ESA)’s PROBA-V Earth observation(EO)satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate,and provides guidance for important decisi...European Space Aqency(ESA)’s PROBA-V Earth observation(EO)satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate,and provides guidance for important decisions on our common global future.However,the interval at which high-resolution images are recorded spans over several days,in contrast to the availability of lower-resolution images which is often daily.We collect an extensive dataset of both high-and low-resolution images taken by PROBA-V instruments during monthly periods to investigate Multi Image Super-resolution,a technique to merge several low-resolution images into one image of higher quality.We propose a convolutional neural network(CNN)that is able to cope with changes in illumination,cloud coverage,and landscape features which are introduced by the fact that the different images are taken over successive satellite passages at the same region.Given a bicubic upscaling of low resolution images taken under optimal conditions,we find the Peak Signal to Noise Ratio of the reconstructed image of the network to be higher for a large majority of different scenes.This shows that applied machine learning has the potential to enhance large amounts of previously collected EO data during multiple satellite passes.展开更多
基金The University of Melbourne,Parkville 3000,VIC,Australia。
文摘Due to advances in modern medicine,liver transplantation has revolutionised the prognosis of many previously incurable liver diseases.This progress has largely been due to advances in immunosuppressant therapy.However,despite the judicious use of immunosuppression,many liver transplant recipients still experience complications such as rejection,which necessitates diagnosis via invasive liver biopsy.There is a clear need for novel,minimally-invasive tests to optimise immunosuppression and improve patient outcomes.An emerging biomarker in this‘‘precision medicine’‘liver transplantation field is that of donorspecific cell free DNA.In this review,we detail the background and methods of detecting this biomarker,examine its utility in liver transplantation and discuss future research directions that may be most impactful.
文摘European Space Aqency(ESA)’s PROBA-V Earth observation(EO)satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate,and provides guidance for important decisions on our common global future.However,the interval at which high-resolution images are recorded spans over several days,in contrast to the availability of lower-resolution images which is often daily.We collect an extensive dataset of both high-and low-resolution images taken by PROBA-V instruments during monthly periods to investigate Multi Image Super-resolution,a technique to merge several low-resolution images into one image of higher quality.We propose a convolutional neural network(CNN)that is able to cope with changes in illumination,cloud coverage,and landscape features which are introduced by the fact that the different images are taken over successive satellite passages at the same region.Given a bicubic upscaling of low resolution images taken under optimal conditions,we find the Peak Signal to Noise Ratio of the reconstructed image of the network to be higher for a large majority of different scenes.This shows that applied machine learning has the potential to enhance large amounts of previously collected EO data during multiple satellite passes.
