For the flow field in a d50 mm hydrocyclone, numerical studies based on computational fluid dynamics (CFD) simulation and experimental studies based on particle image velocimetry (PIV) measurement were carried out res...For the flow field in a d50 mm hydrocyclone, numerical studies based on computational fluid dynamics (CFD) simulation and experimental studies based on particle image velocimetry (PIV) measurement were carried out respectively. The results of two methods show that air core generally forms after 0.7 s, the similar characteristics of air core can be observed. Vortexes and axial velocity distributions obtained by numerical and experimental methods are also in good agreement. Studies of different parameters based on CFD simulation show that tangential velocity distribution inside the hydrocyclone can be regarded as a combined vortex. Axial and tangential velocities increase as the feed rate increases. The enlargement of cone angle and overflow outlet diameter can speed up the overflow discharge rate. The change of underflow outlet diameter has no significant effect on axial and tangential velocities.展开更多
Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In thi...Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation. We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.展开更多
Anti-parallel β-sheet crystallite as the main component of silk fibroin has attracted much attention due to its superior mechanical properties. In this study, we examine the processes of pulling a peptide chain from ...Anti-parallel β-sheet crystallite as the main component of silk fibroin has attracted much attention due to its superior mechanical properties. In this study, we examine the processes of pulling a peptide chain from β-sheet crystallite using steered molecular dynamics simulations to investigate the rupture behavior of the crystallite. We show that the failure of β-sheet crystallite was accompanied by a propagation of instability of hydrogen-bonds （H-bonds） in the crystallite. In addition, we find that there is an optimum size of the crystallite at which the H-bonds can work cooperatively to achieve the highest shear strength. In addition, we find that the stiffness of loading device and the loading rates have significant effects on the rupture behavior of β-sheet crystallite. The stiff loading device facilitates the rebinding of the Hbond network in the stick-slip motion between the chains, while the soft one suppresses it. Moreover, the rupture force of β-sheet crystallites decreases with loading rate. Particularly, when the loading rate decreases to a critical value, the rupture force of the β-sheet crystallite becomes independent of the loading rates. This study provides atomistic details of rupture behaviors of β-sheet crystallite, and, therefore, sheds valuable light on the underlying mechanism of the superior mechanical properties of silk fibroin.展开更多
Colorectal cancer(CRC)is the second most common cause of cancer-related death worldwide and places a major economic burden on the global health care system.The time frame for development from premalignant to malignant...Colorectal cancer(CRC)is the second most common cause of cancer-related death worldwide and places a major economic burden on the global health care system.The time frame for development from premalignant to malignant disease typically spans 10-15 years,and this latent period provides an ideal opportunity for early detection and intervention to improve patient outcomes.Currently,early diagnosis of CRC is hampered by a lack of suitable non-invasive biomarkers that are clinically or economically acceptable for populationbased screening.New blood-based protein biomarkers for early detection of CRC are therefore urgently required.The success of clinical biomarker discovery and validation studies is critically dependent on understanding and adjusting for potential experimental,analytical,and biological factors that can interfere with the robust interpretation of results.In this review we outline some important considerations for research groups undertaking biomarker research with exemplars from our studies.Implementation of experimental strategies to minimise the potential effects of these problems will facilitate the identification of panels of biomarkers with the sensitivity and specificity required for the development of successful tests for the early detection and surveillance of CRC.展开更多
A hydrological simulation in the Huaihe River Basin(HRB) was investigated using two different models: a coupled land surface hydrological model(CLHMS), and a large-scale hydrological model(LSX-HMS). The NCEP-NCAR rean...A hydrological simulation in the Huaihe River Basin(HRB) was investigated using two different models: a coupled land surface hydrological model(CLHMS), and a large-scale hydrological model(LSX-HMS). The NCEP-NCAR reanalysis dataset and observed precipitation data were used as meteorological inputs. The simulation results from both models were compared in terms of flood processes forecasting during high flow periods in the summers of 2003 and 2007, and partial high flow periods in 2000. The comparison results showed that the simulated streamflow by CLHMS model agreed well with the observations with Nash-Sutcliffe coefficients larger than 0.76, in both periods of 2000 at Lutaizi and Bengbu stations in the HRB, while the skill of the LSX-HMS model was relatively poor. The simulation results for the high flow periods in 2003 and 2007 suggested that the CLHMS model can simulate both the peak time and intensity of the hydrological processes, while the LSX-HMS model provides a delayed flood peak. These results demonstrated the importance of considering the coupling between the land surface and hydrological module in achieving better predictions for hydrological processes, and CLHMS was proven to be a promising model for future applications in flood simulation and forecasting.展开更多
Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable p...Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable parameters(river flow,rainfall,salinity)as proxies to determine when to close farms.We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall.Time-series event prediction consists of two steps:(i)feature extraction,and(ii)prediction.A number of data mining challenges exist for these scenarios:(i)which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?,(ii)The farm closure events occur infrequently and this leads to a class imbalance problem;the question is what is the best way to deal with this problem?In this paper we have analysed and compared different combinations of balancing methods(under-sampling and over-sampling),feature extraction methods(cluster profile,curve fitting,Fourier Transform,Piecewise Aggregate Approximation,and Wavelet Transform)and learning algorithms(neural network,support vector machine,k-nearest neighbour,decision tree,and Bayesian Network)to predict closure events accurately considering the above data mining challenges.We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall,given the above data mining challenges.展开更多
A key control point in gene expression is the initiation of protein translation, with a universal stress response being constituted by in- hibitory phosphoryiation of the eukaryotic initiation factor 2α （el F2oL）. ...A key control point in gene expression is the initiation of protein translation, with a universal stress response being constituted by in- hibitory phosphoryiation of the eukaryotic initiation factor 2α （el F2oL）. In humans, four kinases sense diverse physiological stresses to regulate elF2α to control cell differentiation, adaptation, and survival. Here we develop a computational molecular model of elF2α and one of its kinases, the protein kinase R, to simulate the dynamics of their interaction. Predictions generated by coarse-grained dynamics simulations suggest a novel mode of action. Experimentation substantiates these predictions, identifying a previously unrecognized interface in the protein complex, which is constituted by dynamic residues in both elF2α and its kinases that are crucial to regulate protein translation. These findings call for a reinterpretation of the current mechanism of action of the el F2α kinases and demonstrate the value of conducting computational analysis to evaluate protein function.展开更多
基金Projects(50974033,51104035)supported by the National Natural Science Foundation of China
文摘For the flow field in a d50 mm hydrocyclone, numerical studies based on computational fluid dynamics (CFD) simulation and experimental studies based on particle image velocimetry (PIV) measurement were carried out respectively. The results of two methods show that air core generally forms after 0.7 s, the similar characteristics of air core can be observed. Vortexes and axial velocity distributions obtained by numerical and experimental methods are also in good agreement. Studies of different parameters based on CFD simulation show that tangential velocity distribution inside the hydrocyclone can be regarded as a combined vortex. Axial and tangential velocities increase as the feed rate increases. The enlargement of cone angle and overflow outlet diameter can speed up the overflow discharge rate. The change of underflow outlet diameter has no significant effect on axial and tangential velocities.
文摘Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation. We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.
基金supported by the National Science Foundation of China (Grants 11025208, 11372042, 11221202, and 11202026)the support from CSIRO-Intelligent Processing TCP+1 种基金CAFHS’ Capability Development FundCSIRO-Advanced Materials TCP
文摘Anti-parallel β-sheet crystallite as the main component of silk fibroin has attracted much attention due to its superior mechanical properties. In this study, we examine the processes of pulling a peptide chain from β-sheet crystallite using steered molecular dynamics simulations to investigate the rupture behavior of the crystallite. We show that the failure of β-sheet crystallite was accompanied by a propagation of instability of hydrogen-bonds （H-bonds） in the crystallite. In addition, we find that there is an optimum size of the crystallite at which the H-bonds can work cooperatively to achieve the highest shear strength. In addition, we find that the stiffness of loading device and the loading rates have significant effects on the rupture behavior of β-sheet crystallite. The stiff loading device facilitates the rebinding of the Hbond network in the stick-slip motion between the chains, while the soft one suppresses it. Moreover, the rupture force of β-sheet crystallites decreases with loading rate. Particularly, when the loading rate decreases to a critical value, the rupture force of the β-sheet crystallite becomes independent of the loading rates. This study provides atomistic details of rupture behaviors of β-sheet crystallite, and, therefore, sheds valuable light on the underlying mechanism of the superior mechanical properties of silk fibroin.
基金Supported by The CSIRO Preventative Health National Research Flagship and the National Health and Medical Research Council,No.1017078
文摘Colorectal cancer(CRC)is the second most common cause of cancer-related death worldwide and places a major economic burden on the global health care system.The time frame for development from premalignant to malignant disease typically spans 10-15 years,and this latent period provides an ideal opportunity for early detection and intervention to improve patient outcomes.Currently,early diagnosis of CRC is hampered by a lack of suitable non-invasive biomarkers that are clinically or economically acceptable for populationbased screening.New blood-based protein biomarkers for early detection of CRC are therefore urgently required.The success of clinical biomarker discovery and validation studies is critically dependent on understanding and adjusting for potential experimental,analytical,and biological factors that can interfere with the robust interpretation of results.In this review we outline some important considerations for research groups undertaking biomarker research with exemplars from our studies.Implementation of experimental strategies to minimise the potential effects of these problems will facilitate the identification of panels of biomarkers with the sensitivity and specificity required for the development of successful tests for the early detection and surveillance of CRC.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05110202)the National Natural Science Foundation of China (Grant Nos. 41175073, 41471016, and U1133603)
文摘A hydrological simulation in the Huaihe River Basin(HRB) was investigated using two different models: a coupled land surface hydrological model(CLHMS), and a large-scale hydrological model(LSX-HMS). The NCEP-NCAR reanalysis dataset and observed precipitation data were used as meteorological inputs. The simulation results from both models were compared in terms of flood processes forecasting during high flow periods in the summers of 2003 and 2007, and partial high flow periods in 2000. The comparison results showed that the simulated streamflow by CLHMS model agreed well with the observations with Nash-Sutcliffe coefficients larger than 0.76, in both periods of 2000 at Lutaizi and Bengbu stations in the HRB, while the skill of the LSX-HMS model was relatively poor. The simulation results for the high flow periods in 2003 and 2007 suggested that the CLHMS model can simulate both the peak time and intensity of the hydrological processes, while the LSX-HMS model provides a delayed flood peak. These results demonstrated the importance of considering the coupling between the land surface and hydrological module in achieving better predictions for hydrological processes, and CLHMS was proven to be a promising model for future applications in flood simulation and forecasting.
文摘Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable parameters(river flow,rainfall,salinity)as proxies to determine when to close farms.We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall.Time-series event prediction consists of two steps:(i)feature extraction,and(ii)prediction.A number of data mining challenges exist for these scenarios:(i)which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?,(ii)The farm closure events occur infrequently and this leads to a class imbalance problem;the question is what is the best way to deal with this problem?In this paper we have analysed and compared different combinations of balancing methods(under-sampling and over-sampling),feature extraction methods(cluster profile,curve fitting,Fourier Transform,Piecewise Aggregate Approximation,and Wavelet Transform)and learning algorithms(neural network,support vector machine,k-nearest neighbour,decision tree,and Bayesian Network)to predict closure events accurately considering the above data mining challenges.We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall,given the above data mining challenges.
文摘A key control point in gene expression is the initiation of protein translation, with a universal stress response being constituted by in- hibitory phosphoryiation of the eukaryotic initiation factor 2α （el F2oL）. In humans, four kinases sense diverse physiological stresses to regulate elF2α to control cell differentiation, adaptation, and survival. Here we develop a computational molecular model of elF2α and one of its kinases, the protein kinase R, to simulate the dynamics of their interaction. Predictions generated by coarse-grained dynamics simulations suggest a novel mode of action. Experimentation substantiates these predictions, identifying a previously unrecognized interface in the protein complex, which is constituted by dynamic residues in both elF2α and its kinases that are crucial to regulate protein translation. These findings call for a reinterpretation of the current mechanism of action of the el F2α kinases and demonstrate the value of conducting computational analysis to evaluate protein function.
