Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to ...Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.展开更多
Intrinsically disordered or unstructured proteins(or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficienc...Intrinsically disordered or unstructured proteins(or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory(LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone,but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features(MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.展开更多
基金supported by the National Natural Science Foundation of China (81571025 and 81702461)the National Key Research and Development Program of China (2018YFC0116400)+6 种基金the International Cooperation Project from Shanghai Science Foundation (18410711300)Shanghai Science and Technology Development Funds (16JC1420100)the Shanghai Sailing Program (17YF1426600)STCSM (19QC1400600, 17411953300)the Shanghai Pujiang Program (19PJ1406800)the Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJlabthe Interdisciplinary Program of Shanghai Jiao Tong University。
文摘Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.
基金supported by Australian Research Council (Grant No. DP180102060) to YZ and KPin part by the National Health and Medical Research Council (Grant No. 1121629) of Australia to YZ+1 种基金the High Performance Computing Cluster ‘Gowonda’ to complete this studythe aid of the research cloud resources provided by the Queensland Cyber Infrastructure Foundation (QCIF), Australia.
文摘Intrinsically disordered or unstructured proteins(or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory(LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone,but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features(MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.
