Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
4,802
result(s) for
"Ye, Fei"
Sort by:
Parallel population and parallel human : a cyber-physical social approach
\"This book is a result of an ambitious research agenda I made for myself almost 30 years ago after reading Karl Popper's The Open Society and its Enemies. To me, the open society should have no enemies, we must find a way to build the bridge between Popper's utopian social engineering and piecemeal social engineering, perhaps through the Cyber-enabled Social Movement Organizations and Operations (CeSMO), and that would be my research for the rest of my professional career. I had promised myself to write a book entitled The Open Society and its Friends, and even created a new name for my ambition, Bemonad, for Becoming and Being Gottfried Leibniz's Monad, which was redefined as the atom of intelligence for Popper's Artificial World in the sense of ancient Greek philosopher Democritus' atom for matters in the Physical World. Of course, I realized very soon that it is simply a dream and a mission impossible. However, this had dramatically changed my career path from intelligent control for robotic systems to a mixture of science, technology, engineering, and social studies for complex intelligent systems, or an interdisciplinary approach by today's term, starting from my technical report at NASA/UA Space Engineering Research Center (SERC) on Shadow Systems in 1994 and ending up with the creation of the Program for Advanced Research in Complex Systems (PARCS) at the University of Arizona, Tucson, Arizona in 1999.\"-- Provided by publisher.
Book
Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data
In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.
Journal Article
Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.
Journal Article
Both Boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease
COVID-19 was declared a pandemic on March 11 by WHO, due to its great threat to global public health. The coronavirus main protease (M
pro
, also called 3CLpro) is essential for processing and maturation of the viral polyprotein, therefore recognized as an attractive drug target. Here we show that a clinically approved anti-HCV drug, Boceprevir, and a pre-clinical inhibitor against feline infectious peritonitis (corona) virus (FIPV), GC376, both efficaciously inhibit SARS-CoV-2 in Vero cells by targeting M
pro
. Moreover, combined application of GC376 with Remdesivir, a nucleotide analogue that inhibits viral RNA dependent RNA polymerase (RdRp), results in sterilizing additive effect. Further structural analysis reveals binding of both inhibitors to the catalytically active side of SARS-CoV-2 protease M
pro
as main mechanism of inhibition. Our findings may provide critical information for the optimization and design of more potent inhibitors against the emerging SARS-CoV-2 virus.
Coronavirus main protease is essential for viral polyprotein processing and maturation. Here Fu et al. report efficient inhibition of SARS-CoV-2 replication using two inhibitors - Boceprevir and GC376 - targeting the active site of the main viral protease.
Journal Article
Weyl magnons in breathing pyrochlore antiferromagnets
Frustrated quantum magnets not only provide exotic ground states and unusual magnetic structures, but also support unconventional excitations in many cases. Using a physically relevant spin model for a breathing pyrochlore lattice, we discuss the presence of topological linear band crossings of magnons in antiferromagnets. These are the analogues of Weyl fermions in electronic systems, which we dub Weyl magnons. The bulk Weyl magnon implies the presence of chiral magnon surface states forming arcs at finite energy. We argue that such antiferromagnets present a unique example, in which Weyl points can be manipulated in situ in the laboratory by applied fields. We discuss their appearance specifically in the breathing pyrochlore lattice, and give some general discussion of conditions to find Weyl magnons, and how they may be probed experimentally. Our work may inspire a re-examination of the magnetic excitations in many magnetically ordered systems.
It was recently demonstrated that particular materials with non-trivial electronic band structure support quasiparticle excitations described by the relativistic Weyl equation. Here, the authors explore how an analogous magnonic band structure may exist in breathing pyrochlore antiferromagnets.
Journal Article
Structural basis of ketamine action on human NMDA receptors
Ketamine is a non-competitive channel blocker of
N
-methyl-
d
-aspartate (NMDA) receptors
1
. A single sub-anaesthetic dose of ketamine produces rapid (within hours) and long-lasting antidepressant effects in patients who are resistant to other antidepressants
2
,
3
. Ketamine is a racemic mixture containing equal parts of (
R
)- and (
S
)-ketamine, with the (
S
)-enantiomer having greater affinity for the NMDA receptor
4
. Here we describe the cryo-electron microscope structures of human GluN1–GluN2A and GluN1–GluN2B NMDA receptors in complex with
S
-ketamine, glycine and glutamate. Both electron density maps uncovered the binding pocket for
S
-ketamine in the central vestibule between the channel gate and selectivity filter. Molecular dynamics simulation showed that
S
-ketamine moves between two distinct locations within the binding pocket. Two amino acids—leucine 642 on GluN2A (homologous to leucine 643 on GluN2B) and asparagine 616 on GluN1—were identified as key residues that form hydrophobic and hydrogen-bond interactions with ketamine, and mutations at these residues reduced the potency of ketamine in blocking NMDA receptor channel activity. These findings show structurally how ketamine binds to and acts on human NMDA receptors, and pave the way for the future development of ketamine-based antidepressants.
Structures of ketamine bound to human NMDA receptors show how ketamine inhibits receptor activity.
Journal Article
Morphogenesis and cytopathic effect of SARS-CoV-2 infection in human airway epithelial cells
SARS-CoV-2, a β-coronavirus, has rapidly spread across the world, highlighting its high transmissibility, but the underlying morphogenesis and pathogenesis remain poorly understood. Here, we characterize the replication dynamics, cell tropism and morphogenesis of SARS-CoV-2 in organotypic human airway epithelial (HAE) cultures. SARS-CoV-2 replicates efficiently and infects both ciliated and secretory cells in HAE cultures. In comparison, HCoV-NL63 replicates to lower titers and is only detected in ciliated cells. SARS-CoV-2 shows a similar morphogenetic process as other coronaviruses but causes plaque-like cytopathic effects in HAE cultures. Cell fusion, apoptosis, destruction of epithelium integrity, cilium shrinking and beaded changes are observed in the plaque regions. Taken together, our results provide important insights into SARS-CoV-2 cell tropism, replication and morphogenesis.
Here, the authors characterize replication of SARS-CoV-2 in human airway epithelial (HAE) cultures and show that it can infect ciliated and secretory cells, affects transepithelial electrical resistance and causes plaque-like cytopathic effects associated with apoptosis.
Journal Article
Structure of human steroid 5α-reductase 2 with the anti-androgen drug finasteride
Human steroid 5α-reductase 2 (SRD5A2) is an integral membrane enzyme in steroid metabolism and catalyzes the reduction of testosterone to dihydrotestosterone. Mutations in the
SRD5A2
gene have been linked to 5α-reductase deficiency and prostate cancer. Finasteride and dutasteride, as SRD5A2 inhibitors, are widely used antiandrogen drugs for benign prostate hyperplasia. The molecular mechanisms underlying enzyme catalysis and inhibition for SRD5A2 and other eukaryotic integral membrane steroid reductases remain elusive due to a lack of structural information. Here, we report a crystal structure of human SRD5A2 at 2.8 Å, revealing a unique 7-TM structural topology and an intermediate adduct of finasteride and NADPH as NADP-dihydrofinasteride in a largely enclosed binding cavity inside the transmembrane domain. Structural analysis together with computational and mutagenesis studies reveal the molecular mechanisms of the catalyzed reaction and of finasteride inhibition involving residues E57 and Y91. Molecular dynamics simulation results indicate high conformational dynamics of the cytosolic region that regulate NADPH/NADP
+
exchange. Mapping disease-causing mutations of SRD5A2 to our structure suggests molecular mechanisms for their pathological effects. Our results offer critical structural insights into the function of integral membrane steroid reductases and may facilitate drug development.
Human steroid 5α-reductase 2 (SRD5A2) is an integral membrane enzyme and catalyzes 5α-reduction of testosterone to dihydrotestosterone. Structural analysis accompanied by computational and mutagenesis studies reveal the mechanisms of catalysis and inhibition by clinically relevant drugs targeting SRD5A2.
Journal Article
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.
Methods to stratify patients according to mortality risk are essential to allocate limited heath resources during the COVID-19 crisis. Here, using machine learning methods, the authors present a mortality risk prediction model for COVID-19 that uses patients’ clinical data on admission to stratify patients by mortality risk.
Journal Article
Bile acid and receptors: biology and drug discovery for nonalcoholic fatty liver disease
Nonalcoholic fatty liver disease (NAFLD), a series of liver metabolic disorders manifested by lipid accumulation within hepatocytes, has become the primary cause of chronic liver diseases worldwide. About 20%–30% of NAFLD patients advance to nonalcoholic steatohepatitis (NASH), along with cell death, inflammation response and fibrogenesis. The pathogenesis of NASH is complex and its development is strongly related to multiple metabolic disorders (e.g. obesity, type 2 diabetes and cardiovascular diseases). The clinical outcomes include liver failure and hepatocellular cancer. There is no FDA-approved NASH drug so far, and thus effective therapeutics are urgently needed. Bile acids are synthesized in hepatocytes, transported into the intestine, metabolized by gut bacteria and recirculated back to the liver by the enterohepatic system. They exert pleiotropic roles in the absorption of fats and regulation of metabolism. Studies on the relevance of bile acid disturbance with NASH render it as an etiological factor in NASH pathogenesis. Recent findings on the functional identification of bile acid receptors have led to a further understanding of the pathophysiology of NASH such as metabolic dysregulation and inflammation, and bile acid receptors are recognized as attractive targets for NASH treatment. In this review, we summarize the current knowledge on the role of bile acids and the receptors in the development of NAFLD and NASH, especially the functions of farnesoid X receptor (FXR) in different tissues including liver and intestine. The progress in the development of bile acid and its receptors-based drugs for the treatment of NASH including bile acid analogs and non-bile acid modulators on bile acid metabolism is also discussed.
Journal Article