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Article ; Online: Torsional vibration analysis of shaft with multi inertias

Tao Peng / Qun Yan

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 19

Abstract: Abstract An analytical method is proposed to investigate the torsional vibration of the uniform circular shaft with multiple concentrated inertias. The governing equation is established based on the Hamiltonian principle and verified by the dynamical ... ...

Abstract	Abstract An analytical method is proposed to investigate the torsional vibration of the uniform circular shaft with multiple concentrated inertias. The governing equation is established based on the Hamiltonian principle and verified by the dynamical method. The theoretical solutions of frequencies and mode shapes under different boundary conditions are obtained using the separation variable method and integral transformation. The effectiveness of the proposed method is verified by comparison with existing literature. Considering the change of the magnitudes/positions/number of concentrated inertias, and different boundary conditions, the natural frequencies and mode shapes are discussed. Several general rules are obtained. Moreover, some interesting phenomena have been found and explained. The analytical method has applications in the design of shafting with multiple concentrated inertias and the reliability checking of the “approximate” solutions.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Brain-inspired chaotic spiking backpropagation.

Wang, Zijian / Tao, Peng / Chen, Luonan

National science review

2024 Volume 11, Issue 6, Page(s) nwae037

Abstract: Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable ... ...

Abstract	Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.
Language	English
Publishing date	2024-01-30
Publishing country	China
Document type	Journal Article
ZDB-ID	2745465-4
ISSN	2053-714X ; 2053-714X
ISSN (online)	2053-714X
ISSN	2053-714X
DOI	10.1093/nsr/nwae037
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Article ; Online: Role of cotranslational folding for β-sheet-enriched proteins: A perspective from molecular dynamics simulations.

Tao, Peng / Xiao, Yi

Physical review. E

2022 Volume 105, Issue 2-1, Page(s) 24402

Abstract: The formations of correct three-dimensional structures of proteins are essential to their functions. Cotranslational folding is vital for proteins to form correct structures in vivo. Although some experiments have shown that cotranslational folding can ... ...

Abstract	The formations of correct three-dimensional structures of proteins are essential to their functions. Cotranslational folding is vital for proteins to form correct structures in vivo. Although some experiments have shown that cotranslational folding can improve the efficiency of folding, its microscopic mechanism is not yet clear. Previously, we built a model of the ribosomal exit tunnel and investigated the cotranslational folding of a three-helix protein by using all-atom molecular dynamics simulations. Here we study the cotranslational folding of three β-sheet-enriched proteins using the same method. The results show that cotranslational folding can enhance the helical population in most cases and reduce non-native long-range contacts before emerging from the ribosomal exit tunnel. After exiting the tunnel, all proteins fall into local minimal states and the structural ensembles of cotranslational folding show more helical conformations than those of free folding. In particular, for one of the three proteins, the GTT WW domain, we find that one local minimum state of the cotranslational folding is the known folding intermediate, which is not found in free folding. This result suggests that the cotranslational folding may increase the folding efficiency by accelerating the sampling more than by avoiding the misfolded state, which is presently a mainstream viewpoint.
MeSH term(s)	Molecular Dynamics Simulation ; Protein Conformation, beta-Strand ; Protein Folding ; Proteins/metabolism ; Ribosomes/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2022-03-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2844562-4
ISSN	2470-0053 ; 2470-0045
ISSN (online)	2470-0053
ISSN	2470-0045
DOI	10.1103/PhysRevE.105.024402
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Article ; Online: Computational investigation of peptidomimetics as potential inhibitors of SARS-CoV-2 spike protein.

Tarek Ibrahim, Mayar / Tao, Peng

Journal of biomolecular structure & dynamics

2022 Volume 41, Issue 15, Page(s) 7144–7157

Abstract: Several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were observed since the outbreak of the global pandemic at the end of 2019. The trimeric spike glycoprotein of the SARS-CoV-2 virus is crucial for the viral access to ... ...

Abstract	Several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were observed since the outbreak of the global pandemic at the end of 2019. The trimeric spike glycoprotein of the SARS-CoV-2 virus is crucial for the viral access to the host cell by interacting with the human angiotensin converting enzyme 2 (ACE2). Most of the mutations take place in the receptor-binding domain (RBD) of the S1 subunit of the trimeric spike glycoprotein. In this work, we targeted both S1 and S2 subunits of the spike protein in the wild type (WT) and the Omicron variant guided by the interaction of the neutralizing monoclonal antibodies. Virtual screening of two different peptidomimetics databases, ChEMBL and ChemDiv databases, was carried out against both S1 and S2 subunits. The use of these two databases provided diversity and enhanced the chance of finding protein-protein interaction inhibitors (PPIIs). Multi-layered filtration, based on physicochemical properties and docking scores, of nearly 114,000 compounds found in the ChEMBL database and nearly 14,000 compounds in the ChemDiv database was employed. Four peptidomimetics compounds were effective against both the WT and the Omicron S1 subunit with the minimum binding free energy of -25 kcal/mol. Five peptidomimetics compounds were effective against the S2 subunit with the minimum binding free energy of -19 kcal/mol. The dynamical cross-correlation matrix insinuated that the mutations of the RBD in the Omicron variant of the SARS-CoV-2 virus altered the correlated conformational motion of the different regions of the protein.Communicated by Ramaswamy H. Sarma.
Language	English
Publishing date	2022-08-29
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2022.2116601
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Article ; Online: Evaluation of cell surface vimentin positive circulating tumor cells as a prognostic biomarker for stage III/IV colorectal cancer

Yuepeng Cao / Mian Yang / Tao Peng / Yelei Liu / Jiazi Yu

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract Currently, little is known about the phenotypes of circulating tumor cells (CTCs), particularly epithelial and mesenchymal phenotypes, and their impact on the prognosis of colorectal cancer (CRC) patients. This study aims to investigate the CTC ... ...

Abstract	Abstract Currently, little is known about the phenotypes of circulating tumor cells (CTCs), particularly epithelial and mesenchymal phenotypes, and their impact on the prognosis of colorectal cancer (CRC) patients. This study aims to investigate the CTC phenotypes and their prognostic implications in stage III/IV CRC. Patients who were diagnosed with CRC and underwent CTC detection at two hospitals were included. CTCs were detected using a mesenchymal CTC kit, and the clinical and pathological characteristics of CTCs were compared with those of cell surface vimentin-positive CTCs (CSV-CTCs). Disease-free survival (DFS) was assessed and used as an indicator of CTC phenotype-related prognosis. Univariate and multivariate Cox regression analyses were made to identify risk factors, and nomogram models were employed for prognostic prediction. A total of 82 patients were enrolled, with a CTC detection rate of 86.6%. Among the detected CTCs, 60% were CSV-CTCs. The CSV-CTC count showed a positive correlation with the T-stage, the M-stage, and the location of the primary tumor (P = 0.01, P = 0.014, and P = 0.01, respectively). Kaplan–Meier survival analysis revealed that CSV-CTCs were associated with worse DFS in patients receiving first-line oxaliplatin chemotherapy (hazard ratio (HR) = 3.78, 95% CI 1.55–9.26, p = 0.04). When the cut-off value of the CSV-CTC count was 3, the optimal prognostic prediction was achieved. Compound models considering CSV-CTCs, TNM staging, the site of the primary tumor and the Ras gene status yielded the best results in both the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA). This study indicates that CSV-CTCs predominate in CTCs of CRC patients, and a count of CSV-CTCs ≥ 3 is an independent risk factor for worse prognosis.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: HIV-associated splenic diffuse large B-cell lymphoma combined with hepatitis C and tuberculous meningitis:A case report

Tao Peng fei / Hui Ru Xi / Min Hai yan

Heliyon, Vol 9, Iss 9, Pp e20073- (2023)

2023

Abstract: Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive B-lymphocyte-derived malignant proliferative disease that is currently one of the leading causes of death in HIV patients. The incidence of lymphoma in HIV patients is 60–200 times higher than ... ...

Abstract	Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive B-lymphocyte-derived malignant proliferative disease that is currently one of the leading causes of death in HIV patients. The incidence of lymphoma in HIV patients is 60–200 times higher than in the general population compared to the non-HIV population, and diffuse large B-cell lymphoma can cause numerous disease manifestations, especially in severely immunocompromised individuals. We treated a case of HIV-associated splenic diffuse large B-cell lymphoma combined with hepatitis C and tuberculous meningitis. In this case, diffuse large B-cell lymphoma of the spleen was difficult to diagnose. Second, simultaneous treatment of multiple diseases requires consideration of drug interactions. Our case highlights the diagnostic value of early tissue biopsy and the importance of avoiding drug interactions during treatment, and the selection of appropriate CART, anti-hepatitis C, and anti-tuberculosis protocols to reduce mortality from diffuse large B-cell lymphoma comorbidification.
Keywords	HIV ; Diffuse large B-Cell lymphoma of the spleen ; Hepatitis C ; Tuberculous meningitis ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	610
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The role of robotic surgery in neurological cases

Tong Lin / Qinghai Xie / Tao Peng / Xianxiao Zhao / Dongliang Chen

Heliyon, Vol 9, Iss 12, Pp e22523- (2023)

A systematic review on brain and spine applications

2023

Abstract: The application of robotic surgery technologies in neurological surgeries resulted in some advantages compared to traditional surgeries, including higher accuracy and dexterity enhancement. Its success in various surgical fields, especially in urology, ... ...

Abstract	The application of robotic surgery technologies in neurological surgeries resulted in some advantages compared to traditional surgeries, including higher accuracy and dexterity enhancement. Its success in various surgical fields, especially in urology, cardiology, and gynecology surgeries was reported in previous studies, and similar advantages in neurological surgeries are expected. Surgeries in the central nervous system with the pathology of millimeters through small working channels around vital tissue need especially high precision. Applying robotic surgery is therefore an interesting dilemma for these situations. This article reviews various studies published on the application of brain and spine robotic surgery and discusses the current application of robotic technology in neurological cases.
Keywords	Robot surgery ; Brain ; Spine ; Neurological surgeries ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	629
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: PASSerRank: Prediction of Allosteric Sites with Learning to Rank.

Tian, Hao / Xiao, Sian / Jiang, Xi / Tao, Peng

ArXiv

2023

Abstract: Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many ... ...

Abstract	Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, i.e., how well a pocket meets the characteristics of known allosteric sites. The model outperforms other common machine learning models with higher F1 score and Matthews correlation coefficient. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top 3 positions for 83.6% and 80.5% of test proteins, respectively. The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.
Language	English
Publishing date	2023-04-29
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PASSer: fast and accurate prediction of protein allosteric sites.

Tian, Hao / Xiao, Sian / Jiang, Xi / Tao, Peng

Nucleic acids research

2023 Volume 51, Issue W1, Page(s) W427–W431

Abstract: Allostery refers to the biological process by which an effector modulator binds to a protein at a site distant from the active site, known as allosteric site. Identifying allosteric sites is essential for discovering allosteric process and is considered ... ...

Abstract	Allostery refers to the biological process by which an effector modulator binds to a protein at a site distant from the active site, known as allosteric site. Identifying allosteric sites is essential for discovering allosteric process and is considered a critical factor in allosteric drug development. To facilitate related research, we developed PASSer (Protein Allosteric Sites Server) at https://passer.smu.edu, a web application for fast and accurate allosteric site prediction and visualization. The website hosts three trained and published machine learning models: (i) an ensemble learning model with extreme gradient boosting and graph convolutional neural network, (ii) an automated machine learning model with AutoGluon and (iii) a learning-to-rank model with LambdaMART. PASSer accepts protein entries directly from the Protein Data Bank (PDB) or user-uploaded PDB files, and can conduct predictions within seconds. The results are presented in an interactive window that displays protein and pockets' structures, as well as a table that summarizes predictions of the top three pockets with the highest probabilities/scores. To date, PASSer has been visited over 49 000 times in over 70 countries and has executed over 6 200 jobs.
MeSH term(s)	Allosteric Site ; Proteins/chemistry ; Software ; Neural Networks, Computer ; Machine Learning ; Allosteric Regulation
Chemical Substances	Proteins
Language	English
Publishing date	2023-05-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad303
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Article ; Online: PASSerRank: Prediction of allosteric sites with learning to rank.

Tian, Hao / Xiao, Sian / Jiang, Xi / Tao, Peng

Journal of computational chemistry

2023 Volume 44, Issue 28, Page(s) 2223–2229

Abstract	Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.
MeSH term(s)	Humans ; Allosteric Site ; Proteins/metabolism ; Drug Discovery ; Machine Learning
Chemical Substances	Proteins
Language	English
Publishing date	2023-08-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1479181-X
ISSN	1096-987X ; 0192-8651
ISSN (online)	1096-987X
ISSN	0192-8651
DOI	10.1002/jcc.27193
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