Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

Real-Time Monitoring of Cell Apoptosis and Drug Screening Using Fluorescent Light-Up Probe with Aggregation-Induced Emission Characteristics

Abstract: Real-time monitoring of cell apoptosis could provide valuable insights into early detection of therapy efficiency and evaluation of disease progression. In this work, we designed and synthesized a new live-cell-permeable, fluorescent light-up probe for real-time cell apoptosis imaging. The probe is comprised of a hydrophilic caspase-specific Asp-Glu-Val-Asp (DEVD) peptide and a hydrophobic tetraphenylethene (TPE) unit, a typical fluorogen with aggregation-induced emission characteristics. In aqueous solution, the probe is almost nonfluorescent but displays significant fluorescence enhancement in response to caspase-3/-7, which are activated in the apoptotic process and able to cleave the DEVD moieties. This fluorescence “turn-on” response is ascribed to aggregation of cleaved hydrophobic TPE residues, which restricts the intramolecular rotations of TPE phenyl rings and populates the radiative decay channels. The light-up nature of the probe allows real-time monitoring of caspase-3/-7 activities both in so...

Amyloid β Protein and Alzheimer’s Disease: When Computer Simulations Complement Experimental Studies

Abstract: Simulations Complement Experimental Studies Jessica Nasica-Labouze,† Phuong H. Nguyen,† Fabio Sterpone,† Olivia Berthoumieu,‡ Nicolae-Viorel Buchete, Sebastien Cote, Alfonso De Simone, Andrew J. Doig, Peter Faller,‡ Angel Garcia, Alessandro Laio, Mai Suan Li, Simone Melchionna, Normand Mousseau, Yuguang Mu, Anant Paravastu, Samuela Pasquali,† David J. Rosenman, Birgit Strodel, Bogdan Tarus,† John H. Viles, Tong Zhang,†,▲ Chunyu Wang, and Philippe Derreumaux*,†,□ †Laboratoire de Biochimie Theorique, Institut de Biologie Physico-Chimique (IBPC), UPR9080 CNRS, Universite Paris Diderot, Sorbonne Paris Cite, 13 rue Pierre et Marie Curie, 75005 Paris, France ‡LCC (Laboratoire de Chimie de Coordination), CNRS, Universite de Toulouse, Universite Paul Sabatier (UPS), Institut National Polytechnique de Toulouse (INPT), 205 route de Narbonne, BP 44099, Toulouse F-31077 Cedex 4, France School of Physics & Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland Deṕartement de Physique and Groupe de recherche sur les proteines membranaires (GEPROM), Universite de Montreal, C.P. 6128, succursale Centre-ville, Montreal, Quebec H3C 3T5, Canada Department of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom Department of Physics, Applied Physics, & Astronomy, and Department of Biology, Rensselaer Polytechnic Institute, Troy, New York 12180, United States The International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland Institute for Computational Science and Technology, SBI Building, Quang Trung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City, Vietnam Instituto Processi Chimico-Fisici, CNR-IPCF, Consiglio Nazionale delle Ricerche, 00185 Roma, Italy School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551 Singapore Department of Chemical and Biomedical Engineering, Florida A&M University-Florida State University (FAMU-FSU) College of Engineering, 2525 Pottsdamer Street, Tallahassee, Florida 32310, United States National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, Florida 32310, United States Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Julich GmbH, 52425 Julich, Germany School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom Institut Universitaire de France, 75005 Paris, France

Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

Abstract: It is well known that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons (including biases) can learn N distinct samples (x/sub i/,t/sub i/) with zero error, and the weights connecting the input neurons and the hidden neurons can be chosen "almost" arbitrarily. However, these results have been obtained for the case when the activation function for the hidden neurons is the signum function. This paper rigorously proves that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons and with any bounded nonlinear activation function which has a limit at one infinity can learn N distinct samples (x/sub i/,t/sub i/) with zero error. The previous method of arbitrarily choosing weights is not feasible for any SLFN. The proof of our result is constructive and thus gives a method to directly find the weights of the standard SLFNs with any such bounded nonlinear activation function as opposed to iterative training algorithms in the literature.

Canny edge detection enhancement by scale multiplication

Abstract: The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.

Macroporous and Monolithic Anode Based on Polyaniline Hybridized Three-Dimensional Graphene for High-Performance Microbial Fuel Cells

Abstract: Microbial fuel cell (MFC) is of great interest as a promising green energy source to harvest electricity from various organic matters. However, low bacterial loading capacity and low extracellular electron transfer efficiency between the bacteria and the anode often limit the practical applications of MFC. In this work, a macroporous and monolithic MFC anode based on polyaniline hybridized three-dimensional (3D) graphene is demonstrated. It outperforms the planar carbon electrode because of its abilities to three-dimensionally interface with bacterial biofilm, facilitate electron transfer, and provide multiplexed and highly conductive pathways. This study adds a new dimension to the MFC anode design as well as to the emerging graphene applications.