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Yonghao Miao, Ming Zhao, Jing Lin
Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant effect of prior parameters, especially the predefined mode number and balancing parameter, which heavily trouble the traditional VMD. However, parameter-adaptive VMD still encounters some problems when it is applied to the data from industry applications. On one hand, the mode number chosen using parameter-adaptive VMD is not the optimal. Numbers of redundant modes are decomposed. On another hand, parameter-adaptive VMD has much space for the improvement when it is applied to compound-fault diagnosis...
October 12, 2018: ISA Transactions
J-M Hartmann, C Boulet, H Zhang, F Billard, O Faucher, B Lavorel
We present the first theoretical study of collisional dissipation of the alignment of a symmetric-top molecule (ethane gas) impulsively induced by a linearly polarized non-resonant laser field. For this, Classical Molecular Dynamics Simulations (CMDSs) are carried out for an ensemble of C2 H6 molecules based on knowledge of the laser-pulse characteristics and on an input intermolecular potential. These provide, for a given gas pressure and initial temperature, the orientations of all molecules at all times from which the alignment factor is directly obtained...
October 21, 2018: Journal of Chemical Physics
A D Tranter, H J Slatyer, M R Hush, A C Leung, J L Everett, K V Paul, P Vernaz-Gris, P K Lam, B C Buchler, G T Campbell
Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used...
October 19, 2018: Nature Communications
Marcelo E Guerin, Guillaume Stirnemann, David Giganti
An immense repertoire of protein chemical modifications catalyzed by enzymes is available as proteomics data. Quantifying the impact of the conformational dynamics of the modified peptide remains challenging to understand the decisive kinetics and amino acid sequence specificity of these enzymatic reactions in vivo, because the target peptide must be disordered to accommodate the specific enzyme-binding site. Here, we were able to control the conformation of a single-molecule peptide chain by applying mechanical force to activate and monitor its specific cleavage by a model protease...
October 19, 2018: Proceedings of the National Academy of Sciences of the United States of America
Kelsi Perttula, Courtney Schiffman, William M B Edmands, Lauren Petrick, Hasmik Grigoryan, Xiaoming Cai, Marc J Gunter, Alessio Naccarati, Silvia Polidoro, Sandrine Dudoit, Paolo Vineis, Stephen M Rappaport
BACKGROUND: Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. METHODS: Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy)...
October 19, 2018: BMC Cancer
Fan Li, Manhua Liu
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups...
October 2, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Chamari Wijesooriya, Charles Nyamekye, Emily A Smith
Biological membranes serve as the fundamental unit of life, allowing the compartmentalization of cellular contents into subunits with specific functions. The bilayer structure, consisting of lipids, proteins, small molecules and sugars, also serves many other complex functions in addition to maintaining the relative stability of the inner compartments. Signal transduction, regulation of solute exchange, active transport, and energy transduction through ion gradients all take place at biological membranes, primarily with the assistance of membrane proteins...
October 19, 2018: Analytical Chemistry
Zhiyuan Ma, Ping Wang, Zehui Gao, Ruobing Wang, Koroush Khalighi
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters...
2018: PloS One
Sahand Mahmoodian, Mantas Čepulkovskis, Sumanta Das, Peter Lodahl, Klemens Hammerer, Anders S Sørensen
We show that strongly correlated photon transport can be observed in waveguides containing optically dense ensembles of emitters. Remarkably, this occurs even for weak coupling efficiencies. Specifically, we compute the photon transport properties through a chirally coupled system of N two-level systems driven by a weak coherent field, where each emitter can also scatter photons out of the waveguide. The photon correlations arise due to an interplay of nonlinearity and coupling to a loss reservoir, which creates a strong effective interaction between transmitted photons...
October 5, 2018: Physical Review Letters
Mario Theers, Elmar Westphal, Kai Qi, Roland G Winkler, Gerhard Gompper
The spatiotemporal dynamics in systems of active self-propelled particles is controlled by the propulsion mechanism in combination with various direct interactions, such as steric repulsion and hydrodynamics. These direct interactions are typically anisotropic, and come in different "flavors", such as spherical and elongated particle shapes, pusher and puller flow fields, etc. The combination of the various aspects is expected to lead to new emergent behavior. However, it is a priori not evident whether shape and hydrodynamics act synergistically or antagonistically to generate motility-induced clustering (MIC) and phase separation (MIPS)...
October 19, 2018: Soft Matter
Adrian J Lindsay, Barak F Caracheo, Jamie J S Grewal, Daniel Leibovitz, Jeremy K Seamans
Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other types of task-relevant information has proven challenging. To determine the extent to which movement and location information is relevant to mPFC neurons, tetrodes were used to record neuronal activity while limb positions, poses (i.e., recurring constellations of limb positions), velocity, and spatial locations were simultaneously recorded with two cameras every 200 ms as rats freely roamed in an experimental enclosure...
March 2018: ENeuro
Dieu Tien Bui, Mahdi Panahi, Himan Shahabi, Vijay P Singh, Ataollah Shirzadi, Kamran Chapi, Khabat Khosravi, Wei Chen, Somayeh Panahi, Shaojun Li, Baharin Bin Ahmad
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling...
October 18, 2018: Scientific Reports
Hyunjun Eun, Daeyeong Kim, Chanho Jung, Changick Kim
BACKGROUND AND OBJECTIVE: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks...
October 2018: Computer Methods and Programs in Biomedicine
Aman Sharma, Rinkle Rani
BACKGROUND AND OBJECTIVE: Drug-target interaction prediction plays an intrinsic role in the drug discovery process. Prediction of novel drugs and targets helps in identifying optimal drug therapies for various stringent diseases. Computational prediction of drug-target interactions can help to identify potential drug-target pairs and speed-up the process of drug repositioning. In our present, work we have focused on machine learning algorithms for predicting drug-target interactions from the pool of existing drug-target data...
October 2018: Computer Methods and Programs in Biomedicine
Leonard Barreto Moreira, Anderson Amendoeira Namen
BACKGROUND AND OBJECTIVE: Given the phenomenon of aging population, dementias arise as a complex health problem throughout the world. Several methods of machine learning have been applied to the task of predicting dementias. Given its diagnostic complexity, the great challenge lies in distinguishing patients with some type of dementia from healthy people. Particularly in the early stages, the diagnosis positively impacts the quality of life of both the patient and the family. This work presents a hybrid data mining model, involving the mining of texts integrated to the mining of structured data...
October 2018: Computer Methods and Programs in Biomedicine
Kaiyuan Sun, Qian Zhang, Ana Pastore-Piontti, Matteo Chinazzi, Dina Mistry, Natalie E Dean, Diana Patricia Rojas, Stefano Merler, Piero Poletti, Luca Rossi, M Elizabeth Halloran, Ira M Longini, Alessandro Vespignani
BACKGROUND: Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties in the contiguous United States (US), prompting the issuance of travel, prevention, and testing guidance across the contiguous US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US. METHODS: We present a framework for the projection of ZIKV autochthonous transmission in the contiguous US during the 2015-2016 epidemic using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental, and detailed population data...
October 18, 2018: BMC Medicine
Alexander John Dear, Andela Saric, Thomas C T Michaels, Christopher M Dobson, Tuomas P J Knowles
The misfolding and aggregation of proteins into linear fibrils is widespread in human biology, for example in connection with amyloid formation and the pathology of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. The oligomeric species that are formed in the early stages of protein aggregation are of great interest, having been linked with the cellular toxicity associated with these conditions. However, these species are not characterized in any detail experimentally, and their properties are not well understood...
October 18, 2018: Journal of Physical Chemistry. B
Markus Pfeiffer, Boris V Senkovskiy, Danny Haberer, Felix R Fischer, Fan Yang, Klaus Meerholz, Yoichi Ando, Alexander Grüneis, Klas Lindfors
By enhancing the photoluminescence from aligned 7-atom wide armchair-edge graphene nanoribbons using plasmonic nanoantennas, we are able to observe blinking of the emission. The on- and off-times of the blinking follow power law statistics. In time-resolved spectra, we observe spectral diffusion. These findings together is a strong indication of the emission originating from a single quantum emitter. The room temperature photoluminescence displays a narrow spectral width of less than 50 meV, which is significantly smaller than the previously observed ensemble linewidth of 0...
October 18, 2018: Nano Letters
Isidro Cortés Ciriano, Andreas Bender
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques to estimate the reliability of the predictions generated with deep learning networks remain largely underexplored. Here, we present Deep Confidence, a framework to compute valid and efficient confidence intervals for individual predictions using the deep learning technique Snapshot Ensembling and conformal prediction...
October 17, 2018: Journal of Chemical Information and Modeling
Brooks V Udelsman, Pamela S Jones, Yanik J Bababekov, Bob S Carter, David C Chang
No abstract text is available yet for this article.
October 17, 2018: Neurosurgery
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