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"Artificial neural network"

Jean-Michel Garant, Jean-Pierre Perreault, Michelle S Scott
Motivation: G-quadruplex structures in RNA molecules are known to have regulatory impacts in cells but are difficult to locate in the genome. The minimal requirements for G-quadruplex folding in RNA (G ≥3 N 1-7  G ≥3 N 1-7  G ≥3 N 1-7  G ≥3 ) is being challenged by observations made on specific examples in recent years. The definition of potential G-quadruplex sequences has major repercussions on the observation of the structure since it introduces a bias. The canonical motif only describes a sub-population of the reported G-quadruplexes...
August 3, 2017: Bioinformatics
Ehsan Maleki, Hossein Babashah, Somayyeh Koohi, Zahra Kavehvash
This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool...
July 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Dong Wang, Alistair G Borthwick, Handan He, Yuankun Wang, Jieyu Zhu, Yuan Lu, Pengcheng Xu, Xiankui Zeng, Jichun Wu, Lachun Wang, Xinqing Zou, Jiufu Liu, Ying Zou, Ruimin He
Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach...
October 12, 2017: Environmental Research
Haifeng Xing, Bo Hou, Zhihui Lin, Meifeng Guo
MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization)...
October 13, 2017: Sensors
F Resquín, J Gonzalez-Vargas, J Ibáñez, F Brunetti, I Dimbwadyo, L Carrasco, S Alves, C Gonzalez-Alted, A Gomez-Blanco, J L Pons
BACKGROUND: Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function...
October 12, 2017: Journal of Neuroengineering and Rehabilitation
J A Castillo-Garit, G M Casañola-Martin, S J Barigye, H Pham-The, F Torrens, A Torreblanca
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported...
September 2017: SAR and QSAR in Environmental Research
Michael P Pound, Jonathan A Atkinson, Alexandra J Townsend, Michael H Wilson, Marcus Griffiths, Aaron S Jackson, Adrian Bulat, Georgios Tzimiropoulos, Darren M Wells, Erik H Murchie, Tony P Pridmore, Andrew P French
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power...
October 1, 2017: GigaScience
Alex D M Dewar, Antoine Wystrach, Andrew Philippides, Paul Graham
All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation...
October 10, 2017: PLoS Computational Biology
Jun S Kim, Robert K Merrill, Varun Arvind, Deepak Kaji, Sara D Pasik, Chuma C Nwachukwu, Luilly Vargas, Nebiyu S Osman, Eric K Oermann, John M Caridi, Samuel K Cho
STUDY DESIGN: Cross-sectional database study. OBJECTIVE: To train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion. SUMMARY OF BACKGROUND DATA: Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion...
October 9, 2017: Spine
Warren G Campbell, Moyed Miften, Lindsey Olsen, Priscilla Stumpf, Tracey Schefter, Karyn A Goodman, Bernard L Jones
PURPOSE: Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge-based artificial neural network dose models (ANN-DMs) to predict dose distributions that would be approved by experienced physicians. METHODS: Arc-based SBRT treatment plans for 43 pancreatic cancer patients were planned, delivering 30-33 Gy in five fractions...
October 10, 2017: Medical Physics
Mohamad Javad Alizadeh, Ehsan Jafari Nodoushan, Naghi Kalarestaghi, Kwok Wing Chau
This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures...
October 9, 2017: Environmental Science and Pollution Research International
Yung-Chia Chiu, Chih-Wei Chiang, Tsung-Yu Lee
The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is chosen as the case study area. The appropriate input variables used to develop the ANFIS models are determined based on the t-test. The results obtained by ANFIS are compared with those by multiple linear regression (MLR) and artificial neural networks (ANNs). Simulated results show that the identified ANFIS model is superior to the traditional MLR and nonlinear ANNs models in terms of the performance evaluated by the Pearson coefficient of correlation, the root mean square error, the mean absolute percentage, and the mean absolute error...
October 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Uchenna Oparaji, Rong-Jiun Sheu, Mark Bankhead, Jonathan Austin, Edoardo Patelli
Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN...
September 18, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Strahinja Kovačević, Milica Karadžić, Sanja Podunavac-Kuzmanović, Lidija Jevrić
The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP(C)) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity...
October 4, 2017: European Journal of Pharmaceutical Sciences
Shiju Yan, Yunzhi Wang, Faranak Aghaei, Yuchen Qiu, Bin Zheng
RATIONALE AND OBJECTIVES: The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. MATERIALS AND METHODS: The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images...
October 3, 2017: Academic Radiology
Panagiotis Barmpalexis, Agni Grypioti, Georgios K Eleftheriadis, Dimitris G Fatouros
In the present study, liquisolid formulations were developed for improving dissolution profile of aprepitant (APT) in a solid dosage form. Experimental studies were complemented with artificial neural networks and genetic programming. Specifically, the type and concentration of liquid vehicle was evaluated through saturation-solubility studies, while the effect of the amount of viscosity increasing agent (HPMC), the type of wetting (Soluplus® vs. PVP) and solubilizing (Poloxamer®407 vs. Kolliphor®ELP) agents, and the ratio of solid coating (microcrystalline cellulose) to carrier (colloidal silicon dioxide) were evaluated based on in vitro drug release studies...
October 4, 2017: AAPS PharmSciTech
Hamid Reza Akbari Hasanjani, Mahmoud Reza Sohrabi
Simultaneous spectrophotometric estimation of Fluoxetine and Sertraline in tablets were performed using UV-Vis spectroscopic and Artificial Neural Networks (ANN). Absorption spectra of two components were recorded in 200-300 nm wavelengths region with an interval of 1 nm. The calibration models were thoroughly evaluated at several concentration levels using the spectra of synthetic binary mixture (prepared using orthogonal design). Three layers feed-forward neural networks using the back-propagation algorithm (B...
2017: Iranian Journal of Pharmaceutical Research: IJPR
Paul D Myers, Benjamin M Scirica, Collin M Stultz
The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as Artificial Neural Networks (ANN), form an attractive platform to build risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance. Using two cohorts consisting of patients admitted with a non-ST-segment elevation acute coronary syndrome, we constructed an ANN that identifies patients at high risk of cardiovascular death (CVD)...
October 4, 2017: Scientific Reports
E G Silva Júnior, D B O Cardoso, M C Reis, A F O Nascimento, D I Bortolin, M R Martins, L B Sousa
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes...
September 27, 2017: Genetics and Molecular Research: GMR
Verena Schöning, Felix Hammann, Mark Peinl, Jürgen Drewe
Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant families and form a powerful defence mechanism against herbivores. More than 600 different PAs are known. PAs are ester alkaloids composed of a necine base and a necic acid, which can be used to divide PAs in different structural subcategories. The main target organs for PA metabolism and toxicity are liver and lungs. Additionally, PAs are potentially genotoxic, carcinogenic and exhibit developmental toxicity. Only for very few PAs, in vitro and in vivo investigations have characterised their toxic potential...
September 9, 2017: Toxicological Sciences: An Official Journal of the Society of Toxicology
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