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"Artificial neural network"

Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell...
June 13, 2018: Analytical Biochemistry
Dhagash Mehta, Xiaojun Zhao, Edgar A Bernal, David J Wales
Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases...
May 2018: Physical Review. E
Janis Timoshenko, Andris Anspoks, Arturs Cintins, Alexei Kuzmin, Juris Purans, Anatoly I Frenkel
The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra...
June 1, 2018: Physical Review Letters
Matic Ivančič, Peter Naglič, Franjo Pernuš, Boštjan Likar, Miran Bürmen
Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient...
June 15, 2018: Optics Letters
Anjali Singhal, Madan Kumar, Mallika Bhattacharya, Neeta Kumari, Pawan Kumar Jha, Devendra Kumar Chauhan, Indu Shekhar Thakur
In this study, Leucaena leucocephala wood was pretreated with aqueous glycerol having H2 SO4 as the catalyst. Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the process parameters, catalyst concentration (1-3%), duration (120-300 min) and temperature (100-150 °C). ANN gave more accurate predictions for total reducing sugar yield than RSM. ANN also had lower values for error functions. Severity index (SI) was calculated based on the temperature, duration and catalyst concentration...
June 5, 2018: Bioresource Technology
Mohammad Zounemat-Kermani, Abdollah Ramezani-Charmahineh, Jan Adamowski, Ozgur Kisi
Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e...
June 13, 2018: Environmental Monitoring and Assessment
Stewart G Martin, Marie N Lebot, Bhudsaban Sukkarn, Graham Ball, Andrew R Green, Emad A Rakha, Ian O Ellis, Sarah J Storr
G protein-coupled oestrogen receptor 1 (GPER), also called G protein-coupled receptor 30 (GPR30), is attracting considerable attention for its potential role in breast cancer development and progression. Activation by oestrogen (17β-oestradiol; E2) initiates short term, non-genomic, signalling events both in vitro and in vivo . Published literature on the prognostic value of GPER protein expression in breast cancer indicates that further assessment is warranted. We show, using immunohistochemistry on a large cohort of primary invasive breast cancer patients (n=1245), that low protein expression of GPER is not only significantly associated with clinicopathological and molecular features of aggressive behaviour but also significantly associated with adverse survival of breast cancer patients...
May 25, 2018: Oncotarget
Jayanta Datta, Hsin-Piao Lin
Non-orthogonal multiple access (NOMA) systems are being considered as candidates for 5G wireless systems due to their promise of improved spectral efficiency. NOMA schemes are being combined with popular multicarrier schemes such as orthogonal frequency division multiplexing (OFDM) to take advantage of the benefits of multicarrier signals. A variant of the power domain NOMA is Layer Division Multiplexing (LDM). The most commonly deployed power domain LDM scheme involves successive interference cancellation (SIC) based decoding at the receiver...
June 13, 2018: Sensors
Saeedeh Taghadomi-Saberi, Sílvia Mas Garcia, Amin Allah Masoumi, Morteza Sadeghi, Santiago Marco
The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography⁻mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling...
June 13, 2018: Sensors
Lorena Simon-Vidal, Oihane García-Calvo, Uxue Oteo, Sonia Arrasate, Esther Lete, Nuria Sotomayor, Humbert González-Díaz
Machine Learning (ML) algorithms are gaining importance in the processing of chemical information and modelling of chemical reactivity problems. In this work, we have developed a PTML model combining Perturbation-Theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc...
June 13, 2018: Journal of Chemical Information and Modeling
Jonatas Batista Mattos, Manoel Jerônimo Moreira Cruz, Francisco Carlos Fernandes De Paula, Elinaldo Fonseca Sales
The objective of this study was to analyze spatial-seasonal changes to identify the natural and anthropic processes that control groundwater hydrogeochemistry in urban aquifers in municipality of Lençóis (Bahia). Tourism is the main activity of this municipality, which is an important tourist destination in northeastern Brazil and which maintains its tourism infrastructure by using groundwater. Two field campaigns were conducted (dry and rainy seasons) in order to collect groundwater samples extracted from 15 tubular wells distributed over the urban area of the municipality...
June 12, 2018: Environmental Monitoring and Assessment
Pavel Vassiliev, Igor Iezhitsa, Renu Agarwal, Adrian Julian Marcus, Alexander Spasov, Olga Zhukovskaya, Vera Anisimova
This article contains data that relate to the study carried out in the work of Marcus et al. (2018) [1]. Data represent an information about pharmacophore analysis of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives and results of construction of the relationship between intraocular pressure (IOP) lowering activity and hypotensive activity of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives using a multilayer perceptron artificial neural network. In particular, they include the ones listed in this article: 1) table of all pharmacophores of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives that showed IOP lowering activity; 2) table of all pharmacophores of the compounds that showed absence of IOP lowering activity; 3) table of initial data for artificial neural network analysis of relationship between IOP activity and hypotensive activity of this chemical series; 4) graphical representation of the best neural network model of this dependence; 5) original txt-file of results of pharmacophore analysis; 6) xls-file of initial data for neural network modeling; 7) original stw-file of results of neural network modeling; 8) original xml-file of the best neural network model of dependence between IOP lowering activity and hypotensive activity of these azole derivatives...
June 2018: Data in Brief
Boštjan Murovec, Damjan Makuc, Sabina Kolbl Repinc, Zala Prevoršek, Domen Zavec, Robert Šket, Klemen Pečnik, Janez Plavec, Blaž Stres
In this study, nuclear magnetic resonance (1 H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction...
June 9, 2018: Journal of Environmental Management
Youngmin Seo, Sungwon Kim, Vijay P Singh
This paper proposes hybrid soft computing models for daily river stage modeling. The models combine variational mode decomposition (VMD) with different soft computing models, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The performances of VMD-based models (VMD-ANN, VMD-ANFIS, and VMD-RF) are assessed by model efficiency indices and graphical comparison, and compared with those of single models (ANN, ANFIS, and RF) and ensemble empirical mode decomposition (EEMD)-based models (EEMD-ANN, EEMD-ANFIS, and EEMD-RF)...
June 12, 2018: Environmental Monitoring and Assessment
Senlin Zhu, Emmanuel Karlo Nyarko, Marijana Hadzima-Nyarko
The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air-water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT))...
2018: PeerJ
F Lia, A Morote Castellano, M Zammit-Mangion, C Farrugia
Fluorescence spectrometry, combined with principle component analysis, partial least-squares regression (PLSR) and artificial neural network (ANN), was applied for the analysis of Maltese extra virgin olive oil (EVOO) adulterated by blending with vegetable oil (corn oil, soybean oil, linseed oil, or sunflower oil). The novel results showed that adjusted PLSR models based on synchronised spectra for detecting the % amount of EVOO in vegetable oil blends had a lower root mean square error (0.02-6.27%) and higher R2 (0...
June 2018: Journal of Food Science and Technology
Li Yu, Weifeng Jin, Xiaohong Li, Yuyan Zhang
The ultraviolet spectrophotometric method is often used for determining the content of glycyrrhizic acid from Chinese herbal medicine Glycyrrhiza glabra . Based on the traditional single variable approach, four extraction parameters of ammonia concentration, ethanol concentration, circumfluence time, and liquid-solid ratio are adopted as the independent extraction variables. In the present work, central composite design of four factors and five levels is applied to design the extraction experiments. Subsequently, the prediction models of response surface methodology, artificial neural networks, and genetic algorithm-artificial neural networks are developed to analyze the obtained experimental data, while the genetic algorithm is utilized to find the optimal extraction parameters for the above well-established models...
2018: Evidence-based Complementary and Alternative Medicine: ECAM
Alex H Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I Ryu, Krishna V Shenoy, Mark Schnitzer, Tamara G Kolda, Surya Ganguli
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state...
May 31, 2018: Neuron
P Barmpalexis, A Karagianni, I Nikolakakis, K Kachrimanis
The present work describes the development of an efficient, fast and accurate method for the quantification of polymer-based cocrystal formulations. Specifically, the content of carbamazepine-nicotinamide (CBZ/NIC) and ibuprofen-nicotinamide (IBU/NIC) cocrystals in Soluplus®-based formulations was independently determined with the aid of either Raman or Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR) spectroscopy. Spectra peaks from mixtures of IBU/NIC and CBZ/NIC cocrystals with Soluplus at a ratio ranging from 90/10 to 1/99 w/w (cocrystal to SOL) were evaluated and modelled with the aid of feed-forward, back-propagation artificial neural networks (ANNs) and partial least squares (PLS) regression analysis...
June 4, 2018: Journal of Pharmaceutical and Biomedical Analysis
Aleksandra Vuckovic, Vicente Jose Ferrer Gallardo, Mohammed Jarjees, Mathew Fraser, Mariel Purcell
OBJECTIVES: To create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing central neuropathic pain (CNP) by comparing them with patients who had already developed pain and with able bodied controls. METHODS: Multichannel EEG was recorded in the relaxed eyes opened and eyes closed states in 10 able bodied participants and 31 subacute SCI participants (11 with CNP, 10 without NP and 10 who later developed pain within 6 months of the EEG recording)...
May 23, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
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