Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.

• MeSH
• Algorithms MeSH
• Data Interpretation, Statistical MeSH
• Mathematics * MeSH
• Nonlinear Dynamics MeSH
• Neural Networks, Computer * MeSH
• Weather MeSH
• Computer Simulation MeSH
• Machine Learning MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

There is an increasing interest in the use of hospital admission for Chronic obstructive pulmonary disease (COPD) in studies of short-term exposure effects attributed to air pollutants. However, little is known about the effect of air pollutants on COPD symptoms. This study was undertaken to determine whether there was an association between air pollutant levels and both hospital admissions and symptoms for COPD. For model comparison, we present Generalized Linear Model, Generalized Additive Model and a general approach for Bayesian inference via Markov chain Monte Carlo in generalized additive model. Furthermore, for comparing the predictive accuracy, Artificial Neural Networks (ANN) approach is given.

• MeSH
• Bayes Theorem MeSH
• Pulmonary Disease, Chronic Obstructive epidemiology   etiology   MeSH
• Hospitalization statistics & numerical data   MeSH
• Humans MeSH
• Linear Models MeSH
• Monte Carlo Method MeSH
• Neural Networks, Computer MeSH
• Poisson Distribution MeSH
• Predictive Value of Tests MeSH
• Air Pollution adverse effects   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

• MeSH
• Organizational Innovation * MeSH
• Decision Making MeSH
• Social Class MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Europe MeSH

• MeSH
• Eye Movements MeSH
• Psychomotor Disorders MeSH
• Saccades MeSH

• Conspectus
• Psychologie
• NML Fields
• oftalmologie
• psychologie, klinická psychologie
• NML Publication type
• kolektivní monografie

We studied sequence-dependent retention properties of synthetic 5'-terminal phosphate absent trinucleotides containing adenine, guanine and thymine through reversed-phase liquid chromatography (RPLC) and QSRR modelling. We investigated the influence of separation conditions, namely mobile phase composition (ion interaction agent content, pH and organic constituent content), on sequence-dependent separation by means of ion-interaction RPLC (II-RPLC) using two types of models: experimental design-artificial neural networks (ED-ANN), and linear regression based on molecular dynamics data. The aim was to determine those properties of the above-mentioned analytes responsible for the retention dependence of the sequence. Our results show that there is a deterministic relation between sequence and II-RPLC retention properties of the studied trinucleotides. Further, we can conclude that the higher the content of ion-interaction agent in the mobile phase, the more prominent these properties are. We also show that if we approximate the polar component of solvation energy in QSRR by the electrostatic work in transferring molecules from vacuum to water, and the non-polar component by the solvent accessible surface area, these parameters best describe the retention properties of trinucleotides. There are some exceptions to this finding, namely sequences 5'-NAN-3', 5'-ANN-3', 5'-TGN-3', 5'-NTA-3'and 5'-NGA-3' (N stands for generic nucleotide). Their role is still unknown, but since linear regression including these specific constellations showed a higher observable variance coverage than the model with only the basic descriptors, we may assume that solvent-analyte interactions are responsible for the exceptional behaviour of 5'-NAN-3' & 5'-ANN-3' trinucleotides and some intramolecular interactions of neighbouring nucleobases for 5'-TGN-3', 5'-NTA-3'and 5'-NGA-3' trinucleotides.

• MeSH
• Adenine analogs & derivatives   isolation & purification   MeSH
• Chromatography, Reverse-Phase MeSH
• Guanine analogs & derivatives   isolation & purification   MeSH
• Quantitative Structure-Activity Relationship MeSH
• Neural Networks, Computer MeSH
• Oligonucleotides isolation & purification   MeSH
• Solvents MeSH
• Molecular Dynamics Simulation MeSH
• Static Electricity MeSH
• Thymine analogs & derivatives   isolation & purification   MeSH
• Water MeSH
• Chromatography, High Pressure Liquid MeSH
• Publication type
• Journal Article MeSH

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. APPROACH: The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. MAIN RESULTS: The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p   <  0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. SIGNIFICANCE: Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

• MeSH
• Deep Learning * MeSH
• Electrocorticography instrumentation   methods   MeSH
• Epilepsy diagnosis   physiopathology   MeSH
• Electrodes, Implanted * MeSH
• Forecasting MeSH
• Dogs MeSH
• Seizures diagnosis   physiopathology   MeSH
• Animals MeSH
• Check Tag
• Dogs MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Computer Systems MeSH
• Statistics as Topic MeSH

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika
• lékařská informatika

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika

• MeSH
• Medical Informatics MeSH
• Publication type
• Textbook MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařská informatika

• MeSH
• Medical Informatics MeSH
• Publication type
• Congress MeSH
• Collected Work MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařská informatika