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https://www.readbyqxmd.com/read/28818093/case-report-value-of-gene-expression-profiling-in-the-diagnosis-of-atypical-neuroblastoma
#1
Anne C Harttrampf, Qingrong Chen, Eva Jüttner, Julia Geiger, Gordon Vansant, Javed Khan, Udo Kontny
BACKGROUND: Nephroblastoma and neuroblastoma belong to the most common abdominal malignancies in childhood. Similarities in the initial presentation may provide difficulties in distinguishing between these two entities, especially if unusual variations to prevalent patterns of disease manifestation occur. Because of the risk of tumor rupture, European protocols do not require biopsy for diagnosis, which leads to misdiagnosis in some cases. CASE PRESENTATION: We report on a 4½-year-old girl with a renal tumor displaying radiological and laboratory characteristics supporting the diagnosis of nephroblastoma...
August 17, 2017: BMC Research Notes
https://www.readbyqxmd.com/read/28817568/an-optimal-strategy-for-epilepsy-surgery-disruption-of-the-rich-club
#2
Marinho A Lopes, Mark P Richardson, Eugenio Abela, Christian Rummel, Kaspar Schindler, Marc Goodfellow, John R Terry
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28816677/prototype-incorporated-emotional-neural-network
#3
Oyebade K Oyedotun, Adnan Khashman
Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters...
August 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28813941/inverse-estimation-of-multiple-muscle-activations-based-on-linear-logistic-regression
#4
Masashi Sekiya, Toshiaki Tsuji
This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28812881/predicting-microbial-fuel-cell-biofilm-communities-and-bioreactor-performance-using-artificial-neural-networks
#5
Keaton Larson Lesnik, Hong Liu
The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including 8 separate substrates and 3 different wastewaters...
August 16, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28811818/prototype-generation-using-self-organizing-maps-for-informativeness-based-classifier
#6
Leandro Juvêncio Moreira, Leandro A Silva
The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28811038/fast-and-robust-online-adaptive-planning-in-stereotactic-mr-guided-adaptive-radiation-therapy-smart-for-pancreatic-cancer
#7
O Bohoudi, A M E Bruynzeel, S Senan, J P Cuijpers, B J Slotman, F J Lagerwaard, M A Palacios
BACKGROUND AND PURPOSE: To implement a robust and fast stereotactic MR-guided adaptive radiation therapy (SMART) online strategy in locally advanced pancreatic cancer (LAPC). MATERIAL AND METHODS: SMART strategy for plan adaptation was implemented with the MRIdian system (ViewRay Inc.). At each fraction, OAR (re-)contouring is done within a distance of 3cm from the PTV surface. Online plan re-optimization is based on robust prediction of OAR dose and optimization objectives, obtained by building an artificial neural network (ANN)...
August 12, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/28810826/improving-fold-resistance-prediction-of-hiv-1-against-protease-and-reverse-transcriptase-inhibitors-using-artificial-neural-networks
#8
Olivier Sheik Amamuddy, Nigel T Bishop, Özlem Tastan Bishop
BACKGROUND: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs...
August 15, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28809480/rapid-life-cycle-impact-screening-using-artificial-neural-networks
#9
Runsheng Song, Arturo A Keller, Sangwon Suh
The number of chemicals in the market is rapidly increasing, while our understanding of the life-cycle impacts of these chemicals lags considerably. To address this, we developed deep Artificial Neural Network (ANN) models to estimate life-cycle impacts of chemicals. Using molecular structure information, we trained multilayer ANNs for life-cycle impacts of chemicals using six impact categories, including cumulative energy demand, global warming (IPCC 2007), acidification (TRACI), human health (Impact2000+), ecosystem quality (Impact2000+), and eco-indicator 99 (I,I, total)...
August 15, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28806597/a-nested-multivariate-chemometrics-based-calibration-strategy-for-direct-trace-biometal-analysis-in-soft-tissue-utilizing-energy-dispersive-x-ray-fluorescence-edxrf-and-scattering-spectrometry
#10
REVIEW
J J Okonda, K H Angeyo, J M Mangala, S M Kisia
Compton scatter-modulated fluorescence and multivariate chemometric (artificial neural network (ANN) and principal component regression (PCR)) calibration strategy was explored for direct rapid trace biometals (Mn, Fe, Cu, Zn, Se) analysis in "complex" matrices (model soft tissues). This involved spectral feature selection (multiple fluorescence signatures) normalized to or in conjunction with Compton scatter. ANN model resulted in more accurate trace biometal determination (R(2)>0.9) compared to PCR. Hybrid nested (ANN and PCR) approach led to optimized accurate biometals' concentrations in Oyster tissue (≤ ± 10%)...
August 8, 2017: Applied Radiation and Isotopes
https://www.readbyqxmd.com/read/28803649/root-cause-investigation-of-deviations-in-protein-chromatography-based-on-mechanistic-models-and-artificial-neural-networks
#11
Gang Wang, Till Briskot, Tobias Hahn, Pascal Baumann, Jürgen Hubbuch
In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. Based on visual inspection of the UV signal, it is hard to identify the source of the error and almost unfeasible to determine the quantity of deviation. The problem becomes even more pronounced, if multiple root causes of the deviation are interconnected and lead to an observable deviation...
August 1, 2017: Journal of Chromatography. A
https://www.readbyqxmd.com/read/28802156/neutron-spectrum-unfolding-using-radial-basis-function-neural-networks
#12
Amin Asgharzadeh Alvar, Mohammad Reza Deevband, Meghdad Ashtiyani
Neutron energy spectrum unfolding has been the subject of research for several years. The Bayesian theory, Monte Carlo simulation, and iterative methods are some of the methods that have been used for neutron spectrum unfolding. In this study, the radial basis function (RBF), multilayer perceptron, and artificial neural networks (ANNs) were used for the unfolding of neutron spectrum, and a comparison was made between the networks' results. Both neural network architectures were trained and tested using the same data set for neutron spectrum unfolding from the response of LiI detectors with Eu impurity...
July 26, 2017: Applied Radiation and Isotopes
https://www.readbyqxmd.com/read/28799926/daily-runoff-prediction-using-the-linear-and-non-linear-models
#13
Alireza Sharifi, Yagob Dinpashoh, Rasoul Mirabbasi
Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets...
August 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/28799191/serum-proteome-in-a-sporadic-amyotrophic-lateral-sclerosis-geographical-cluster
#14
Stefano De Benedetti, Elisabetta Gianazza, Cristina Banfi, Alessandro Marocchi, Christian Lunetta, Silvana Penco, Francesco Bonomi, Stefania Iametti
PURPOSE: This study was meant to characterize the serum proteome in a small geographical cluster of sporadic ALS subjects originating from a restricted geographical area and sharing the same environmental exposure, in a broader context of evaluating the relevance of environmental factors to disease onset, status, and progression. EXPERIMENTAL DESIGN: An Artificial Neural Network based software was used to compare the relative abundance of proteins identified as different (by means of bi-dimensional electrophoresis and mass spectrometry) in the serum proteome of patients and age-matched healthy controls...
August 10, 2017: Proteomics. Clinical Applications
https://www.readbyqxmd.com/read/28796509/predicting-thermal-behavior-of-secondary-organic-aerosols
#15
John H Offenberg, Michael Lewandowski, Tadeusz E Kleindienst, Kenneth S Docherty, Mohammed Jaoui, Jonathan Krug, Theran P Riedel, David A Olson
Volume concentrations of secondary organic aerosol (SOA) are measured in 139 steady-state, single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet. The response to change in temperature is well predicted through a feedforward Artificial Neural Network. The most parsimonious model, as indicated by Akaike's Information Criterion, Corrected (AIC,C), utilizes 11 input variables, a single hidden layer of 4 tanh activation function nodes, and a single linear output function...
August 10, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28794478/a-method-based-on-artificial-intelligence-to-fully-automatize-the-evaluation-of-bovine-blastocyst-images
#16
José Celso Rocha, Felipe José Passalia, Felipe Delestro Matos, Maria Beatriz Takahashi, Diego de Souza Ciniciato, Marc Peter Maserati, Mayra Fernanda Alves, Tamie Guibu de Almeida, Bruna Lopes Cardoso, Andrea Cristina Basso, Marcelo Fábio Gouveia Nogueira
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard...
August 9, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28783639/supervised-learning-based-on-temporal-coding-in-spiking-neural-networks
#17
Hesham Mostafa
Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere...
August 1, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28783014/pilot-scale-treatment-of-atrazine-production-wastewater-by-uv-o3-ultrasound-factor-effects-and-system-optimization
#18
Liang Jing, Bing Chen, Diya Wen, Jisi Zheng, Baiyu Zhang
This study shed light on removing atrazine from pesticide production wastewater using a pilot-scale UV/O3/ultrasound flow-through system. A significant quadratic polynomial prediction model with an adjusted R(2) of 0.90 was obtained from central composite design with response surface methodology. The optimal atrazine removal rate (97.68%) was obtained at the conditions of 75 W UV power, 10.75 g h(-1) O3 flow rate and 142.5 W ultrasound power. A Monte Carlo simulation aided artificial neural networks model was further developed to quantify the importance of O3 flow rate (40%), UV power (30%) and ultrasound power (30%)...
August 3, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28780486/rapid-and-simultaneous-analysis-of-five-alkaloids-in-four-parts-of-coptidis-rhizoma-by-near-infrared-spectroscopy
#19
Xue Jintao, Liu Yufei, Ye Liming, Li Chunyan, Yang Quanwei, Wang Weiying, Jing Yun, Zhang Minxiang, Li Peng
Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression...
July 29, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28777718/blind-nonnegative-source-separation-using-biological-neural-networks
#20
Cengiz Pehlevan, Sreyas Mohan, Dmitri B Chklovskii
Blind source separation-the extraction of independent sources from a mixture-is an important problem for both artificial and natural signal processing. Here, we address a special case of this problem when sources (but not the mixing matrix) are known to be nonnegative-for example, due to the physical nature of the sources. We search for the solution to this problem that can be implemented using biologically plausible neural networks. Specifically, we consider the online setting where the data set is streamed to a neural network...
August 4, 2017: Neural Computation
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