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Artificial neural networks

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https://www.readbyqxmd.com/read/28550374/performance-of-an-artificial-multi-observer-deep-neural-network-for-fully-automated-segmentation-of-polycystic-kidneys
#1
Timothy L Kline, Panagiotis Korfiatis, Marie E Edwards, Jaime D Blais, Frank S Czerwiec, Peter C Harris, Bernard F King, Vicente E Torres, Bradley J Erickson
Deep learning techniques are being rapidly applied to medical imaging tasks-from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys...
May 26, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28545607/dynamically-weighted-evolutionary-ordinal-neural-network-for-solving-an-imbalanced-liver-transplantation-problem
#2
Manuel Dorado-Moreno, María Pérez-Ortiz, Pedro A Gutiérrez, Rubén Ciria, Javier Briceño, César Hervás-Martínez
OBJECTIVE: Create an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of organ survival at different thresholds for each donor-recipient pair considered. Currently, this decision is mainly based on the Model for End-stage Liver Disease, which depends only on the severity of the recipient and obviates donor-recipient compatibility...
March 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28541894/the-extreme-value-machine
#3
Ethan M Rudd, Lalit P Jain, Walter J Scheirer, Terrance E Boult
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function - ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g., artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes...
May 23, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28541664/prediction-of-collision-cross-section-values-for-small-molecules-application-to-pesticide-residue-analysis
#4
Lubertus Bijlsma, Richard Bade, Alberto Celma, Lauren Mullin, Gareth Cleland, Sara Stead, Felix Hernandez, Juan Vicente Sancho
The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. However, its utility is limited by the number of experimental CCS values currently available. This work demonstrates the potential of artificial neural networks (ANNs) for the prediction of CCS values of pesticides. The predictor, based on eight software-chosen mo-lecular descriptors, was optimised using CCS values of 205 small molecules and validated using a set of 131 pesticides...
May 25, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28540682/the-limitations-of-existing-approaches-in-improving-microrna-target-prediction-accuracy
#5
Rasiah Loganantharaj, Thomas A Randall
MicroRNAs (miRNAs) are small (18-24 nt) endogenous RNAs found across diverse phyla involved in posttranscriptional regulation, primarily downregulation of mRNAs. Experimentally determining miRNA-mRNA interactions can be expensive and time-consuming, making the accurate computational prediction of miRNA targets a high priority. Since miRNA-mRNA base pairing in mammals is not perfectly complementary and only a fraction of the identified motifs are real binding sites, accurately predicting miRNA targets remains challenging...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28537036/modeling-of-policies-for-reduction-of-ghg-emissions-in-energy-sector-using-ann-case-study-croatia-eu
#6
Tomislav Bolanča, Tomislav Strahovnik, Šime Ukić, Mirjana Novak Stankov, Marko Rogošić
This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources...
May 24, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28537025/prediction-of-dissolved-oxygen-concentration-in-hypoxic-river-systems-using-support-vector-machine-a-case-study-of-wen-rui-tang-river-china
#7
Xiaoliang Ji, Xu Shang, Randy A Dahlgren, Minghua Zhang
Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China...
May 23, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28536910/development-of-modis-data-based-algorithm-for-retrieving-sea-surface-temperature-in-coastal-waters
#8
Jiao Wang, Zhiqiang Deng
A new algorithm was developed for retrieving sea surface temperature (SST) in coastal waters using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua platform. The new SST algorithm was trained using the Artificial Neural Network (ANN) method and tested using 8 years of remote sensing data from MODIS Aqua sensor and in situ sensing data from the US coastal waters in Louisiana, Texas, Florida, California, and New Jersey. The ANN algorithm could be utilized to map SST in both deep offshore and particularly shallow nearshore waters at the high spatial resolution of 1 km, greatly expanding the coverage of remote sensing-based SST data from offshore waters to nearshore waters...
June 2017: Environmental Monitoring and Assessment
https://www.readbyqxmd.com/read/28532357/visual-object-recognition-do-we-finally-know-more-now-than-we-did
#9
Isabel Gauthier, Michael J Tarr
How do we recognize objects despite changes in their appearance? The past three decades have been witness to intense debates regarding both whether objects are encoded invariantly with respect to viewing conditions and whether specialized, separable mechanisms are used for the recognition of different object categories. We argue that such dichotomous debates ask the wrong question. Much more important is the nature of object representations: What are features that enable invariance or differential processing between categories? Although the nature of object features is still an unanswered question, new methods for connecting data to models show significant potential for helping us to better understand neural codes for objects...
October 14, 2016: Annual Review of Vision Science
https://www.readbyqxmd.com/read/28532161/modelling-of-phytoextraction-efficiency-of-microbially-stimulated-salix-dasyclados-l-in-the-soils-with-different-speciation-of-heavy-metals
#10
Michał Złoch, Tomasz Kowalkowski, Jarosław Tyburski, Katarzyna Hrynkiewicz
Bioaugmentation of soils with selected microorganisms during phytoextraction can be the key solution, but the actual efficacy of this technology should be confirmed for different physicochemical soil parameters and heavy metal availability to guarantee the universality of this method. Equally important is development of the accurate prediction tool to manage phytoremediation process.The main objective of this study was to evaluate the role of three metalotolerant siderophore-producing Streptomyces sp. B1-B3 strains in the phytoremediation of heavy metals with the use of S...
May 22, 2017: International Journal of Phytoremediation
https://www.readbyqxmd.com/read/28530547/application-of-machine-learning-approaches-for-protein-protein-interactions-prediction
#11
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, Bing Niu
BACKGROUND: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28523139/an-overview-of-the-use-of-artificial-neural-networks-in-lung-cancer-research
#12
EDITORIAL
Luca Bertolaccini, Piergiorgio Solli, Alessandro Pardolesi, Antonello Pasini
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification...
April 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/28522333/development-of-qsars-for-parameterizing-physiology-based-toxicokinetic-models
#13
Dimosthenis Α Sarigiannis, Krystalia Papadaki, Periklis Kontoroupis, Spyros P Karakitsios
A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis - Menten constant (Km)...
May 15, 2017: Food and Chemical Toxicology
https://www.readbyqxmd.com/read/28520235/high-dimensional-neural-network-potentials-for-complex-systems
#14
Jörg Behler
Modern simulation techniques have reached a level of maturity, which allows addressing a wide range of problems in chemistry and materials science. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and in spite of the rapid evolution of computer hardware no fundamental change of this situation can be expected. Consequently, to reach an atomic level understanding of complex systems, the development of more efficient but equally reliable atomistic potentials has received considerable attention in recent years...
May 18, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28510124/computer-aided-estimation-of-the-herg-mediated-cardiotoxicity-risk-of-potential-drug-components
#15
E V Radchenko, Yu A Rulev, A Ya Safanyaev, V A Palyulin, N S Zefirov
The hERG potassium channel is one of the most important anti-targets determining cardiotoxicity of potential drugs. Using fragmental descriptors and artificial neural networks, the predictive models of the relationship between the structure of organic compounds and their activity with respect to hERG were built, and the structural factors affecting it were analyzed. By their predictive ability and applicability domain, these models (N = 1000, Q (2) = 0.77, RMSE cv = 0.45 for affinity and N = 2886, Q (2) = 0...
March 2017: Doklady. Biochemistry and Biophysics
https://www.readbyqxmd.com/read/28507947/preparation-optimization-and-activity-evaluation-of-plga-streptokinase-nanoparticles-using-electrospray
#16
Nasrin Yaghoobi, Reza Faridi Majidi, Mohammad Ali Faramarzi, Hadi Baharifar, Amir Amani
Purpose: PLGA nanoparticles (NPs) have been extensively investigated as carriers of different drug molecules to enhance their therapeutic effects or preserve them from the aqueous environment. Streptokinase (SK) is an important medicine for thrombotic diseases. Methods: In this study, we used electrospray to encapsulate SK in PLGA NPs and evaluate its activity. This is the first paper which investigates activity of an electrosprayed enzyme. Effect of three input parameters, namely, voltage, internal diameter of needle (nozzle) and concentration ratio of polymer to protein on size and size distribution (SD) of NPs was evaluated using artificial neural networks (ANNs)...
April 2017: Advanced Pharmaceutical Bulletin
https://www.readbyqxmd.com/read/28505758/machine-learning-applied-to-proton-radiography-of-high-energy-density-plasmas
#17
Nicholas F Y Chen, Muhammad Firmansyah Kasim, Luke Ceurvorst, Naren Ratan, James Sadler, Matthew C Levy, Raoul Trines, Robert Bingham, Peter Norreys
Proton radiography is a technique extensively used to resolve magnetic field structures in high-energy-density plasmas, revealing a whole variety of interesting phenomena such as magnetic reconnection and collisionless shocks found in astrophysical systems. Existing methods of analyzing proton radiographs give mostly qualitative results or specific quantitative parameters, such as magnetic field strength, and recent work showed that the line-integrated transverse magnetic field can be reconstructed in specific regimes where many simplifying assumptions were needed...
April 2017: Physical Review. E
https://www.readbyqxmd.com/read/28504360/oxygen-extraction-fraction-mapping-at-3-tesla-using-an-artificial-neural-network-a-feasibility-study
#18
Sebastian Domsch, Bettina Mürle, Sebastian Weingärtner, Jascha Zapp, Frederik Wenz, Lothar R Schad
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied using least-squares regression (LSR) to an analytical tissue model. However, the LSR method has not yet become clinically established due to the necessity for long acquisition times. Artificial neural networks (ANNs) recently have received increasing interest for robust curve-fitting and might pose an alternative to the conventional LSR method for reduced acquisition times...
May 14, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28500895/a-universal-multilingual-weightless-neural-network-tagger-via-quantitative-linguistics
#19
Hugo C C Carneiro, Carlos E Pedreira, Felipe M G França, Priscila M V Lima
In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks...
April 26, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28499818/biosurfactant-biopolymer-driven-microbial-enhanced-oil-recovery-meor-and-its-optimization-by-an-ann-ga-hybrid-technique
#20
Gunaseelan Dhanarajan, Vivek Rangarajan, Chandrakanth Bandi, Abhivyakti Dixit, Susmita Das, Kranthikiran Ale, Ramkrishna Sen
A lipopeptide biosurfactant produced by marine Bacillus megaterium and a biopolymer produced by thermophilic Bacillus licheniformis were tested for their application potential in the enhanced oil recovery. The crude biosurfactant obtained after acid precipitation effectively reduced the surface tension of deionized water from 70.5 to 28.25mN/m and the interfacial tension between lube oil and water from 18.6 to 1.5mN/m at a concentration of 250mgL(-1). The biosurfactant exhibited a maximum emulsification activity (E24) of 81...
May 10, 2017: Journal of Biotechnology
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