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artificial neural network

Srikanth Katla, Bappa Karmakar, Subbi Rami Reddy Tadi, Naresh Mohan, B Anand, Uttariya Pal, Senthilkumar Sivaprakasam
AIMS: The present study was aimed at design of experiments (DoE) and artificial intelligence based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization. METHODS AND RESULTS: Artificial neural network- Genetic algorithm (ANN-GA) model exhibited improved huIFNα2b production and better predictability compared to RSM. The optimized medium exhibited 5-fold increase in huIFNα2b titer compared to the complex medium...
February 18, 2019: Journal of Applied Microbiology
Ryad Zemouri, Christine Devalland, Séverine Valmary-Degano, Noureddine Zerhouni
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides...
February 14, 2019: Annales de Pathologie
Maryam Zeinolabedini, Mohammad Najafzadeh
In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant (WWTP). Daily datasets of sewage sludge have been used to develop the artificial intelligence models. Six mother wavelet (W) functions were employed as a preprocessor in order to increase accuracy level of ANNs. In this way, a 4-day lags were considered as input variables to conduct training and testing stages for the proposed W-ANNs...
February 16, 2019: Environmental Monitoring and Assessment
Somnath Chowdhury, Jaya Sikder, Tamal Mandal, Gopinath Halder
The current investigation deals with how chemically activated carbon derived from industrial paper sludge (ACPS) performs on sorptive removal of enrofloxacin (ENF), an antibacterial drug from its water solution. Thermogravimetric (TGA) and proximate analysis of raw paper sludge (RPS) were conducted. ACPS was characterized with proximate analysis, XRD, FT-IR, SEM and BET. The influence of five operational parameters viz. adsorbate concentration (initial), dose of adsorbent, pH, temperature, and contact time on the adsorption of ENF onto ACPS has been conducted using batch experiments...
February 8, 2019: Science of the Total Environment
Rong Zhang, Zhao-Yue Chen, Li-Jun Xu, Chun-Quan Ou
BACKGROUND: Drought is a major natural disaster that causes severe social and economic losses. The prediction of regional droughts may provide important information for drought preparedness and farm irrigation. The existing drought prediction models are mainly based on a single weather station. Efforts need to be taken to develop a new multistation-based prediction model. OBJECTIVES: This study optimizes the predictor selection process and develops a new model to predict droughts using past drought index, meteorological measures and climate signals from 32 stations during 1961 to 2016 in Shaanxi province, China...
February 10, 2019: Science of the Total Environment
Toluwalope Ajayi, Rozita Dara, Zvonimir Poljak
Porcine Epidemic Diarrhea Virus (PEDV) emerged in North America in 2013. The first case of PEDV in Canada was identified on an Ontario farm in January 2014. Surveillance was instrumental in identifying the initial case and in minimizing the spread of the virus to other farms. With recent advances in predictive analytics showing promise for health and disease forecasting, the primary objective of this study was to apply machine learning predictive methods (random forest, artificial neural networks, and classification and regression trees) to provincial PEDV incidence data, and in so doing determine their accuracy for predicting future PEDV trends...
March 1, 2019: Preventive Veterinary Medicine
Timothy Cogan, Maribeth Cogan, Lakshman Tamil
About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings...
February 5, 2019: Computers in Biology and Medicine
Daniel Durstewitz, Georgia Koppe, Andreas Meyer-Lindenberg
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them...
February 15, 2019: Molecular Psychiatry
Maxim Signaevsky, Marcel Prastawa, Kurt Farrell, Nabil Tabish, Elena Baldwin, Natalia Han, Megan A Iida, John Koll, Clare Bryce, Dushyant Purohit, Vahram Haroutunian, Ann C McKee, Thor D Stein, Charles L White, Jamie Walker, Timothy E Richardson, Russell Hanson, Michael J Donovan, Carlos Cordon-Cardo, Jack Zeineh, Gerardo Fernandez, John F Crary
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden...
February 15, 2019: Laboratory Investigation; a Journal of Technical Methods and Pathology
Umberto Michelucci, Michael Baumgartner, Francesca Venturini
Luminescence-based sensors for measuring oxygen concentration are widely used in both industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most cases is non-linear, is parameterized through device-specific constants...
February 14, 2019: Sensors
Ali Kaab, Mohammad Sharifi, Hossein Mobli, Ashkan Nabavi-Pelesaraei, Kwok-Wing Chau
This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production...
February 6, 2019: Science of the Total Environment
Naghmeh Mahmoodian, Anna Schaufler, Ali Pashazadeh, Axel Boese, Michael Friebe, Alfredo Illanes
Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeons to be aware of hidden or non-obvious events. To minimize the impact it is crucial for the surgeon to detect such a perforation very early. We propose a novel approach to identify perforations based on the acquisition and analysis of audio signals on the outside proximal end of a guide wire. The signals were acquired using a stethoscope equipped with a microphone and attached to the proximal end of the guide wire via a 3D printed adapter...
February 7, 2019: Computers in Biology and Medicine
Sven Kleinert, Ayhan Tajalli, Tamas Nagy, Uwe Morgner
The knowledge of the temporal shape of femtosecond pulses is of major interest for all their applications. The reconstruction of the temporal shape of these pulses is an inverse problem for characterization techniques, which benefit from an inherent redundancy in the measurement. Conventionally, time-consuming optimization algorithms are used to solve the inverse problems. Here, we demonstrate the reconstruction of ultrashort pulses from dispersion scan traces employing a deep neural network. The network is trained with a multitude of artificial and noisy dispersion scan traces from randomly shaped pulses...
February 15, 2019: Optics Letters
Yilong Yang, Zhuyifan Ye, Yan Su, Qianqian Zhao, Xiaoshan Li, Defang Ouyang
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems...
January 2019: Acta Pharmaceutica Sinica. B
Peilin Li, Deyu Kong, Tian Tang, Di Su, Pu Yang, Huixia Wang, Zhihe Zhao, Yang Liu
In this study, multilayer perceptron artificial neural networks are used to predict orthodontic treatment plans, including the determination of extraction-nonextraction, extraction patterns, and anchorage patterns. The neural network can output the feasibilities of several applicable treatment plans, offering orthodontists flexibility in making decisions. The neural network models show an accuracy of 94.0% for extraction-nonextraction prediction, with an area under the curve (AUC) of 0.982, a sensitivity of 94...
February 14, 2019: Scientific Reports
Christodoulou Evangelia, M A Jie, Gary S Collins, Ewout W Steyerberg, Jan Y Verbakel, Ben van Calster
OBJECTIVE: To compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling. STUDY DESIGN AND SETTING: We conducted a Medline literature search (1/2016 to 8/2017), and extracted comparisons between LR and ML models for binary outcomes. RESULTS: We included 71 out of 927 studies. The median sample size was 1250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and 8 events per predictor (range 0...
February 11, 2019: Journal of Clinical Epidemiology
Masashi Sekiya, Sho Sakaino, Toshiaki Tsuji
This paper addresses a techique to estimate muscle activity from movement data. Statistical models, such as linear regression (LR) models and artificial neural networks (ANNs), are good candidate estimation techniques. Although an ANN has a high estimation capability, it is frequently in the clinical application that a very small amount of data leads to performance deterioration. Conversely, an LR model needs fewer data, while its generalization performance is limited. In this paper therefore, a muscle activity estimation method is proposed that uses a linear logistic regression model to improve the generalization performance...
February 8, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Georges Matar, Jean-Marc Lina, Georges Kaddoum
Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video-surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of view obstruction. We propose an autonomous method for classifying the four state-of-art HBLPs in healthy adult subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject...
February 13, 2019: IEEE Journal of Biomedical and Health Informatics
Manuela Macedonia, Claudia Repetto, Anja Ischebeck, Karsten Mueller
Word learning is basic to foreign language acquisition, however time consuming and not always successful. Empirical studies have shown that traditional (visual) word learning can be enhanced by gestures. The gesture benefit has been attributed to depth of encoding. Gestures can lead to depth of encoding because they trigger semantic processing and sensorimotor enrichment of the novel word. However, the neural underpinning of depth of encoding is still unclear. Here, we combined an fMRI and a behavioral study to investigate word encoding online...
2019: Frontiers in Psychology
Caio A Custódio, Érica R Filletti, Vivian V França
In this work we propose an artificial neural network functional to the ground-state energy of fermionic interacting particles in homogeneous chains described by the Hubbard model. Our neural network functional was proven to have an excellent performance: it deviates from numerically exact calculations by less than 0.15% for a vast regime of interactions and for all the regimes of filling factors and magnetizations. When compared to analytical functionals, the neural functional was found to be more precise for all the regimes of parameters, being particularly superior at the weakly interacting regime: where the analytical parametrization fails the most, ~7%, against only ~0...
February 13, 2019: Scientific Reports
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