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https://www.readbyqxmd.com/read/28344110/toward-a-systematic-exploration-of-nano-bio-interactions
#1
REVIEW
Xue Bai, Fang Liu, Yin Liu, Cong Li, Shenqing Wang, Hongyu Zhou, Wenyi Wang, Hao Zhu, Dave Winkler, Bing Yan
Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability...
March 24, 2017: Toxicology and Applied Pharmacology
https://www.readbyqxmd.com/read/28333051/artificial-intelligence-based-model-for-optimization-of-cod-removal-efficiency-of-an-up-flow-anaerobic-sludge-blanket-reactor-in-the-saline-wastewater-treatment
#2
Alain R Picos-Benítez, Juan D López-Hincapié, Abraham U Chávez-Ramírez, Adrián Rodríguez-García
The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables...
March 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#3
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325441/a-review-of-fuzzy-cognitive-maps-in-medicine-taxonomy-methods-and-applications
#4
REVIEW
Abdollah Amirkhani, Elpiniki I Papageorgiou, Akram Mohseni, Mohammad R Mosavi
BACKGROUND AND OBJECTIVE: A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28321856/automation-is-it-really-different-this-time
#5
REVIEW
Judy Wajcman
This review examines several recent books that deal with the impact of automation and robotics on the future of jobs. Most books in this genre predict that the current phase of digital technology will create massive job loss in an unprecedented way, that is, that this wave of automation is different from previous waves. Uniquely digital technology is said to automate professional occupations for the first time. This review critically examines these claims, puncturing some of the hyperbole about automation, robotics and Artificial Intelligence...
March 2017: British Journal of Sociology
https://www.readbyqxmd.com/read/28298701/identification-of-probabilities
#6
Paul M B Vitányi, Nick Chater
Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in principle? We explore this question and find some surprisingly positive and general results...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28285459/a-critical-review-for-developing-accurate-and-dynamic-predictive-models-using-machine-learning-methods-in-medicine-and-health-care
#7
Hamdan O Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi
Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28279828/intelligent-evaluation-of-color-sensory-quality-of-black-tea-by-visible-near-infrared-spectroscopy-technology-a-comparison-of-spectra-and-color-data-information
#8
Qin Ouyang, Yan Liu, Quansheng Chen, Zhengzhu Zhang, Jiewen Zhao, Zhiming Guo, Hang Gu
Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables...
March 3, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28231323/even-good-bots-fight-the-case-of-wikipedia
#9
Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi, Taha Yasseri
In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful...
2017: PloS One
https://www.readbyqxmd.com/read/28223265/use-of-a-novel-artificial-intelligence-platform-on-mobile-devices-to-assess-dosing-compliance-in-a-phase-2-clinical-trial-in-subjects-with-schizophrenia
#10
Earle E Bain, Laura Shafner, David P Walling, Ahmed A Othman, Christy Chuang-Stein, John Hinkle, Adam Hanina
BACKGROUND: Accurately monitoring and collecting drug adherence data can allow for better understanding and interpretation of the outcomes of clinical trials. Most clinical trials use a combination of pill counts and self-reported data to measure drug adherence, despite the drawbacks of relying on these types of indirect measures. It is assumed that doses are taken, but the exact timing of these events is often incomplete and imprecise. OBJECTIVE: The objective of this pilot study was to evaluate the use of a novel artificial intelligence (AI) platform (AiCure) on mobile devices for measuring medication adherence, compared with modified directly observed therapy (mDOT) in a substudy of a Phase 2 trial of the α7 nicotinic receptor agonist (ABT-126) in subjects with schizophrenia...
February 21, 2017: JMIR MHealth and UHealth
https://www.readbyqxmd.com/read/28219431/assessment-of-triglyceride-and-cholesterol-in-overweight-people-based-on-multiple-linear-regression-and-artificial-intelligence-model
#11
Jing Ma, Jiong Yu, Guangshu Hao, Dan Wang, Yanni Sun, Jianxin Lu, Hongcui Cao, Feiyan Lin
BACKGROUND: The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. METHODS: A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL)...
February 20, 2017: Lipids in Health and Disease
https://www.readbyqxmd.com/read/28215473/machine-learning-based-prediction-of-adverse-drug-effects-an-example-of-seizure-inducing-compounds
#12
Mengxuan Gao, Hideyoshi Igata, Aoi Takeuchi, Kaoru Sato, Yuji Ikegaya
Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning...
February 2017: Journal of Pharmacological Sciences
https://www.readbyqxmd.com/read/28176905/effect-of-roll-compaction-on-granule-size-distribution-of-microcrystalline-cellulose-mannitol-mixtures-computational-intelligence-modeling-and-parametric-analysis
#13
Pezhman Kazemi, Mohammad Hassan Khalid, Ana Pérez Gago, Peter Kleinebudde, Renata Jachowicz, Jakub Szlęk, Aleksander Mendyk
Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD...
2017: Drug Design, Development and Therapy
https://www.readbyqxmd.com/read/28167793/theory-of-cortical-function
#14
David J Heeger
Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation)...
February 21, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28159597/gene-selection-for-microarray-cancer-classification-using-a-new-evolutionary-method-employing-artificial-intelligence-concepts
#15
M Dashtban, Mohammadali Balafar
Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators...
February 1, 2017: Genomics
https://www.readbyqxmd.com/read/28138223/computational-intelligence-models-to-predict-porosity-of-tablets-using-minimum-features
#16
Mohammad Hassan Khalid, Pezhman Kazemi, Lucia Perez-Gandarillas, Abderrahim Michrafy, Jakub Szlęk, Renata Jachowicz, Aleksander Mendyk
The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico...
2017: Drug Design, Development and Therapy
https://www.readbyqxmd.com/read/28111297/modeling-of-glucose-release-from-native-and-modified-wheat-starch-gels-during-in-vitro-gastrointestinal-digestion-using-artificial-intelligence-methods
#17
A R Yousefi, Seyed M A Razavi
Estimation of the amounts of glucose release (AGR) during gastrointestinal digestion can be useful to identify food of potential use in the diet of individuals with diabetes. In this work, adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm-artificial neural network (GA-ANN) and group method of data handling (GMDH) models were applied to estimate the AGR from native (NWS), cross-linked (CLWS) and hydroxypropylated wheat starch (HPWS) gels during digestion under simulated gastrointestinal conditions...
April 2017: International Journal of Biological Macromolecules
https://www.readbyqxmd.com/read/28055930/deep-learning-for-health-informatics
#18
Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, Guang-Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data...
January 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27974883/artificial-neural-network-and-genetic-algorithm-hybrid-intelligence-for-predicting-thai-stock-price-index-trend
#19
Montri Inthachot, Veera Boonjing, Sarun Intakosum
This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction...
2016: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/27920762/artificial-intelligence-vs-statistical-modeling-and-optimization-of-continuous-bead-milling-process-for-bacterial-cell-lysis
#20
Shafiul Haque, Saif Khan, Mohd Wahid, Sajad A Dar, Nipunjot Soni, Raju K Mandal, Vineeta Singh, Dileep Tiwari, Mohtashim Lohani, Mohammed Y Areeshi, Thavendran Govender, Hendrik G Kruger, Arshad Jawed
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery...
2016: Frontiers in Microbiology
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