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https://www.readbyqxmd.com/read/29037014/deep-into-the-brain-artificial-intelligence-in-stroke-imaging
#1
REVIEW
Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang
Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner...
September 2017: Journal of Stroke
https://www.readbyqxmd.com/read/29021869/a-perspective-of-genes-and-environment-for-the-development-of-environmental-mutagen-research-in-asia
#2
EDITORIAL
Takashi Yagi
Two years have passed since the Japanese Environmental Society (JEMS) made the official journal Genes and Environment (G&E) open access. Current subjects on environmental mutagen research to further advance this field are described herein, and the roles of JEMS and G&E are discussed. Various important subjects are being investigated in current research fields such as severe environmental pollution in Asian countries; the identification of new hazardous substances and elucidation of mutation mechanisms using newly developed techniques; the development of new genotoxicity assays including in silico predictions using information technology and artificial intelligence as well as bioassays...
2017: Genes and Environment: the Official Journal of the Japanese Environmental Mutagen Society
https://www.readbyqxmd.com/read/28986108/temporal-case-based-reasoning-for-type-1-diabetes-mellitus-bolus-insulin-decision-support
#3
Daniel Brown, Arantza Aldea, Rachel Harrison, Clare Martin, Ian Bayley
Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this process bolus calculators have been developed that suggest the appropriate dose. However these calculators do not automatically adapt to the specific circumstances of an individual and require fine-tuning of parameters, a process that often requires the input of an expert...
October 3, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28968739/snpdelscore-combining-multiple-methods-to-score-deleterious-effects-of-noncoding-mutations-in-the-human-genome
#4
Roberto Vera Alvarez, Shan Li, David Landsman, Ivan Ovcharenko
Abstract: Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning, and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them...
September 14, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28959387/classification-models-to-predict-survival-of-kidney-transplant-recipients-using-two-intelligent-techniques-of-data-mining-and-logistic-regression
#5
M Nematollahi, R Akbari, S Nikeghbalian, C Salehnasab
Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008-2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors...
2017: International Journal of Organ Transplantation Medicine
https://www.readbyqxmd.com/read/28958450/experimental-and-ai-based-numerical-modeling-of-contaminant-transport-in-porous-media
#6
Vahid Nourani, Shahram Mousavi, Fahreddin Sadikoglu, Vijay P Singh
This study developed a new hybrid artificial intelligence (AI)-meshless approach for modeling contaminant transport in porous media. The key innovation of the proposed approach is that both black box and physically-based models are combined for modeling contaminant transport. The effectiveness of the approach was evaluated using experimental and real world data. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were calibrated to predict temporal contaminant concentrations (CCs), and the effect of noisy and de-noised data on the model performance was evaluated...
September 21, 2017: Journal of Contaminant Hydrology
https://www.readbyqxmd.com/read/28946482/fuzzy-logic-modeling-of-pb-ii-sorption-onto-mesoporous-nio-zncl2-rosa-canina-l-seeds-activated-carbon-nanocomposite-prepared-by-ultrasound-assisted-co-precipitation-technique
#7
Hamedreza Javadian, Maryam Ghasemi, Montserrat Ruiz, Ana Maria Sastre, Seyed Mostafa Hosseini Asl, Mojtaba Masomi
In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N2 adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments...
January 2018: Ultrasonics Sonochemistry
https://www.readbyqxmd.com/read/28945910/natural-and-artificial-intelligence-in-neurosurgery-a-systematic-review
#8
Joeky T Senders, Omar Arnaout, Aditya V Karhade, Hormuzdiyar H Dasenbrock, William B Gormley, Marike L Broekman, Timothy R Smith
BACKGROUND: Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE: To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." METHODS: A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature...
September 7, 2017: Neurosurgery
https://www.readbyqxmd.com/read/28943335/single-nucleotide-polymorphism-relevance-learning-with-random-forests-for-type-2-diabetes-risk-prediction
#9
Beatriz López, Ferran Torrent-Fontbona, Ramón Viñas, José Manuel Fernández-Real
OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. METHODS: We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute...
September 21, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28893410/an-artificial-neural-network-to-predict-resting-energy-expenditure-in-obesity
#10
Emmanuel Disse, Séverine Ledoux, Cécile Bétry, Cyrielle Caussy, Christine Maitrepierre, Muriel Coupaye, Martine Laville, Chantal Simon
BACKGROUND & AIMS: The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields...
September 1, 2017: Clinical Nutrition: Official Journal of the European Society of Parenteral and Enteral Nutrition
https://www.readbyqxmd.com/read/28872869/demystifying-multitask-deep-neural-networks-for-quantitative-structure-activity-relationships
#11
Yuting Xu, Junshui Ma, Andy Liaw, Robert P Sheridan, Vladimir Svetnik
Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks...
October 2, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28864396/environmental-indicators-of-oyster-norovirus-outbreaks-in-coastal-waters
#12
Shima Shamkhali Chenar, Zhiqiang Deng
This paper presents an artificial intelligence-based approach to identifying environmental indicators of oyster norovirus outbreaks in coastal waters. It was found that oyster norovirus outbreaks are generally linked to the extreme combination of antecedent environmental conditions characterized by low water temperature, low solar radiation, low gage height, low salinity, strong wind, and heavy precipitation. Among the six environmental indicators, the most important three indicators, including water temperature, solar radiation and gage height, are capable of explaining 77...
August 25, 2017: Marine Environmental Research
https://www.readbyqxmd.com/read/28814023/representing-high-dimensional-data-to-intelligent-prostheses-and-other-wearable-assistive-robots-a-first-comparison-of-tile-coding-and-selective-kanerva-coding
#13
Jaden B Travnik, Patrick M Pilarski
Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28802577/how-fast-fast-folding-proteins-fold-in-silico
#14
Yuan-Ping Pang
In reported microcanonical molecular dynamics simulations, fast-folding proteins CLN025 and Trp-cage autonomously folded to experimentally determined native conformations. However, the folding times of these proteins derived from the simulations were more than 4-10 times longer than their experimental values. This article reports autonomous folding of CLN025 and Trp-cage in isobaric-isothermal molecular dynamics simulations with agreements within factors of 0.69-1.75 between simulated and experimental folding times at different temperatures...
October 7, 2017: Biochemical and Biophysical Research Communications
https://www.readbyqxmd.com/read/28791547/data-based-prediction-of-blood-glucose-concentrations-using-evolutionary-methods
#15
J Ignacio Hidalgo, J Manuel Colmenar, Gabriel Kronberger, Stephan M Winkler, Oscar Garnica, Juan Lanchares
Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections...
August 8, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28771788/her2-challenge-contest-a-detailed-assessment-of-automated-her2-scoring-algorithms-in-whole-slide-images-of-breast-cancer-tissues
#16
Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian O Ellis, Mohammad Ilyas, Nasir Rajpoot
AIMS: Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required...
August 3, 2017: Histopathology
https://www.readbyqxmd.com/read/28735210/moisture-content-prediction-in-poultry-litter-using-artificial-intelligence-techniques-and-monte-carlo-simulation-to-determine-the-economic-yield-from-energy-use
#17
José Octavio Rico-Contreras, Alberto Alfonso Aguilar-Lasserre, Juan Manuel Méndez-Contreras, Jhony Josué López-Andrés, Gabriela Cid-Chama
The objective of this study is to determine the economic return of poultry litter combustion in boilers to produce bioenergy (thermal and electrical), as this biomass has a high-energy potential due to its component elements, using fuzzy logic to predict moisture and identify the high-impact variables. This is carried out using a proposed 7-stage methodology, which includes a statistical analysis of agricultural systems and practices to identify activities contributing to moisture in poultry litter (for example, broiler chicken management, number of air extractors, and avian population density), and thereby reduce moisture to increase the yield of the combustion process...
July 20, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28702488/data-on-the-physical-and-mechanical-properties-of-soilcrete-materials-modified-with-metakaolin
#18
Panagiotis G Asteris, Konstantinos G Kolovos
During the last decades eco-friendly, low-cost, sustainable construction materials for utilization in civil engineering projects have attracted much attention. To this end, soilcretes are non-conventional construction materials produced by mixing natural soil such as natural clay or limestone sand with a hydraulic binder and are recently under detailed and in-depth investigation by many researchers. In this paper the results of the physical and mechanical characteristics of a large set of cylindrical specimens under uniaxial compression, are presented...
August 2017: Data in Brief
https://www.readbyqxmd.com/read/28663789/molecular-and-phenotypic-biomarkers-of-aging
#19
REVIEW
Xian Xia, Weiyang Chen, Joseph McDermott, Jing-Dong Jackie Han
Individuals of the same age may not age at the same rate. Quantitative biomarkers of aging are valuable tools to measure physiological age, assess the extent of 'healthy aging', and potentially predict health span and life span for an individual. Given the complex nature of the aging process, the biomarkers of aging are multilayered and multifaceted. Here, we review the phenotypic and molecular biomarkers of aging. Identifying and using biomarkers of aging to improve human health, prevent age-associated diseases, and extend healthy life span are now facilitated by the fast-growing capacity of multilevel cross-sectional and longitudinal data acquisition, storage, and analysis, particularly for data related to general human populations...
2017: F1000Research
https://www.readbyqxmd.com/read/28659000/recurrent-neural-network-based-modeling-of-gene-regulatory-network-using-elephant-swarm-water-search-algorithm
#20
Sudip Mandal, Goutam Saha, Rajat Kumar Pal
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN)...
June 13, 2017: Journal of Bioinformatics and Computational Biology
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