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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
#1
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
#2
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...
August 9, 2017: Biochemical and Biophysical Research Communications
https://www.readbyqxmd.com/read/28791547/data-based-prediction-of-blood-glucose-concentrations-using-evolutionary-methods
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
https://www.readbyqxmd.com/read/28653123/pixel-level-deep-segmentation-artificial-intelligence-quantifies-muscle-on-computed-tomography-for-body-morphometric-analysis
#9
Hyunkwang Lee, Fabian M Troschel, Shahein Tajmir, Georg Fuchs, Julia Mario, Florian J Fintelmann, Synho Do
Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an "eyeball test" to assess whether patients will tolerate major surgery or chemotherapy, "eyeballing" is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts...
August 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28620199/predicting-the-outcomes-of-organic-reactions-via-machine-learning-are-current-descriptors-sufficient
#10
G Skoraczyński, P Dittwald, B Miasojedow, S Szymkuć, E P Gajewska, B A Grzybowski, A Gambin
As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted...
June 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28566328/somatic-mutations-drive-distinct-imaging-phenotypes-in-lung-cancer
#11
Emmanuel Rios Velazquez, Chintan Parmar, Ying Liu, Thibaud P Coroller, Gisele Cruz, Olya Stringfield, Zhaoxiang Ye, Mike Makrigiorgos, Fiona Fennessy, Raymond H Mak, Robert Gillies, John Quackenbush, Hugo J W L Aerts
Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics...
July 15, 2017: Cancer Research
https://www.readbyqxmd.com/read/28545640/artificial-intelligence-in-precision%C3%A2-cardiovascular-medicine
#12
REVIEW
Chayakrit Krittanawong, HongJu Zhang, Zhen Wang, Mehmet Aydar, Takeshi Kitai
Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm...
May 30, 2017: Journal of the American College of Cardiology
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
#13
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...
July 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28522333/development-of-qsars-for-parameterizing-physiology-based-toxicokinetic-models
#14
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/28499165/preparing-diopside-nanoparticle-scaffolds-via-space-holder-method-simulation-of-the-compressive-strength-and-porosity
#15
Majid Abdellahi, Aliakbar Najafinezhad, Hamid Ghayour, Saeed Saber-Samandari, Amirsalar Khandan
In the present study, diopside nanopowders were prepared via mechanical milling with eggshell as the calcium source. The space holder method (compaction of ceramic powder and spacer) as one of the most important methods to produce ceramic/metal scaffolds was used to produce diopside scaffolds. For the first time, the effect of the spacer size on mechanical properties and porosity of the obtained scaffolds was experimentally discussed. According to the results obtained, the NaCl particles (as the spacer) with the size of 400-600µm maintained their original spherical shape during the compaction and sintering processes...
May 3, 2017: Journal of the Mechanical Behavior of Biomedical Materials
https://www.readbyqxmd.com/read/28494618/machine-learning-methods-to-predict-diabetes-complications
#16
Arianna Dagliati, Simone Marini, Lucia Sacchi, Giulia Cogni, Marsida Teliti, Valentina Tibollo, Pasquale De Cata, Luca Chiovato, Riccardo Bellazzi
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients...
May 1, 2017: Journal of Diabetes Science and Technology
https://www.readbyqxmd.com/read/28420077/recognition-of-the-duration-and-prediction-of-insect-prevalence-of-stored-rough-rice-infested-by-the-red-flour-beetle-tribolium-castaneum-herbst-using-an-electronic-nose
#17
Sai Xu, Zhiyan Zhou, Keliang Li, Sierra Mari Jamir, Xiwen Luo
The purpose of this research is to explore the feasibility of applying an electronic nose for the intelligent monitoring of injurious insects in a stored grain environment. In this study, we employed an electronic nose to sample rough rice that contained three degrees of red flour beetle (Tribolium castaneum Herbst) infestation for different durations-light degree (LD), middle degree (MD), and heavy degree (HD)-and manually investigated the insect situation at the same time. Manual insect situation investigation shows that, in all three rice treatments, the insect amounts gradually decreased after infestation...
April 14, 2017: Sensors
https://www.readbyqxmd.com/read/28418055/application-of-machine-statistical-learning-artificial-intelligence-and-statistical-experimental-design-for-the-modeling-and-optimization-of-methylene-blue-and-cd-ii-removal-from-a-binary-aqueous-solution-by-natural-walnut-carbon
#18
H Mazaheri, M Ghaedi, M H Ahmadi Azqhandi, A Asfaram
Analytical chemists apply statistical methods for both the validation and prediction of proposed models. Methods are required that are adequate for finding the typical features of a dataset, such as nonlinearities and interactions. Boosted regression trees (BRTs), as an ensemble technique, are fundamentally different to other conventional techniques, with the aim to fit a single parsimonious model. In this work, BRT, artificial neural network (ANN) and response surface methodology (RSM) models have been used for the optimization and/or modeling of the stirring time (min), pH, adsorbent mass (mg) and concentrations of MB and Cd(2+) ions (mg L(-1)) in order to develop respective predictive equations for simulation of the efficiency of MB and Cd(2+) adsorption based on the experimental data set...
April 18, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28412664/new-model-for-prediction-binary-mixture-of-antihistamine-decongestant-using-artificial-neural-networks-and-least-squares-support-vector-machine-by-spectrophotometry-method
#19
Shirin Mofavvaz, Mahmoud Reza Sohrabi, Alireza Nezamzadeh-Ejhieh
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated...
July 5, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28384614/how-much-information-about-embryo-implantation-potential-is-included-in-morphokinetic-data-a-prediction-model-based-on-artificial-neural-networks-and-principal-component-analysis
#20
Robert Milewski, Agnieszka Kuczyńska, Bożena Stankiewicz, Waldemar Kuczyński
PURPOSE: The aim of this study was to answer the question of how much information about embryo implantation potential can be obtained from morphokinetic parameters through the creation a predictive model based on morphokinetic information and using advanced data-mining and artificial intelligence methods. MATERIALS AND METHODS: Time-lapse recordings of 610 embryos were included in the analysis. For each embryo, absolute (t2, t3, t4, t5) and relative (cc2 and s2) morphokinetic parameters were collected...
March 2017: Advances in Medical Sciences
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