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https://www.readbyqxmd.com/read/28728054/an-artificial-neural-network-system-to-identify-alleles-in-reference-electropherograms
#1
Duncan Taylor, Ash Harrison, David Powers
Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i...
July 8, 2017: Forensic Science International. Genetics
https://www.readbyqxmd.com/read/28727724/grandmothering-and-cognitive-resources-are-required-for-the-emergence-of-menopause-and-extensive-post-reproductive-lifespan
#2
Carla Aimé, Jean-Baptiste André, Michel Raymond
Menopause, the permanent cessation of ovulation, occurs in humans well before the end of the expected lifespan, leading to an extensive post-reproductive period which remains a puzzle for evolutionary biologists. All human populations display this particularity; thus, it is difficult to empirically evaluate the conditions for its emergence. In this study, we used artificial neural networks to model the emergence and evolution of allocation decisions related to reproduction in simulated populations. When allocation decisions were allowed to freely evolve, both menopause and extensive post-reproductive life-span emerged under some ecological conditions...
July 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28727384/a-neural-network-based-algorithm-for-predicting-stone-free-status-after-eswl-therapy
#3
Ilker Seckiner, Serap Seckiner, Haluk Sen, Omer Bayrak, Kazim Dogan, Sakip Erturhan
OBJECTIVE: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. MATERIALS AND METHODS: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables...
July 20, 2017: International Braz J Urol: Official Journal of the Brazilian Society of Urology
https://www.readbyqxmd.com/read/28726698/experimental-study-and-artificial-neural-network-modeling-of-tartrazine-removal-by-photocatalytic-process-under-solar-light
#4
Aicha Sebti, Fatiha Souahi, Faroudja Mohellebi, Sadek Igoud
This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO2 concentration, initial organic pollutant concentration and solar radiation intensity are used to train, validate and test the networks...
July 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/28725932/dna-methylation-in-elovl2-and-c1orf132-correctly-predicted-chronological-age-of-individuals-from-three-disease-groups
#5
M Spólnicka, E Pośpiech, B Pepłońska, R Zbieć-Piekarska, Ż Makowska, A Pięta, J Karłowska-Pik, B Ziemkiewicz, M Wężyk, P Gasperowicz, T Bednarczuk, M Barcikowska, C Żekanowski, R Płoski, Wojciech Branicki
Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions...
July 19, 2017: International Journal of Legal Medicine
https://www.readbyqxmd.com/read/28725174/altered-functional-connectivity-following-an-inflammatory-white-matter-injury-in-the-newborn-rat-a-high-spatial-and-temporal-resolution-intrinsic-optical-imaging-study
#6
Edgar Guevara, Wyston C Pierre, Camille Tessier, Luis Akakpo, Irène Londono, Frédéric Lesage, Gregory A Lodygensky
Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28722757/discrimination-of-polygoni-multiflori-radix-and-cynanchi-auriculati-radix-using-ultra-high-performance-liquid-chromatography-fingerprints-and-chemical-pattern-recognition
#7
Lili Sun, Meng Wang, Yali Liu, Huijie Zhang, Yanan Liu, Xiaoliang Ren, Yanru Deng
In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulteration (Cynanchi Auriculati Radix, CAR). The ultra-high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA), and counter-propagation artificial neural network (CP-ANN)...
July 19, 2017: Biomedical Chromatography: BMC
https://www.readbyqxmd.com/read/28722475/ann-qsar-workflow-for-predicting-the-inhibition-of-hiv-1-reverse-transcriptase-by-pyridinone-non-nucleoside-derivatives
#8
Abolfazl Barzegar, Elham Zamani-Gharehchamani, Ali Kadkhodaie-Ilkhchi
AIM: Pyridinone derivatives have high potency against non-nucleoside reverse transcriptase inhibitor (NNRTI)-resistant human immunodeficiency virus type-1 strains. Quantitative structure-activity relationship (QSAR) studies on a series of pyridinone derivatives acting as NNRTIs are very important in designing the next generation of NNRTIs. Methodology & results: The QSAR models were developed using linear (single and forward stepwise) and combined nonlinear artificial neural network (ANN) approaches...
July 19, 2017: Future Medicinal Chemistry
https://www.readbyqxmd.com/read/28722050/database-and-new-models-based-on-a-group-contribution-method-to-predict-the-refractive-index-of-ionic-liquids
#9
Xinxin Wang, Xingmei Lu, Qing Zhou, Yongsheng Zhao, Xiaoqian Li, Suojiang Zhang
Refractive index is one of the important physical properties, which is widely used in separation and purification. In this study, the refractive index data of ILs were collected to establish a comprehensive database, which included about 2138 pieces of data from 1996 to 2014. The Group Contribution-Artificial Neural Network (GC-ANN) model and Group Contribution (GC) method were employed to predict the refractive index of ILs at different temperatures from 283.15 K to 368.15 K. Average absolute relative deviations (AARD) of the GC-ANN model and the GC method were 0...
July 19, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28719829/detection-of-driver-engagement-in-secondary-tasks-from-observed-naturalistic-driving-behavior
#10
Mengqiu Ye, Osama A Osman, Sherif Ishak, Bita Hashemi
Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the impact of different causes of distraction on driving behavior. However, only a few studies attempted to address how some driving behavior attributes could be linked to the cause of distraction. In essence, this study takes advantage of the rich SHRP 2 Naturalistic Driving Study (NDS) database to develop a model for detecting the likelihood of a driver's involvement in secondary tasks from distinctive attributes of driving behavior...
July 15, 2017: Accident; Analysis and Prevention
https://www.readbyqxmd.com/read/28719805/seizure-detection-from-eeg-signals-using-multivariate-empirical-mode-decomposition
#11
Asmat Zahra, Nadia Kanwal, Naveed Ur Rehman, Shoaib Ehsan, Klaus D McDonald-Maier
We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T-F) analysis of non-stationary data sets. We select suitable feature sets based on the multiscale T-F representation of the EEG data via MEMD for the classification purposes. The classification is achieved using the artificial neural networks...
July 8, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28719236/applying-an-artificial-neural-network-model-for-developing-a-severity-score-for-patients-with-hereditary-amyloid-polyneuropathy
#12
Shenia Novis, Felipe Machado, Victor B Costa, Debora Foguel, Marcia W Cruz, José Manoel de Seixas
Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mutations. So far, a scale designed to classify the severity of this disease has not yet been validated. This work proposes the implementation of an artificial neural network (ANN) in order to develop a severity scale for monitoring the disease progression in FAP patients. In order to achieve this goal, relevant symptoms and laboratory findings were collected from 98 Brazilian patients included in THAOS - the Transthyretin Amyloidosis Outcomes Survey...
July 18, 2017: Amyloid: the International Journal of Experimental and Clinical Investigation
https://www.readbyqxmd.com/read/28719235/artificial-neural-network-modelling-of-attr-amyloidosis-is-now-the-time
#13
John L Berk
No abstract text is available yet for this article.
July 18, 2017: Amyloid: the International Journal of Experimental and Clinical Investigation
https://www.readbyqxmd.com/read/28713814/application-of-artificial-neural-network-and-support-vector-machines-in-predicting-metabolizable-energy-in-compound-feeds-for-pigs
#14
Hamed Ahmadi, Markus Rodehutscord
BACKGROUND: In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. METHODS: The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch...
2017: Frontiers in Nutrition
https://www.readbyqxmd.com/read/28713345/experimental-study-and-ann-dual-time-scale-perturbation-model-of-electrokinetic-properties-of-microbiota
#15
Yong Liu, Cristian R Munteanu, Carlos Fernandez-Lozano, Alejandro Pazos, Tao Ran, Zhiliang Tan, Yizun Yu, Chuanshe Zhou, Shaoxun Tang, Humberto González-Díaz
The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μe), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, γ). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N)) to establish a predictive artificial neural network (ANN) model...
2017: Frontiers in Microbiology
https://www.readbyqxmd.com/read/28711805/neural-network-modeling-to-support-an-experimental-study-of-the-delignification-process-of-sugarcane-bagasse-after-alkaline-hydrogen-peroxide-pre-treatment
#16
Isabelle C Valim, Juliana L G Fidalgo, Artur S C Rego, Cecília Vilani, Ana Rosa F A Martins, Brunno F Santos
The present study examines the use of Artificial Neural Networks (ANN) as prediction and fault detection tools for the delignification process of sugarcane bagasse via hydrogen peroxide (H2O2). Experimental conditions varied from 25 to 45°C for temperature and from 1.5% to 7.5% (v/v) for H2O2 concentrations. Analytical results for the delignification were obtained by Fourier Transform Infrared (FT-IR) analysis and used for the ANN training and testing steps, allowing for the development of ANN models. The condition experimentally identified as the most suitable for the delignification process was of 25°C with 4...
July 6, 2017: Bioresource Technology
https://www.readbyqxmd.com/read/28708848/predicting-all-cause-risk-of-30-day-hospital-readmission-using-artificial-neural-networks
#17
Mehdi Jamei, Aleksandr Nisnevich, Everett Wetchler, Sylvia Sudat, Eric Liu
Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists...
2017: PloS One
https://www.readbyqxmd.com/read/28702051/random-forest-based-approach-for-maximum-power-point-tracking-of-photovoltaic-systems-operating-under-actual-environmental-conditions
#18
Hussain Shareef, Ammar Hussein Mutlag, Azah Mohamed
Many maximum power point tracking (MPPT) algorithms have been developed in recent years to maximize the produced PV energy. These algorithms are not sufficiently robust because of fast-changing environmental conditions, efficiency, accuracy at steady-state value, and dynamics of the tracking algorithm. Thus, this paper proposes a new random forest (RF) model to improve MPPT performance. The RF model has the ability to capture the nonlinear association of patterns between predictors, such as irradiance and temperature, to determine accurate maximum power point...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28696170/predicting-ascospore-release-of-monilinia-vaccinii-corymbosi-of-blueberry-with-machine-learning
#19
Dalphy Harteveld, Michael Grant, Jay W Pscheidt, Tobin Peever
Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the US Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar-basis rather than inoculum availability. To establish a prediction model for ascospore release, apothecial development was tracked in three fields, one in western OR and two in northwestern WA in 2015 and 2016...
July 11, 2017: Phytopathology
https://www.readbyqxmd.com/read/28696083/artificial-neural-network-for-prediction-of-in-hospital-mortality-after-open-repair-of-ruptured-abdominal-aortic-aneurysm
#20
Thomas Luebke, Payman Majd, Spyridon N Mylonas, Jan Brunkwall
No abstract text is available yet for this article.
October 2017: Journal of Cardiovascular Surgery
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