153 research outputs found
Participant Experiences in the Environmental Determinants of Diabetes in the Young Study : Common Reasons for Withdrawing
No full textBackground. To characterize participant reasons for withdrawing from a diabetes focused longitudinal clinical observational trial (TEDDY) during the first three study years. Methods. 8677 children were recruited into the TEDDY study. At participant withdrawal staff recorded any reason parents provided for withdrawal. Reasons were categorized into (1) family characteristics and (2) protocol reasons. Families who informed staff of their withdrawal were classified as active withdrawals (AW); families without a final contact were considered passive withdrawals (PW). Results. Withdrawal was highest during the first study year (n = 1220). Most families were AW(n = 1549; 73.4%). PW was more common in the United States (n = 1001; 37.8%) and among young mothers (p = 0.001). The most frequent protocol characteristic was blood draw (55%) and the most common family reason was not having enough time (66%). The blood draw was more common among female participants; being too busy was more common among males. Both reasons were associated with study satisfaction. Conclusions. Results suggest that, for families of children genetically at risk for diabetes, procedures that can be painful/frightening should be used with caution. Study procedures must also be considered for the demands placed on participants. Study satisfaction should be regularly assessed as an indicator of risk for withdrawal.Peer reviewe
Social networks in primates : smart and tolerant species have more efficient networks
No full textE.W., A.W. and the vervet monkeys data were funded Sinergia grant (CRSI33_133040) from the Swiss National Science Foundation to R. Bshary, C. P. van Schaik, and A.W. L.H. and S.F.B. were supported by NSF CAREER Award 0847351 to S.F.B. Date of Acceptance: 03/12/2014Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.Peer reviewe
Pharmacogenetics of ophthalmic topical β-blockers
No full textGlaucoma is the second leading cause of blindness worldwide. The primary glaucoma risk factor is elevated intraocular pressure. Topical β-blockers are affordable and widely used to lower intraocular pressure. Genetic variability has been postulated to contribute to interpersonal differences in efficacy and safety of topical β-blockers. This review summarizes clinically significant polymorphisms that have been identified in the β-adrenergic receptors (ADRB1, ADRB2 and ADRB3). The implications of polymorphisms in CYP2D6 are also discussed. Although the candidate-gene approach has facilitated significant progress in our understanding of the genetic basis of glaucoma treatment response, most drug responses involve a large number of genes, each containing multiple polymorphisms. Genome-wide association studies may yield a more comprehensive set of polymorphisms associated with glaucoma outcomes. An understanding of the genetic mechanisms associated with variability in individual responses to topical β-blockers may advance individualized treatment at a lower cost
Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms
No full textOsman, Onur (Arel Author), Özekes, Serhat (Arel Author)In this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Na < ve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical study. An experimental setup was built for this purpose. 105 motor units were measured. 44 of them belonged to JME group consisting of 9 patients and 61 of them belonged to NC group comprising ten healthy volunteers. k-fold cross validation was applied to train and test the models. ROC curves were drawn for k values of 4, 6, 8 and 10. 100% of detection sensitivity was obtained for DT, NN, and NB classification methods. The lowest FP number, which was obtained by NN, was 5
Determinig the efficacy of mathematical programming approaches for multi-group classification:
No full textManagers have been grappling with the problem of extracting patterns out of the vast database generated by their systems. The advent of powerful information systems in organizations and the consequent agglomeration of vast pool of data since the mid-1980s have created renewed interest in the usefulness of discriminant analysis (DA). Expert systems have come to the aid of managers in their day-to-day decision making with many successful applications in financial planning, sales management, and other areas of business operations (Erenguc and Koehler 1990).
Currently, no comprehensive research study exists that tests the robustness of multi-group classification analysis. Our research aims to bridge the gaps in the existing works and take a step further by extending our study to four-group classification problems. The main purpose of this research is to determine the efficacy of mathematical programming classification models, more specifically, LP methods vis-à-vis statistical approaches such as discriminant analysis (Mahalanobis) and logistic regression, an artificial intelligence (AI) technique such as a neural network, and a non-parametric technique such as k-nearest neighborhood (k-NN) for four-group classification problems. This research also proposes an integrated (hybrid) model that combines a non-parametric classification technique and a LP approach to enhance the overall classification performance. Furthermore, the study extends an existing two-group LP model (Bal et al. 2006) based on the work of (Lam and Moy 1996b) and apply it to four-group classification problems. These models are tested through robust computational experiments under varying data conditions using a financial product example. The characteristics of a real dataset are used to simulate (Monte Carlo method) multiple sample runs for four group classification problems with three continuous independent variables.
The experimental results show that LP approaches in general and the proposed integrated method in particular consistently have lower misclassification rates for most data characteristics. Furthermore, the integrated method utilizes the strengths of both the methods: k-NN and linear programming, thereby considerably improving the classification accuracy.Ph.D.Includes bibliographical references (p. 83-97)by Dinesh R. Pa
Book reviews
No full textThe genus Pholiota, an important genus in forestry since many species are parasitic on woody plants, has been monographed by the author on the basis of numerous collections from the whole of North-western and Central Europe. After the introductory chapters with extensive information on material and methods, an overview is given of the current state of knowledge on the genus and the characters used for delimitation of taxa. The taxonomic part gives an infrageneric classification, followed by keys to the subgenera and species. All accepted taxa are fully described and illustrated with linedrawings. In addition most species are illustrated in colour with at least one, but in many cases even two photographs. Notes are given on ecology and distribution, and all collections studied are cited per country of origin. The discussions are often elaborate and give much additional information as to the status of the taxon versus related species and interpretations in literature. Five new combinations have been made. The book concludes with a long, annotated list of type studies, excluded and doubtful taxa, and a very comprehensive list of references.
Holec’s concept of Pholiota follows in great lines that of Jacobsson (Windahlia 19, 1990) and Noordeloos (Flora agaricina neerlandica, vol. 4,1999) with slight alterations. Kuehneromyces is not included, and also the status of Pholiota albocrenulata, P. oedipus, and P. myosotis is discussed. On species level, a wide species concept is used for example in P. conisans, which includes both forms on wood and on grasses (P. ‘graminis’) The nomenclature of the group of P. aurivella, P. adiposa, and P. cerifera has been adjusted, and follows Noordeloos (l. c.). Within section Spumosa, Holec records besides the known European taxa P. spumosa, P. mixta, and P. highlandensis, a collection of Pholiota brunnescens, originally described from North America, and indicates that more taxa can be expected in this group. The present study is exemplary for how a good monograph should be made: it is very complete and consistent. As such it should be widely used and consulted by everyone working in taxonomy and forestry
Sensorless metal object detection for wireless power transfer using machine learning
No full textPurpose This study aims to realize a sensorless metal object detection (MOD) using machine learning, to prevent the wireless power transfer (WPT) system from the risks of electric discharge and fire accidents caused by foreign metal objects. Design/methodology/approach The data constructed by analyzing the input impedance using the finite element method are used in machine learning. From the loci of the input impedance of systems, the trained neural network (NN), support vector machine and naive Bayes classifier judge if a metal object exists. Then the proposed method is tested by experiments too. Findings In the test using simulated data, all of the three machine learning methods show high accuracy of over 80% for detecting an aluminum cylinder. And in the experimental verifications, the existence of an aluminum cylinder and empty can are successfully identified by a NN. Originality/value This work provides a new sensorless MOD method for WPT using three machine learning methods. And it shows that NNs obtain high accuracy than the others in both simulated and experimental verifications
Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran
No full textPublisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R 2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation.Peer reviewe
Prevalence of metabolic syndrome in patients with psoriasis: a population-based study in the United Kingdom.
No full textIncreasing epidemiological evidence suggests independent associations between psoriasis and cardiovascular and metabolic disease. Our objective was to test the hypothesis that directly assessed psoriasis severity relates to the prevalence of metabolic syndrome and its components. A population-based, cross-sectional study was undertaken using computerized medical records from the Health Improvement Network Study population including individuals in the age group of 45-65 years with psoriasis and practice-matched controls. The diagnosis and extent of psoriasis were determined using provider-based questionnaires. Metabolic syndrome was defined using the National Cholesterol Education Program Adult Treatment Panel III criteria. A total of 44,715 individuals were included: 4,065 with psoriasis and 40,650 controls. In all, 2,044 participants had mild psoriasis (2% body surface area (BSA)), 1,377 had moderate psoriasis (3-10% BSA), and 475 had severe psoriasis (>10% BSA). Psoriasis was associated with metabolic syndrome, adjusted odds ratio (adj. OR 1.41, 95% confidence interval (CI) 1.31-1.51), varying in a "dose-response" manner, from mild (adj. OR 1.22, 95% CI 1.11-1.35) to severe psoriasis (adj. OR 1.98, 95% CI 1.62-2.43). Psoriasis is associated with metabolic syndrome and the association increases with increasing disease severity. Furthermore, associations with obesity, hypertriglyceridemia, and hyperglycemia increase with increasing disease severity independently of other metabolic syndrome components. These findings suggest that screening for metabolic disease should be considered for psoriasis, especially when it is severe
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