Comparing the direct normal form and multiple scales methods through frequency detuning.
Ontology highlight
ABSTRACT:
SUBMITTER: Elliott AJ
PROVIDER: S-EPMC6404644 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The purpose of the present studies was to examine the effects of response scale on the observed relationships between protective behavioral strategies (PBS) measures and alcohol-related outcomes. We reasoned that an "absolute frequency" scale (stem: "how many times …"; response scale: 0 times to 11+ times) conflates the frequency of using PBS with the frequency of consuming alcohol; thus, we hypothesized that the use of an absolute frequency response scale would result in positive relationships between types of PBS and alcohol-related outcomes. Alternatively, a "contingent frequency" scale (stem: "When drinking … how often …"; response scale: never to always) does not conflate frequency of alcohol use with use of PBS; therefore, we hypothesized that use of a contingent frequency scale would result in negative relationships between use of PBS and alcohol-related outcomes. Two published measures of PBS were used across studies: the Protective Behavioral Strategies Survey (PBSS) and the Strategy Questionnaire (SQ). Across three studies, we demonstrate that when measured using a contingent frequency response scale, PBS measures relate negatively to alcohol-related outcomes in a theoretically consistent manner; however, when PBS measures were measured on an absolute frequency response scale, they were nonsignificantly or positively related to alcohol-related outcomes. We discuss the implications of these findings for the assessment of PBS.
| S-EPMC5216259 | biostudies-literature
Project description:The estimated mean copy per partition (?) is the essential information from a digital PCR (dPCR) experiment because ? can be used to calculate the target concentration in a sample. However, little information is available how to statistically compare dPCR runs of multiple runs or reduplicates. The comparison of ? values from several runs is a multiple comparison problem, which can be solved using the binary structure of dPCR data. We propose and evaluate two novel methods based on Generalized Linear Models (GLM) and Multiple Ratio Tests (MRT) for comparison of digital PCR experiments. We enriched our MRT framework with computation of simultaneous confidence intervals suitable for comparing multiple dPCR runs. The evaluation of both statistical methods support that MRT is faster and more robust for dPCR experiments performed in large scale. Our theoretical results were confirmed by the analysis of dPCR measurements of dilution series. Both methods were implemented in the dpcR package (v. 0.2) for the open source R statistical computing environment.
| S-EPMC4983647 | biostudies-other
Project description:Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, indicating that multiple processes may mediate divergence in different portions of the genome. We tested this idea by comparing alternative geographic predctors of differentiation in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we described the genomic landscapes across species and modeled conditions that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across species, influenced by varying levels of population structuring and admixture between deserts, and the best-fit models contrasted between the whole genome and partitions along the genome. Both historical and contemporary mechanisms were important in explaining genetic distance, but particularly past and current environments, suggesting that genomic evolution was modulated by climate and habitat There were also different best-ftit models across genomic partitions of the data, indicating that these regions capture different evolutionary histories. These results show that the genomic landscape of differentiation can be associated with alternative geographic factors operating on different portions of the genome, which reflect how heterogeneous patterns of genetic differentiation can evolve across species and genomes.
| S-EPMC9577548 | biostudies-literature
Project description:Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.
| S-EPMC8049288 | biostudies-literature
Project description:With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed and/or weighted networks too, thus properly exploiting richer information. In this work, we contribute to the effort of comparing the different methods for comparing networks and providing a guide for the selection of an appropriate one. First, we review and classify a collection of network comparison methods, highlighting the criteria they are based on and their advantages and drawbacks. The set includes methods requiring known node-correspondence, such as DeltaCon and Cut Distance, as well as methods not requiring a priori known node-correspondence, such as alignment-based, graphlet-based, and spectral methods, and the recently proposed Portrait Divergence and NetLSD. We test the above methods on synthetic networks and we assess their usability and the meaningfulness of the results they provide. Finally, we apply the methods to two real-world datasets, the European Air Transportation Network and the FAO Trade Network, in order to discuss the results that can be drawn from this type of analysis.
| S-EPMC6879644 | biostudies-literature
Project description:Assessment of amyloid deposits is a critical step for the identification of Alzheimer disease (AD) signature in asymptomatic elders. Whether the different amyloid processing methods impacts on the quality of clinico-radiological correlations is still unclear. We directly compared in 155 elderly controls with extensive neuropsychological testing at baseline and 4.5 years follow-up three approaches: (i) operator-dependent standard visual reading, (ii) operator-independent automatic SUVR with four different reference regions, and (iii) novel operator and region of reference-independent automatic Aβ-index. The coefficient of variance was used to examine inter-individual variability for each processing method. Using visually-established amyloid positivity as the gold standard, the area under the receiver operating characteristic curve (ROC) was computed. Linear regression models were used to assess the association between changes in continuous cognitive score and amyloid uptake values. In SUVR analyses, the coefficient of variance varied from 1.718 to 1.762 according to the area of reference and was of - 3.045 for the Aβ-index method. Compared to the visual rating, Aβ-index method showed the largest area under the ROC curve [0.9568 (95% CI 0.9252, 0.98833)]. The best cut-off score was of - 0.3359 with sensitivity and specificity values of 0.97 and 0.83, respectively. Only the Aß-index was related to more severe decrement of cognitive performances [regression coefficient: 9.103 (95% CI 1.148, 17.058)]. The Aβ-index is considered as preferred option in asymptomatic elders, since it is operator-independent, avoids the selection of reference area, is closer to established visual scoring and correlates with the evolution of cognitive performances.
| S-EPMC7542434 | biostudies-literature
Project description:Environmental cues can impact development to elicit distinct phenotypes in the adult. The consequences of phenotypic plasticity can have profound effects on morphology, life cycle, and behavior to increase the fitness of the organism. The molecular mechanisms governing these interactions are beginning to be elucidated in a few cases, such as social insects. Nevertheless, there is a paucity of systems that are amenable to rigorous experimentation, preventing both detailed mechanistic insight and the establishment of a generalizable conceptual framework. The mouth dimorphism of the model nematode Pristionchus pacificus offers the rare opportunity to examine the genetics, genomics, and epigenetics of environmental influence on developmental plasticity. Yet there are currently no easily tunable environmental factors that affect mouth-form ratios and are scalable to large cultures required for molecular biology. Here we present a suite of culture conditions to toggle the mouth-form phenotype of P. pacificus. The effects are reversible, do not require the costly or labor-intensive synthesis of chemicals, and proceed through the same pathways previously examined from forward genetic screens. Different species of Pristionchus exhibit different responses to culture conditions, demonstrating unique gene-environment interactions, and providing an opportunity to study environmental influence on a macroevolutionary scale.
| S-EPMC5543044 | biostudies-literature
Project description:BACKGROUND:Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE:The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS:Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS:Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS:The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.
| S-EPMC7338927 | biostudies-literature
Project description:The increase in incidence of obesity and chronic diseases and their health care costs have raised the importance of quality diet on the health policy agendas. The healthy eating index is an important measure for diet quality which consists of 12 components derived from ratios of dependent variables with distributions hard to specify, measurement errors and excessive zero observations difficult to model parametrically. Hypothesis testing involving data of such nature poses challenges because the widely used multiple comparison procedures such as Hotelling's T(2) test and Bonferroni correction may suffer from substantial loss of efficiency. We propose a marginal rank-based inverse normal transformation approach to normalizing the marginal distribution of the data before employing a multivariate test procedure. Extensive simulation was conducted to demonstrate the ability of the proposed approach to adequately control the type I error rate as well as increase the power of the test, with data particularly from non-symmetric or heavy-tailed distributions. The methods are exemplified with data from a dietary intervention study for type I diabetic children. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
| S-EPMC4938741 | biostudies-literature
Project description:Diffusion through biological gels is crucial for effective drug delivery using nanoparticles. Here, we demonstrate a new method to measure diffusivity over a large range of length scales - from tens of nanometers to tens of micrometers - using photoactivatable fluorescent nanoparticle probes. We have applied this method to investigate the length-scale dependent mobility of nanoparticles in fibrin gels and in sputum from patients with cystic fibrosis (CF). Nanoparticles composed of poly(lactic-co-glycolic acid), with polyethylene glycol coatings to resist bioadhesion, were internally labeled with caged rhodamine to make the particles photoactivatable. We activated particles within a region of sample using brief, targeted exposure to UV light, uncaging the rhodamine and causing the particles in that region to become fluorescent. We imaged the subsequent spatiotemporal evolution in fluorescence intensity and observed the collective particle diffusion over tens of minutes and tens of micrometers. We also performed complementary multiple particle tracking experiments on the same particles, extending significantly the range over which particle motion and its heterogeneity can be observed. In fibrin gels, both methods showed an immobile fraction of particles and a mobile fraction that diffused over all measured length scales. In the CF sputum, particle diffusion was spatially heterogeneous and locally anisotropic but nevertheless typically led to unbounded transport extending tens of micrometers within tens of minutes. These findings provide insight into the mesoscale architecture of these gels and its role in setting their permeability on physiologically relevant length scales, pointing toward strategies for improving nanoparticle drug delivery.
| S-EPMC6037165 | biostudies-literature