Understanding the trends in HIV and hepatitis C prevalence amongst injecting drug users in different settings--implications for intervention impact.

PubMed

Vickerman, Peter; Martin, Natasha K; Hickman, Matthew

2012-06-01

A recent systematic review observed that HIV prevalence amongst injectors is negligible (<1%) below a threshold HCV prevalence of 30%, but thereafter increases with HCV prevalence. We explore whether a model can reproduce these trends, what determines different epidemiological profiles and how this affects intervention impact. An HIV/HCV transmission model was developed. Univariate sensitivity analyses determined whether the model projected a HCV prevalence threshold below which HIV is negligible, and how different behavioural and epidemiological factors affect the threshold. Multivariate uncertainty analyses considered whether the model could reproduce the observed breadth of HIV/HCV epidemics, how specific behavioural patterns produce different epidemic profiles, and how this affects an intervention's impact (reduces injecting risk by 30%). The model projected a HCV prevalence threshold, which varied depending on the heterogeneity in risk, mixing, and injecting duration in a setting. Multivariate uncertainty analyses showed the model could produce the same range of observed HIV/HCV epidemics. Variability in injecting transmission risk, degree of heterogeneity and injecting duration mainly determined different epidemic profiles. The intervention resulted in 50%/28% reduction in HIV incidence/prevalence and 37%/10% reduction in HCV incidence/prevalence over five years. For either infection, greater impact occurred in settings with lower prevalence of that infection and higher prevalence of the other infection. There are threshold levels of HCV prevalence below which HIV risk is negligible but these thresholds are likely to vary by setting. A setting's HIV and HCV prevalence may give insights into IDU risk behaviour and intervention impact. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Identification and reproducibility of dietary patterns in a Danish cohort: the Inter99 study.

PubMed

Lau, Cathrine; Glümer, Charlotte; Toft, Ulla; Tetens, Inge; Carstensen, Bendix; Jørgensen, Torben; Borch-Johnsen, Knut

2008-05-01

We aimed to identify dietary patterns in a Danish adult population and assess the reproducibility of the dietary patterns identified. Baseline data of 3,372 women and 3,191 men (30-60 years old) from the population-based survey Inter99 was used. Food intake, assessed by a FFQ, was aggregated into thirty-four separate food groups. Dietary patterns were identified by principal component analysis. Confirmatory factor analysis and Bland Altman plots were used to assess the reproducibility of the dietary patterns identified. The Bland Altman plots were used as an alternative and new method. Two factors were retained for both women and men, which accounted for 15.1-17.4 % of the total variation. The 'Traditional' pattern was characterised by high loadings ( > or = 0.40) on paté or high-fat meat for sandwiches, mayonnaise salads, red meat, potatoes, butter and lard, low-fat fish, low-fat meat for sandwiches, and sauces. The 'Modern' pattern was characterised by high loadings on vegetables, fruit, mixed vegetable dishes, vegetable oil and vinegar dressing, poultry, and pasta, rice and wheat kernels. Small differences were observed between patterns identified for women and men. The root mean square error approximation from the confirmatory factor analysis was 0.08. The variation observed from the Bland Altman plots of factors from explorative v. confirmative analyses and explorative analyses from two sub-samples was between 18.8 and 47.7 %. Pearson's correlation was >0.89 (P < 0.0001). The reproducibility was better for women than for men. We conclude that the 'Traditional' and 'Modern' dietary patterns identified were reproducible.

Analysing the teleconnection systems affecting the climate of the Carpathian Basin

NASA Astrophysics Data System (ADS)

Kristóf, Erzsébet; Bartholy, Judit; Pongrácz, Rita

2017-04-01

Nowadays, the increase of the global average near-surface air temperature is unequivocal. Atmospheric low-frequency variabilities have substantial impacts on climate variables such as air temperature and precipitation. Therefore, assessing their effects is essential to improve global and regional climate model simulations for the 21st century. The North Atlantic Oscillation (NAO) is one of the best-known atmospheric teleconnection patterns affecting the Carpathian Basin in Central Europe. Besides NAO, we aim to analyse other interannual-to-decadal teleconnection patterns, which might have significant impacts on the Carpathian Basin, namely, the East Atlantic/West Russia pattern, the Scandinavian pattern, the Mediterranean Oscillation, and the North-Sea Caspian Pattern. For this purpose primarily the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA-20C atmospheric reanalysis dataset and multivariate statistical methods are used. The indices of each teleconnection pattern and their correlations with temperature and precipitation will be calculated for the period of 1961-1990. On the basis of these data first the long range (i. e. seasonal and/or annual scale) forecast ability is evaluated. Then, we aim to calculate the same indices of the relevant teleconnection patterns for the historical and future simulations of Coupled Model Intercomparison Project Phase 5 (CMIP5) models and compare them against each other using statistical methods. Our ultimate goal is to examine all available CMIP5 models and evaluate their abilities to reproduce the selected teleconnection systems. Thus, climate predictions for the 21st century for the Carpathian Basin may be improved using the best-performing models among all CMIP5 model simulations.

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

GenePattern | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

GenePattern is a genomic analysis platform that provides access to hundreds of tools for the analysis and visualization of multiple data types. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. A new GenePattern Notebook environment allows users to combine GenePattern analyses with text, graphics, and code to create complete reproducible research narratives.

Reproducible diagnostic metabolites in plasma from typhoid fever patients in Asia and Africa.

PubMed

Näsström, Elin; Parry, Christopher M; Vu Thieu, Nga Tran; Maude, Rapeephan R; de Jong, Hanna K; Fukushima, Masako; Rzhepishevska, Olena; Marks, Florian; Panzner, Ursula; Im, Justin; Jeon, Hyonjin; Park, Seeun; Chaudhury, Zabeen; Ghose, Aniruddha; Samad, Rasheda; Van, Tan Trinh; Johansson, Anders; Dondorp, Arjen M; Thwaites, Guy E; Faiz, Abul; Antti, Henrik; Baker, Stephen

2017-05-09

Salmonella Typhi is the causative agent of typhoid. Typhoid is diagnosed by blood culture, a method that lacks sensitivity, portability and speed. We have previously shown that specific metabolomic profiles can be detected in the blood of typhoid patients from Nepal (Näsström et al., 2014). Here, we performed mass spectrometry on plasma from Bangladeshi and Senegalese patients with culture confirmed typhoid fever, clinically suspected typhoid, and other febrile diseases including malaria. After applying supervised pattern recognition modelling, we could significantly distinguish metabolite profiles in plasma from the culture confirmed typhoid patients. After comparing the direction of change and degree of multivariate significance, we identified 24 metabolites that were consistently up- or down regulated in a further Bangladeshi/Senegalese validation cohort, and the Nepali cohort from our previous work. We have identified and validated a metabolite panel that can distinguish typhoid from other febrile diseases, providing a new approach for typhoid diagnostics.

Functional imaging of cerebral blood flow and glucose metabolism in Parkinson's disease and Huntington's disease.

PubMed

Ma, Yilong; Eidelberg, David

2007-01-01

Brain imaging of cerebral blood flow and glucose metabolism has been playing key roles in describing pathophysiology of Parkinson's disease (PD) and Huntington's disease (HD), respectively. Many biomarkers have been developed in recent years to investigate the abnormality in molecular substrate, track the time course of disease progression, and evaluate the efficacy of novel experimental therapeutics. A growing body of literature has emerged on neurobiology of these two movement disorders in resting states and in response to brain activation tasks. In this paper, we review the latest applications of these approaches in patients and normal volunteers at rest conditions. The discussions focus on brain mapping studies with univariate and multivariate statistical analyses on a voxel basis. In particular, we present data to validate the reproducibility and reliability of unique spatial covariance patterns related with PD and HD.

Characterising the reproducibility and reliability of dietary patterns among Yup'ik Alaska Native people.

PubMed

Ryman, Tove K; Boyer, Bert B; Hopkins, Scarlett; Philip, Jacques; O'Brien, Diane; Thummel, Kenneth; Austin, Melissa A

2015-02-28

FFQ data can be used to characterise dietary patterns for diet-disease association studies. In the present study, we evaluated three previously defined dietary patterns--'subsistence foods', market-based 'processed foods' and 'fruits and vegetables'--among a sample of Yup'ik people from Southwest Alaska. We tested the reproducibility and reliability of the dietary patterns, as well as the associations of these patterns with dietary biomarkers and participant characteristics. We analysed data from adult study participants who completed at least one FFQ with the Center for Alaska Native Health Research 9/2009-5/2013. To test the reproducibility of the dietary patterns, we conducted a confirmatory factor analysis (CFA) of a hypothesised model using eighteen food items to measure the dietary patterns (n 272). To test the reliability of the dietary patterns, we used the CFA to measure composite reliability (n 272) and intra-class correlation coefficients for test-retest reliability (n 113). Finally, to test the associations, we used linear regression (n 637). All factor loadings, except one, in CFA indicated acceptable correlations between foods and dietary patterns (r>0·40), and model-fit criteria were >0·90. Composite and test-retest reliability of the dietary patterns were, respectively, 0·56 and 0·34 for 'subsistence foods', 0·73 and 0·66 for 'processed foods', and 0·72 and 0·54 for 'fruits and vegetables'. In the multi-predictor analysis, the dietary patterns were significantly associated with dietary biomarkers, community location, age, sex and self-reported lifestyle. This analysis confirmed the reproducibility and reliability of the dietary patterns in the present study population. These dietary patterns can be used for future research and development of dietary interventions in this underserved population.

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Characterizing the Reproducibility and Reliability of Dietary Patterns among Yup’ik Alaska Native People

PubMed Central

Ryman, Tove K.; Boyer, Bert B.; Hopkins, Scarlett; Philip, Jacques; O’Brien, Diane; Thummel, Kenneth; Austin, Melissa A.

2015-01-01

Food frequency questionnaire (FFQ) data can be used to characterize dietary patterns for diet-disease association studies. Among a sample of Yup’ik people from Southwest Alaska, we evaluated three previously defined dietary patterns: “subsistence foods” and market-based “processed foods” and “fruits and vegetables”. We tested the reproducibility and reliability of the dietary patterns and tested associations of the patterns with dietary biomarkers and participant characteristics. We analyzed data from adult study participants who completed at least one FFQ with the Center for Alaska Native Health Research 9/2009–5/2013. To test reproducibility we conducted a confirmatory factor analysis (CFA) of a hypothesized model using 18 foods to measure the dietary patterns (n=272). To test the reliability of the dietary patterns, we used CFA to measure the composite reliability (n=272) and intraclass correlation coefficients for test-retest reliability (n=113). Finally, to test associations we used linear regression (n=637). All CFA factor loadings, except one, indicated acceptable correlations between foods and dietary patterns (r > 0.40) and model fit criteria were greater than 0.90. Composite and test-retest reliability of dietary patterns were respectively 0.56 and 0.34 for subsistence foods, 0.73 and 0.66 for processed foods, and 0.72 and 0.54 for fruits and vegetables. In the multi-predictor analysis, dietary patterns were significantly associated with dietary biomarkers, community location, age, sex, and self-reported lifestyle. This analysis confirmed the reproducibility and reliability of the dietary patterns in this study population. These dietary patterns can be used for future research and development of dietary interventions in this underserved population. PMID:25656871

A Western dietary pattern is associated with higher blood pressure in Iranian adolescents.

PubMed

Hojhabrimanesh, Abdollah; Akhlaghi, Masoumeh; Rahmani, Elham; Amanat, Sasan; Atefi, Masoumeh; Najafi, Maryam; Hashemzadeh, Maral; Salehi, Saedeh; Faghih, Shiva

2017-02-01

The dietary determinants of adolescent blood pressure (BP) are not well understood. We determined the association between major dietary patterns and BP in a sample of Iranian adolescents. This cross-sectional study was conducted among a representative sample (n = 557) of Shirazi adolescents aged 12-19 years. Participants' systolic and diastolic BP was measured using a validated oscillometric BP monitor. Usual dietary intakes during the past 12 months were assessed using a valid and reproducible 168-item semiquantitative food frequency questionnaire through face-to-face interviews. Principal component factor analysis was used to identify major dietary patterns based on a set of 25 predefined food groups. Overall, three major dietary patterns were identified, among which only the Western pattern (abundant in soft drinks, sweets and desserts, salt, mayonnaise, tea and coffee, salty snacks, high-fat dairy products, French fries, and red or processed meats) had a significant association with BP. After adjusting for potential confounders in the analysis of covariance models, multivariable adjusted means of the systolic and mean BP of subjects in the highest tertile of the Western pattern score were significantly higher than those in the lowest tertile (for systolic BP: mean difference 6.9 mmHg, P = 0.001; and for mean BP: mean difference 4.2 mmHg, P = 0.003). A similar but statistically insignificant difference was observed in terms of diastolic BP. The findings suggest that a Western dietary pattern is associated with higher BP in Iranian adolescents. However, additional large-scale prospective studies with adequate methodological quality are required to confirm these findings.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

PubMed

Lepre, Jorge; Rice, J Jeremy; Tu, Yuhai; Stolovitzky, Gustavo

2004-05-01

Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Magnetoencephalography with temporal spread imaging to visualize propagation of epileptic activity.

PubMed

Shibata, Sumiya; Matsuhashi, Masao; Kunieda, Takeharu; Yamao, Yukihiro; Inano, Rika; Kikuchi, Takayuki; Imamura, Hisaji; Takaya, Shigetoshi; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke; Mima, Tatsuya; Fukuyama, Hidenao; Mikuni, Nobuhiro; Miyamoto, Susumu

2017-05-01

We describe temporal spread imaging (TSI) that can identify the spatiotemporal pattern of epileptic activity using Magnetoencephalography (MEG). A three-dimensional grid of voxels covering the brain is created. The array-gain minimum-variance spatial filter is applied to an interictal spike to estimate the magnitude of the source and the time (Ta) when the magnitude exceeds a predefined threshold at each voxel. This calculation is performed through all spikes. Each voxel has the mean Ta () and spike number (N sp ), which is the number of spikes whose source exceeds the threshold. Then, a random resampling method is used to determine the cutoff value of N sp for the statistically reproducible pattern of the activity. Finally, all the voxels where the source exceeds the threshold reproducibly shown on the MRI with a color scale representing . Four patients with intractable mesial temporal lobe epilepsy (MTLE) were analyzed. In three patients, the common pattern of the overlap between the propagation and the hypometabolism shown by fluorodeoxyglucose-positron emission tomography (FDG-PET) was identified. TSI can visualize statistically reproducible patterns of the temporal and spatial spread of epileptic activity. TSI can assess the statistical significance of the spatiotemporal pattern based on its reproducibility. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

PubMed Central

Batal, Iyad; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

ERIC Educational Resources Information Center

Hsieh, Fushing; Ferrer, Emilio; Chen, Shu-Chun; Chow, Sy-Miin

2010-01-01

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to…

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

Multivariate temporal pattern analysis applied to the study of rat behavior in the elevated plus maze: methodological and conceptual highlights.

PubMed

Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G

2014-08-30

Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze. Copyright © 2014 Elsevier B.V. All rights reserved.

Reproducible diagnostic metabolites in plasma from typhoid fever patients in Asia and Africa

PubMed Central

Näsström, Elin; Parry, Christopher M; Vu Thieu, Nga Tran; Maude, Rapeephan R; de Jong, Hanna K; Fukushima, Masako; Rzhepishevska, Olena; Marks, Florian; Panzner, Ursula; Im, Justin; Jeon, Hyonjin; Park, Seeun; Chaudhury, Zabeen; Ghose, Aniruddha; Samad, Rasheda; Van, Tan Trinh; Johansson, Anders; Dondorp, Arjen M; Thwaites, Guy E; Faiz, Abul; Antti, Henrik; Baker, Stephen

2017-01-01

Salmonella Typhi is the causative agent of typhoid. Typhoid is diagnosed by blood culture, a method that lacks sensitivity, portability and speed. We have previously shown that specific metabolomic profiles can be detected in the blood of typhoid patients from Nepal (Näsström et al., 2014). Here, we performed mass spectrometry on plasma from Bangladeshi and Senegalese patients with culture confirmed typhoid fever, clinically suspected typhoid, and other febrile diseases including malaria. After applying supervised pattern recognition modelling, we could significantly distinguish metabolite profiles in plasma from the culture confirmed typhoid patients. After comparing the direction of change and degree of multivariate significance, we identified 24 metabolites that were consistently up- or down regulated in a further Bangladeshi/Senegalese validation cohort, and the Nepali cohort from our previous work. We have identified and validated a metabolite panel that can distinguish typhoid from other febrile diseases, providing a new approach for typhoid diagnostics. DOI: http://dx.doi.org/10.7554/eLife.15651.001 PMID:28483042

Suitability of elemental fingerprinting for assessing the geographic origin of pumpkin (Cucurbita pepo var. styriaca) seed oil.

PubMed

Bandoniene, Donata; Zettl, Daniela; Meisel, Thomas; Maneiko, Marija

2013-02-15

An analytical method was developed and validated for the classification of the geographical origin of pumpkin seeds and oil from Austria, China and Russia. The distribution of element traces in pumpkin seed and pumpkin seed oils in relation to the geographical origin of soils of several agricultural farms in Austria was studied in detail. Samples from several geographic origins were taken from parts of the pumpkin, pumpkin flesh, seeds, the oil extracted from the seeds and the oil-extraction cake as well as the topsoil on which the plants were grown. Plants from different geographical origin show variations of the elemental patterns that are significantly large, reproducible over the years and ripeness period and show no significant influence of oil production procedure, to allow to a discrimination of geographical origin. A successful differentiation of oils from different regions in Austria, China and Russia classified with multivariate data analysis is demonstrated. Copyright © 2012 Elsevier Ltd. All rights reserved.

Inferring consistent functional interaction patterns from natural stimulus FMRI data

PubMed Central

Sun, Jiehuan; Hu, Xintao; Huang, Xiu; Liu, Yang; Li, Kaiming; Li, Xiang; Han, Junwei; Guo, Lei

2014-01-01

There has been increasing interest in how the human brain responds to natural stimulus such as video watching in the neuroimaging field. Along this direction, this paper presents our effort in inferring consistent and reproducible functional interaction patterns under natural stimulus of video watching among known functional brain regions identified by task-based fMRI. Then, we applied and compared four statistical approaches, including Bayesian network modeling with searching algorithms: greedy equivalence search (GES), Peter and Clark (PC) analysis, independent multiple greedy equivalence search (IMaGES), and the commonly used Granger causality analysis (GCA), to infer consistent and reproducible functional interaction patterns among these brain regions. It is interesting that a number of reliable and consistent functional interaction patterns were identified by the GES, PC and IMaGES algorithms in different participating subjects when they watched multiple video shots of the same semantic category. These interaction patterns are meaningful given current neuroscience knowledge and are reasonably reproducible across different brains and video shots. In particular, these consistent functional interaction patterns are supported by structural connections derived from diffusion tensor imaging (DTI) data, suggesting the structural underpinnings of consistent functional interactions. Our work demonstrates that specific consistent patterns of functional interactions among relevant brain regions might reflect the brain's fundamental mechanisms of online processing and comprehension of video messages. PMID:22440644

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Reproducing the Photospheric Magnetic Field Evolution During the Rise of Cycle 24 with Flux Transport by Supergranules

NASA Technical Reports Server (NTRS)

Hathaway, David H.; Upton, Lisa

2012-01-01

We simulate the transport of magnetic flux in the Sun s photosphere by an evolving pattern of cellular horizontal flows (supergranules). Characteristics of the simulated flow pattern match observed characteristics including the velocity power spectrum, cell lifetimes, and cell pattern motion in longitude and latitude. Simulations using an average, and north-south symmetric, meridional motion of the cellular pattern produce polar magnetic fields that are too weak in the North and too strong in the South. Simulations using cellular patterns with meridional motions that evolve with the observed changes in strength and north-south asymmetry will be analyzed to see if they reproduce the polar field evolution observed during the rise of Cycle 24.

Reproducible nucleation sites for flux dendrites in MgB 2

NASA Astrophysics Data System (ADS)

Johansen, T. H.; Shantsev, D. V.; Olsen, Å. A. F.; Roussel, M.; Pan, A. V.; Dou, S. X.

2007-12-01

Magneto-optical imaging was used to study dendritic flux penetration in films of MgB 2. By repeating experiments under the same external conditions, reproducible features were seen in the pattern formation; dendrites tend to nucleate from fixed locations along the edge. However, their detailed structure deeper inside the film is never reproduced. The reproducibility in nucleation sites is explained as a result of edge roughness causing field hot spots.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

Application of the aberration ring test (ARTEMIS) to determine lens quality and predict its lithographic performance

NASA Astrophysics Data System (ADS)

Moers, Marco H. P.; van der Laan, Hans; Zellenrath, Mark; de Boeij, Wim; Beaudry, Neil A.; Cummings, Kevin D.; van Zwol, Adriaan; Brecht, Arthur; Willekers, Rob

2001-09-01

ARTEMISTM (Aberration Ring Test Exposed at Multiple Illumination Settings) is a technique to determine in-situ, full-field, low and high order lens aberrations. In this paper we are analyzing the ARTEMISTM data of PAS5500/750TM DUV Step & Scan systems and its use as a lithographic prediction tool. ARTEMISTM is capable of determining Zernike coefficients up to Z25 with a 3(sigma) reproducibility range from 1.5 to 4.5 nm depending on the aberration type. 3D electric field simulations, that take the extended geometry of the phase shift feature into account, have been used for an improved treatment of the extraction of the spherical Zernike coefficients. Knowledge of the extracted Zernike coefficients allows an accurate prediction of the lithographic performance of the scanner system. This ability is demonstrated for a two bar pattern and an isolation pattern. The RMS difference between the ARTEMISTM-based lithographic prediction and the lithographic measurement is 2.5 nm for the two bar pattern and 3 nm for the isolation pattern. The 3(sigma) reproducibility of the prediction for the two bar pattern is 2.5 nm and 1 nm for the isolation pattern. This is better than the reproducibility of the lithographic measurements themselves.

Reproducible, high performance patch antenna array apparatus and method of fabrication

DOEpatents

Strassner, II, Bernd H.

2007-01-23

A reproducible, high-performance patch antenna array apparatus includes a patch antenna array provided on a unitary dielectric substrate, and a feed network provided on the same unitary substrate and proximity coupled to the patch antenna array. The reproducibility is enhanced by using photolithographic patterning and etching to produce both the patch antenna array and the feed network.

Characterization of a commercialized SERS-active substrate and its application to the identification of intact Bacillus endospores

NASA Astrophysics Data System (ADS)

Alexander, Troy A.; Le, Dianna M.

2007-06-01

Surface-enhanced-Raman-spectroscopy (SERS) can be made an attractive approach for the identification of Raman-active compounds and biological materials (i.e., toxins, viruses, or intact bacterial cells or spores) through development of reproducible, spatially uniform SERS-active substrates. Recently, reproducible (from substrate to substrate), spatially homogeneous (over large areas) SERS-active substrates have been commercialized and are now available in the marketplace. Scanning electron microscopy and high-resolution, tapping-mode atomic force microscopy have been used to analyze these novel plasmonic surfaces for topographical consistency. Additionally, we have assessed, by wavelength-tunable microreflectance spectrometry, the spatial distribution of the localized surface plasmon resonance (LSPR) across a single substrate surface as well as the LSPR λMAX variance from substrate to substrate. These analyses reveal that these surfaces are topologically uniform with small LSPR variance from substrate to substrate. Further, we have utilized these patterned surfaces to acquire SERS spectral signatures of four intact, genetically distinct Bacillus spore species cultivated under identical growth conditions. Salient spectral signature features make it possible to discriminate among these genetically distinct spores. Additionally, partial least squares, a multivariate calibration method, has been used to develop personal-computer-borne algorithms useful for classification of unknown spore samples based solely on SERS spectral signatures. To our knowledge, this is the first report detailing application of these commercially available SERS-active substrates to identification of intact Bacillus spores.

Methods to increase reproducibility in differential gene expression via meta-analysis

PubMed Central

Sweeney, Timothy E.; Haynes, Winston A.; Vallania, Francesco; Ioannidis, John P.; Khatri, Purvesh

2017-01-01

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size. PMID:27634930

The Role of Pattern Goodness in the Reproduction of Backward Masked Patterns

ERIC Educational Resources Information Center

Bell, Herbert H.; Handel, Stephen

1976-01-01

The purpose of the present work was to investigate the relation between pattern goodness and accuracy of reproduction in backward masking. It may be hypothesized that good patterns, being easier to encode as wholes, will be reproduced more easily than poorer patterns. Four experiments were performed. (Author)

Two modes of the silk road pattern and their interannual variability simulated by LASG/IAP AGCM SAMIL2.0

NASA Astrophysics Data System (ADS)

Song, Fengfei; Zhou, Tianjun; Wang, Lu

2013-05-01

In this study, two modes of the Silk Road pattern were investigated using NCEP2 reanalysis data and the simulation produced by Spectral Atmospheric Circulation Model of IAP LASG, Version 2 (SAMIL2.0) that was forced by SST observation data. The horizontal distribution of both modes were reasonably reproduced by the simulation, with a pattern correlation coefficient of 0.63 for the first mode and 0.62 for the second mode. The wave train was maintained by barotropic energy conversion (denoted as CK) and baroclinic energy conversion (denoted as CP) from the mean flow. The distribution of CK was dominated by its meridional component (CK y ) in both modes. When integrated spatially, CK y was more efficient than its zonal component (CK x ) in the first mode but less in the second mode. The distribution and efficiency of CK were not captured well by SAMIL2.0. However, the model performed reasonably well at reproducing the distribution and efficiency of CP in both modes. Because CP is more efficient than CK, the spatial patterns of the Silk Road pattern were well reproduced. Interestingly, the temporal phase of the second mode was well captured by a single-member simulation. However, further analysis of other ensemble runs demonstrated that the successful reproduction of the temporal phase was a result of internal variability rather than a signal of SST forcing. The analysis shows that the observed temporal variations of both CP and CK were poorly reproduced, leading to the low accuracy of the temporal phase of the Silk Road pattern in the simulation.

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

Breathing simulator of workers for respirator performance test.

PubMed

Yuasa, Hisashi; Kumita, Mikio; Honda, Takeshi; Kimura, Kazushi; Nozaki, Kosuke; Emi, Hitoshi; Otani, Yoshio

2015-01-01

Breathing machines are widely used to evaluate respirator performance but they are capable of generating only limited air flow patterns, such as, sine, triangular and square waves. In order to evaluate the respirator performance in practical use, it is desirable to test the respirator using the actual breathing patterns of wearers. However, it has been a difficult task for a breathing machine to generate such complicated flow patterns, since the human respiratory volume changes depending on the human activities and workload. In this study, we have developed an electromechanical breathing simulator and a respiration sampling device to record and reproduce worker's respiration. It is capable of generating various flow patterns by inputting breathing pattern signals recorded by a computer, as well as the fixed air flow patterns. The device is equipped with a self-control program to compensate the difference in inhalation and exhalation volume and the measurement errors on the breathing flow rate. The system was successfully applied to record the breathing patterns of workers engaging in welding and reproduced the breathing patterns.

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

Reproducing the Photospheric Magnetic Field Evolution during the Rise of Cycle 24 with Flux Transport by Supergranules

NASA Technical Reports Server (NTRS)

Hathaway, David; Upton, Lisa

2012-01-01

We simulate the transport of magnetic flux in the Sun s photosphere by an evolving pattern of cellular horizontal flows (supergranules). Characteristics of the simulated flow pattern can match observed characteristics including the velocity power spectrum, cell lifetimes, and cell motions in longitude and latitude. Simulations using an average, and north-south symmetric, meridional motion of the cellular pattern produce polar magnetic fields that are too weak in the North and too strong in the South. Simulations using cellular patterns with meridional motions that evolve with the observed changes in strength and north-south asymmetry will be analyzed to see if they reproduce the polar field evolution observed during the rise of Cycle 24.

Reproducibility of Search Strategies Is Poor in Systematic Reviews Published in High-Impact Pediatrics, Cardiology and Surgery Journals: A Cross-Sectional Study

PubMed Central

2016-01-01

Background A high-quality search strategy is considered an essential component of systematic reviews but many do not contain reproducible search strategies. It is unclear if low reproducibility spans medical disciplines, is affected by librarian/search specialist involvement or has improved with increased awareness of reporting guidelines. Objectives To examine the reporting of search strategies in systematic reviews published in Pediatrics, Surgery or Cardiology journals in 2012 and determine rates and predictors of including a reproducible search strategy. Methods We identified all systematic reviews published in 2012 in the ten highest impact factor journals in Pediatrics, Surgery and Cardiology. Each search strategy was coded to indicate what elements were reported and whether the overall search was reproducible. Reporting and reproducibility rates were compared across disciplines and we measured the influence of librarian/search specialist involvement, discipline or endorsement of a reporting guideline on search reproducibility. Results 272 articles from 25 journals were included. Reporting of search elements ranged widely from 91% of articles naming search terms to 33% providing a full search strategy and 22% indicating the date the search was executed. Only 22% of articles provided at least one reproducible search strategy and 13% provided a reproducible strategy for all databases searched in the article. Librarians or search specialists were reported as involved in 17% of articles. There were strong disciplinary differences on the reporting of search elements. In the multivariable analysis, only discipline (Pediatrics) was a significant predictor of the inclusion of a reproducible search strategy. Conclusions Despite recommendations to report full, reproducible search strategies, many articles still do not. In addition, authors often report a single strategy as covering all databases searched, further decreasing reproducibility. Further research is needed to determine how disciplinary culture may encourage reproducibility and the role that journal editors and peer reviewers could play. PMID:27669416

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Understanding Human Motion Skill with Peak Timing Synergy

NASA Astrophysics Data System (ADS)

Ueno, Ken; Furukawa, Koichi

The careful observation of motion phenomena is important in understanding the skillful human motion. However, this is a difficult task due to the complexities in timing when dealing with the skilful control of anatomical structures. To investigate the dexterity of human motion, we decided to concentrate on timing with respect to motion, and we have proposed a method to extract the peak timing synergy from multivariate motion data. The peak timing synergy is defined as a frequent ordered graph with time stamps, which has nodes consisting of turning points in motion waveforms. A proposed algorithm, PRESTO automatically extracts the peak timing synergy. PRESTO comprises the following 3 processes: (1) detecting peak sequences with polygonal approximation; (2) generating peak-event sequences; and (3) finding frequent peak-event sequences using a sequential pattern mining method, generalized sequential patterns (GSP). Here, we measured right arm motion during the task of cello bowing and prepared a data set of the right shoulder and arm motion. We successfully extracted the peak timing synergy on cello bowing data set using the PRESTO algorithm, which consisted of common skills among cellists and personal skill differences. To evaluate the sequential pattern mining algorithm GSP in PRESTO, we compared the peak timing synergy by using GSP algorithm and the one by using filtering by reciprocal voting (FRV) algorithm as a non time-series method. We found that the support is 95 - 100% in GSP, while 83 - 96% in FRV and that the results by GSP are better than the one by FRV in the reproducibility of human motion. Therefore we show that sequential pattern mining approach is more effective to extract the peak timing synergy than non-time series analysis approach.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

NUCLEOSYNTHESIS IN HIGH-ENTROPY HOT BUBBLES OF SUPERNOVAE AND ABUNDANCE PATTERNS OF EXTREMELY METAL-POOR STARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Izutani, Natsuko; Umeda, Hideyuki, E-mail: izutani@astron.s.u-tokyo.ac.j, E-mail: umeda@astron.s.u-tokyo.ac.j

2010-09-01

There have been suggestions that the abundance of extremely metal-poor (EMP) stars can be reproduced by hypernovae (HNe), not by normal supernovae (SNe). However, recently it was also suggested that if the innermost neutron-rich or proton-rich matter is ejected, the abundance patterns of ejected matter are changed, and normal SNe may also reproduce the observations of EMP stars. In this Letter, we calculate explosive nucleosynthesis with various Y {sub e} and entropy, and investigate whether normal SNe with this innermost matter, which we call the 'hot-bubble' component, can reproduce the abundance of EMP stars. We find that neutron-rich (Y {submore » e} = 0.45-0.49) and proton-rich (Y {sub e} = 0.51-0.55) matter can increase Zn/Fe and Co/Fe ratios as observed, but tend to overproduce other Fe-peak elements. In addition, we find that if slightly proton-rich matter with 0.50 {<=} Y {sub e} < 0.501 with s/k {sub b} {approx} 15-40 is ejected as much as {approx}0.06 M {sub sun}, even normal SNe can reproduce the abundance of EMP stars, though it requires fine-tuning of Y {sub e}. On the other hand, HNe can more easily reproduce the observations of EMP stars without fine-tuning. Our results imply that HNe are the most likely origin of the abundance pattern of EMP stars.« less

Quantifying patterns of research interest evolution

NASA Astrophysics Data System (ADS)

Jia, Tao; Wang, Dashun; Szymanski, Boleslaw

Changing and shifting research interest is an integral part of a scientific career. Despite extensive investigations of various factors that influence a scientist's choice of research topics, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of extensive publication records, finding that research interest change follows a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research interest evolution. We develop a random walk based model, which adequately reproduces our empirical observations. Our study presents one of the first quantitative analyses of macroscopic patterns governing research interest change, documenting a high degree of regularity underlying scientific research and individual careers.

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

NASA Astrophysics Data System (ADS)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Acidified pressurized hot water for the continuous extraction of cadmium and lead from plant materials prior to ETAAS

NASA Astrophysics Data System (ADS)

Morales-Muñoz, S.; Luque-García, J. L.; Luque de Castro, M. D.

2003-01-01

Acidified and pressurized hot water is proposed for the continuous leaching of Cd and Pb from plants prior to determination by electrothermal atomic absorption spectrometry. Beech leaves (a certified reference material—CRM 100—where the analytes were not certified) were used for optimizing the method by a multivariate approach. The samples (0.5 g) were subjected to dynamic extraction with water modified with 1% v/v HNO 3 at 250 °C as leachant. A kinetics study was performed in order to know the pattern of the extraction process. The method was validated with a CRM (olive leaves, 062 from the BCR) where the analytes had been certified. The agreement between the certified values and those found using the proposed method demonstrates its usefulness. The repeatability and within-laboratory reproducibility were 3.7 and 2.3% for Cd and 1.04% and 6.3% for Pb, respectively. The precision of the method, together with its efficiency, rapidity, and environmental acceptability, makes it a good alternative for the determination of trace metals in plant material.

Fluorescence-based assay as a new screening tool for toxic chemicals

PubMed Central

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-01-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients. PMID:27653274

Fluorescence-based assay as a new screening tool for toxic chemicals.

PubMed

Moczko, Ewa; Mirkes, Evgeny M; Cáceres, César; Gorban, Alexander N; Piletsky, Sergey

2016-09-22

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

Fluorescence-based assay as a new screening tool for toxic chemicals

NASA Astrophysics Data System (ADS)

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-09-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

The Origin of Species by Means of Mathematical Modelling.

PubMed

Bessonov, Nikolai; Reinberg, Natalia; Banerjee, Malay; Volpert, Vitaly

2018-04-30

Darwin described biological species as groups of morphologically similar individuals. These groups of individuals can split into several subgroups due to natural selection, resulting in the emergence of new species. Some species can stay stable without the appearance of a new species, some others can disappear or evolve. Some of these evolutionary patterns were described in our previous works independently of each other. In this work we have developed a single model which allows us to reproduce the principal patterns in Darwin's diagram. Some more complex evolutionary patterns are also observed. The relation between Darwin's definition of species, stated above, and Mayr's definition of species (group of individuals that can reproduce) is also discussed.

Breathing simulator of workers for respirator performance test

PubMed Central

YUASA, Hisashi; KUMITA, Mikio; HONDA, Takeshi; KIMURA, Kazushi; NOZAKI, Kosuke; EMI, Hitoshi; OTANI, Yoshio

2014-01-01

Breathing machines are widely used to evaluate respirator performance but they are capable of generating only limited air flow patterns, such as, sine, triangular and square waves. In order to evaluate the respirator performance in practical use, it is desirable to test the respirator using the actual breathing patterns of wearers. However, it has been a difficult task for a breathing machine to generate such complicated flow patterns, since the human respiratory volume changes depending on the human activities and workload. In this study, we have developed an electromechanical breathing simulator and a respiration sampling device to record and reproduce worker’s respiration. It is capable of generating various flow patterns by inputting breathing pattern signals recorded by a computer, as well as the fixed air flow patterns. The device is equipped with a self-control program to compensate the difference in inhalation and exhalation volume and the measurement errors on the breathing flow rate. The system was successfully applied to record the breathing patterns of workers engaging in welding and reproduced the breathing patterns. PMID:25382381

Natural progression of blood-induced joint damage in patients with haemophilia: clinical relevance and reproducibility of three-dimensional gait analysis.

PubMed

Lobet, S; Detrembleur, C; Francq, B; Hermans, C

2010-09-01

A major complication in haemophilia is the destruction of joint cartilage because of recurrent intraarticular and intramuscular bleeds. Therefore, joint assessment is critical to quantify the extent of joint damage, which has traditionally been evaluated using both radiological and clinical joint scores. Our study aimed to evaluate the natural progression of haemophilic arthopathy using three-dimensional gait analysis (3DGA) and to assess the reproducibility of this technique. We hypothesized that the musculoskeletal function was relatively stable in patients with haemophilia. Eighteen adults with established haemophilic arthropathies were evaluated twice by 3DGA (mean follow-up: 18 +/- 5 weeks). Unexpectedly, our findings revealed infraclinical deterioration of gait pattern, characterized by a 3.2% decrease in the recovery index, which is indicative of the subject's ability to save energy while walking. A tendency towards modification of segmental joint function was also observed. Gait analysis was sufficiently reproducible with regards to spatiotemporal parameters as well as kinetic, mechanical and energetic gait variables. The kinematic variables were reproducible in both the sagittal and frontal planes. In conclusion, 3DGA is a reproducible tool to assess abnormal gait patterns and monitor natural disease progression in haemophilic patients.

Benchmarking contactless acquisition sensor reproducibility for latent fingerprint trace evidence

NASA Astrophysics Data System (ADS)

Hildebrandt, Mario; Dittmann, Jana

2015-03-01

Optical, nano-meter range, contactless, non-destructive sensor devices are promising acquisition techniques in crime scene trace forensics, e.g. for digitizing latent fingerprint traces. Before new approaches are introduced in crime investigations, innovations need to be positively tested and quality ensured. In this paper we investigate sensor reproducibility by studying different scans from four sensors: two chromatic white light sensors (CWL600/CWL1mm), one confocal laser scanning microscope, and one NIR/VIS/UV reflection spectrometer. Firstly, we perform an intra-sensor reproducibility testing for CWL600 with a privacy conform test set of artificial-sweat printed, computer generated fingerprints. We use 24 different fingerprint patterns as original samples (printing samples/templates) for printing with artificial sweat (physical trace samples) and their acquisition with contactless sensory resulting in 96 sensor images, called scan or acquired samples. The second test set for inter-sensor reproducibility assessment consists of the first three patterns from the first test set, acquired in two consecutive scans using each device. We suggest using a simple feature space set in spatial and frequency domain known from signal processing and test its suitability for six different classifiers classifying scan data into small differences (reproducible) and large differences (non-reproducible). Furthermore, we suggest comparing the classification results with biometric verification scores (calculated with NBIS, with threshold of 40) as biometric reproducibility score. The Bagging classifier is nearly for all cases the most reliable classifier in our experiments and the results are also confirmed with the biometric matching rates.

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...

Visualizing frequent patterns in large multivariate time series

NASA Astrophysics Data System (ADS)

Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

2011-01-01

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback

PubMed Central

Cisler, Josh M.; Bush, Keith; James, G. Andrew; Smitherman, Sonet; Kilts, Clinton D.

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD. PMID:26241958

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback.

PubMed

Cisler, Josh M; Bush, Keith; James, G Andrew; Smitherman, Sonet; Kilts, Clinton D

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD.

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

PubMed Central

Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

2015-01-01

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

A Theoretical Model of Jigsaw-Puzzle Pattern Formation by Plant Leaf Epidermal Cells.

PubMed

Higaki, Takumi; Kutsuna, Natsumaro; Akita, Kae; Takigawa-Imamura, Hisako; Yoshimura, Kenji; Miura, Takashi

2016-04-01

Plant leaf epidermal cells exhibit a jigsaw puzzle-like pattern that is generated by interdigitation of the cell wall during leaf development. The contribution of two ROP GTPases, ROP2 and ROP6, to the cytoskeletal dynamics that regulate epidermal cell wall interdigitation has already been examined; however, how interactions between these molecules result in pattern formation remains to be elucidated. Here, we propose a simple interface equation model that incorporates both the cell wall remodeling activity of ROP GTPases and the diffusible signaling molecules by which they are regulated. This model successfully reproduces pattern formation observed in vivo, and explains the counterintuitive experimental results of decreased cellulose production and increased thickness. Our model also reproduces the dynamics of three-way cell wall junctions. Therefore, this model provides a possible mechanism for cell wall interdigitation formation in vivo.

Does Melody Assist in the Reproduction of Novel Rhythm Patterns?

ERIC Educational Resources Information Center

Kinney, Daryl W.; Forsythe, Jere L.

2013-01-01

We examined music education majors' ability to reproduce rhythmic stimuli presented in melody and rhythm only conditions. Participants reproduced rhythms of two-measure music examples by immediately echo-performing through a method of their choosing (e.g., clapping, tapping, vocalizing). Forty examples were presented in melody and rhythm only…

Physical vs. photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis

PubMed Central

Mishra, Gautam; Easton, Christopher D.; McArthur, Sally L.

2009-01-01

Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microcopy (AFM) imaging, Time-of-Flight Static Secondary Ion Mass Spectrometry (ToF-SSIMS) imaging and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including Principal Component Analysis (PCA) and Region of Interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complimentary characterization and analytical techniques during the fabrication plasma polymer micropatterns. PMID:19950941

Multivariate pattern analysis of fMRI: the early beginnings.

PubMed

Haxby, James V

2012-08-15

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application. Copyright © 2012 Elsevier Inc. All rights reserved.

SU-E-J-227: Breathing Pattern Consistency and Reproducibility: Comparative Analysis for Supine and Prone Body Positioning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laugeman, E; Weiss, E; Chen, S

2014-06-01

Purpose: Evaluate and compare the cycle-to-cycle consistency of breathing patterns and their reproducibility over the course of treatment, for supine and prone positioning. Methods: Respiratory traces from 25 patients were recorded for sequential supine/prone 4DCT scans acquired prior to treatment, and during the course of the treatment (weekly or bi-weekly). For each breathing cycle, the average(AVE), end-of-exhale(EoE) and end-of-inhale( EoI) locations were identified using in-house developed software. In addition, the mean values and variations for the above quantities were computed for each breathing trace. F-tests were used to compare the cycle-to-cycle consistency of all pairs of sequential supine and pronemore » scans. Analysis of variances was also performed using population means for AVE, EoE and EoI to quantify differences between the reproducibility of prone and supine respiration traces over the treatment course. Results: Consistency: Cycle-to-cycle variations are less in prone than supine in the pre-treatment and during-treatment scans for AVE, EoE and EoI points, for the majority of patients (differences significant at p<0.05). The few cases where the respiratory pattern had more variability in prone appeared to be random events. Reproducibility: The reproducibility of breathing patterns (supine and prone) improved as treatment progressed, perhaps due to patients becoming more comfortable with the procedure. However, variability in supine position continued to remain significantly larger than in prone (p<0.05), as indicated by the variance analysis of population means for the pretreatment and subsequent during-treatment scans. Conclusions: Prone positioning stabilizes breathing patterns in most subjects investigated in this study. Importantly, a parallel analysis of the same group of patients revealed a tendency towards increasing motion amplitude of tumor targets in prone position regardless of their size or location; thus, the choice for body positioning during radiation therapy will have to consider the clinical relevance of the two opposing trends - breathing consistency and motion amplitude.« less

A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

PubMed

Park, Sung Hee; Lee, Ji Young; Kim, Sangsoo

2011-01-01

Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects. This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3). The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.

Expression of p53, p21 and cyclin D1 in penile cancer: p53 predicts poor prognosis.

PubMed

Gunia, Sven; Kakies, Christoph; Erbersdobler, Andreas; Hakenberg, Oliver W; Koch, Stefan; May, Matthias

2012-03-01

To evaluate the role of p53, p21 and cyclin D1 expression in patients with penile cancer (PC). Paraffin-embedded tissues from PC specimens from six pathology departments were subjected to a central histopathological review performed by one pathologist. The tissue microarray technique was used for immunostaining which was evaluated by two independent pathologists and correlated with cancer-specific survival (CSS). κ-statistics were used to assess interobserver variability. Uni- and multivariable Cox proportional hazards analysis was applied to assess the independent effects of several prognostic factors on CSS over a median of 32 months (IQR 6-66 months). Specimens and clinical data from 110 men treated surgically for primary PC were collected. p53 staining was positive in 30 and negative in 62 specimens. κ-statistics showed substantial interobserver reproducibility of p53 staining evaluation (κ=0.73; p<0.001). The 5-year CSS rate for the entire study cohort was 74%. Five-year CSS was 84% in p53-negative and 51% in p53-positive PC patients (p=0.003). Multivariable analysis showed p53 (HR=3.20; p=0.041) and pT-stage (HR=4.29; p<0.001) as independent significant prognostic factors for CSS. Cyclin D1 and p21 expression were not correlated with survival. However, incorporating p21 into a multivariable Cox model did contribute to improved model quality for predicting CSS. In patients with PC, the expression of p53 in the primary tumour specimen can be reproducibly assessed and is negatively associated with cancer specific survival.

Detection and identification of bacteria in a juice matrix with Fourier transform-near infrared spectroscopy and multivariiate analysis.

PubMed

Rodriguez-Saona, L E; Khambaty, F M; Fry, F S; Dubois, J; Calvey, E M

2004-11-01

The use of Fourier transform-near infrared (FT-NIR) spectroscopy combined with multivariate pattern recognition techniques was evaluated to address the need for a fast and senisitive method for the detection of bacterial contamination in liquids. The complex cellular composition of bacteria produces FT-NIR vibrational transitions (overtone and combination bands), forming the basis for identification and subtyping. A database including strains of Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Bacillus cereus, and Bacillus thuringiensis was built, with special care taken to optimize sample preparation. The bacterial cells were treated with 70% (vol/vol) ethanolto enhance safe handling of pathogenic strains and then concentrated on an aluminum oxide membrane to obtain a thin bacterial film. This simple membrane filtration procedure generated reproducible FT-NIR spectra that allowed for the rapid discrimination among closely related strains. Principal component analysis and soft independent modeling of class analogy of transformed spectra in the region 5,100 to 4,400 cm(-1) were able to discriminate between bacterial species. Spectroscopic analysis of apple juices inoculated with different strains of E. coli at approximately 10(5) CFU/ml showed that FT-NIR spectralfeatures are consistent with bacterial contamination and soft independent modeling of class analogy correctly predicted the identity of the contaminant as strains of E. coli. FT-NIR in conjunction with multivariate techniques can be used for the rapid and accurate evaluation of potential bacterial contamination in liquids with minimal sample manipulation, and hence limited exposure of the laboratory worker to the agents.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

A gene network model accounting for development and evolution of mammalian teeth

PubMed Central

Salazar-Ciudad, Isaac; Jernvall, Jukka

2002-01-01

Generation of morphological diversity remains a challenge for evolutionary biologists because it is unclear how an ultimately finite number of genes involved in initial pattern formation integrates with morphogenesis. Ideally, models used to search for the simplest developmental principles on how genes produce form should account for both developmental process and evolutionary change. Here we present a model reproducing the morphology of mammalian teeth by integrating experimental data on gene interactions and growth into a morphodynamic mechanism in which developing morphology has a causal role in patterning. The model predicts the course of tooth-shape development in different mammalian species and also reproduces key transitions in evolution. Furthermore, we reproduce the known expression patterns of several genes involved in tooth development and their dynamics over developmental time. Large morphological effects frequently can be achieved by small changes, according to this model, and similar morphologies can be produced by different changes. This finding may be consistent with why predicting the morphological outcomes of molecular experiments is challenging. Nevertheless, models incorporating morphology and gene activity show promise for linking genotypes to phenotypes. PMID:12048258

Dietary patterns and changes in body weight in women.

PubMed

Schulze, Matthias B; Fung, Teresa T; Manson, Joann E; Willett, Walter C; Hu, Frank B

2006-08-01

Our objective was to examine the association between adherence to dietary patterns and weight change in women. Women (51,670, 26 to 46 years old) in the Nurses' Health Study II were followed from 1991 to 1999. Dietary intake and body weight were ascertained in 1991, 1995, and 1999. A Western pattern, characterized by high intakes of red and processed meats, refined grains, sweets and desserts, and potatoes, and a prudent pattern, characterized by high intakes of fruits, vegetables, whole grains, fish, poultry, and salad dressing, were identified with principal component analysis, and associations between patterns and change in body weight were estimated. Women who increased their Western pattern score had greater weight gain (multivariate adjusted means, 4.55 kg for 1991 to 1995 and 2.86 kg for 1995 to 1999) than women who decreased their Western pattern score (2.70 and 1.37 kg for the two time periods), adjusting for baseline lifestyle and dietary confounders and changes in confounders over time (p < 0.001 for both time periods). Furthermore, among women who increased their prudent pattern score, weight gain was smaller (multivariate-adjusted means, 1.93 kg for 1991 to 1995 and 0.66 kg for 1995 to 1999) than among women who decreased their prudent pattern score (4.83 and 3.35 kg for the two time periods) (p < 0.001). The largest weight gain between 1991 and 1995 and between 1995 and 1999 was observed among women who decreased their prudent pattern score while increasing their Western pattern score (multivariate adjusted means, 6.80 and 4.99 kg), whereas it was smallest for the opposite change in patterns (0.87 and -0.64 kg) (p < 0.001). Adoption of a Western dietary pattern is associated with larger weight gain in women, whereas a prudent dietary pattern may facilitate weight maintenance.

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Trends in the sand: Directional evolution in the shell shape of recessing scallops (Bivalvia: Pectinidae).

PubMed

Sherratt, Emma; Alejandrino, Alvin; Kraemer, Andrew C; Serb, Jeanne M; Adams, Dean C

2016-09-01

Directional evolution is one of the most compelling evolutionary patterns observed in macroevolution. Yet, despite its importance, detecting such trends in multivariate data remains a challenge. In this study, we evaluate multivariate evolution of shell shape in 93 bivalved scallop species, combining geometric morphometrics and phylogenetic comparative methods. Phylomorphospace visualization described the history of morphological diversification in the group; revealing that taxa with a recessing life habit were the most distinctive in shell shape, and appeared to display a directional trend. To evaluate this hypothesis empirically, we extended existing methods by characterizing the mean directional evolution in phylomorphospace for recessing scallops. We then compared this pattern to what was expected under several alternative evolutionary scenarios using phylogenetic simulations. The observed pattern did not fall within the distribution obtained under multivariate Brownian motion, enabling us to reject this evolutionary scenario. By contrast, the observed pattern was more similar to, and fell within, the distribution obtained from simulations using Brownian motion combined with a directional trend. Thus, the observed data are consistent with a pattern of directional evolution for this lineage of recessing scallops. We discuss this putative directional evolutionary trend in terms of its potential adaptive role in exploiting novel habitats. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Replicability of time-varying connectivity patterns in large resting state fMRI samples.

PubMed

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D

2017-12-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

PubMed Central

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L.; Stephen, Julia M.; Claus, Eric D.; Mayer, Andrew R.; Calhoun, Vince D.

2018-01-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain’s inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. PMID:28916181

A Theoretical Model of Jigsaw-Puzzle Pattern Formation by Plant Leaf Epidermal Cells

PubMed Central

Higaki, Takumi; Kutsuna, Natsumaro; Akita, Kae; Takigawa-Imamura, Hisako; Yoshimura, Kenji; Miura, Takashi

2016-01-01

Plant leaf epidermal cells exhibit a jigsaw puzzle–like pattern that is generated by interdigitation of the cell wall during leaf development. The contribution of two ROP GTPases, ROP2 and ROP6, to the cytoskeletal dynamics that regulate epidermal cell wall interdigitation has already been examined; however, how interactions between these molecules result in pattern formation remains to be elucidated. Here, we propose a simple interface equation model that incorporates both the cell wall remodeling activity of ROP GTPases and the diffusible signaling molecules by which they are regulated. This model successfully reproduces pattern formation observed in vivo, and explains the counterintuitive experimental results of decreased cellulose production and increased thickness. Our model also reproduces the dynamics of three-way cell wall junctions. Therefore, this model provides a possible mechanism for cell wall interdigitation formation in vivo. PMID:27054467

Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity

PubMed Central

Wolfe, Benjamin E.; Button, Julie E.; Santarelli, Marcela; Dutton, Rachel J.

2014-01-01

SUMMARY Tractable microbial communities are needed to bridge the gap between observations of patterns of microbial diversity and mechanisms that can explain these patterns. We developed cheese rinds as model microbial communities by characterizing in situ patterns of diversity and by developing an in vitro system for community reconstruction. Sequencing of 137 different rind communities across 10 countries revealed 24 widely distributed and culturable genera of bacteria and fungi as dominant community members. Reproducible community types formed independent of geographic location of production. Intensive temporal sampling demonstrated that assembly of these communities is highly reproducible. Patterns of community composition and succession observed in situ can be recapitulated in a simple in vitro system. Widespread positive and negative interactions were identified between bacterial and fungal community members. Cheese rind microbial communities represent an experimentally tractable system for defining mechanisms that influence microbial community assembly and function. PMID:25036636

Spontaneous formation of spiral-like patterns with distinct periodic physical properties by confined electrodeposition of Co-In disks

NASA Astrophysics Data System (ADS)

Golvano-Escobal, Irati; Gonzalez-Rosillo, Juan Carlos; Domingo, Neus; Illa, Xavi; López-Barberá, José Francisco; Fornell, Jordina; Solsona, Pau; Aballe, Lucia; Foerster, Michael; Suriñach, Santiago; Baró, Maria Dolors; Puig, Teresa; Pané, Salvador; Nogués, Josep; Pellicer, Eva; Sort, Jordi

2016-07-01

Spatio-temporal patterns are ubiquitous in different areas of materials science and biological systems. However, typically the motifs in these types of systems present a random distribution with many possible different structures. Herein, we demonstrate that controlled spatio-temporal patterns, with reproducible spiral-like shapes, can be obtained by electrodeposition of Co-In alloys inside a confined circular geometry (i.e., in disks that are commensurate with the typical size of the spatio-temporal features). These patterns are mainly of compositional nature, i.e., with virtually no topographic features. Interestingly, the local changes in composition lead to a periodic modulation of the physical (electric, magnetic and mechanical) properties. Namely, the Co-rich areas show higher saturation magnetization and electrical conductivity and are mechanically harder than the In-rich ones. Thus, this work reveals that confined electrodeposition of this binary system constitutes an effective procedure to attain template-free magnetic, electric and mechanical surface patterning with specific and reproducible shapes.

A New Approach in Generating Meteorological Forecasts for Ensemble Streamflow Forecasting using Multivariate Functions

NASA Astrophysics Data System (ADS)

Khajehei, S.; Madadgar, S.; Moradkhani, H.

2014-12-01

The reliability and accuracy of hydrological predictions are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model parameters and model structure. To reduce the total uncertainty in hydrological applications, one approach is to reduce the uncertainty in meteorological forcing by using the statistical methods based on the conditional probability density functions (pdf). However, one of the requirements for current methods is to assume the Gaussian distribution for the marginal distribution of the observed and modeled meteorology. Here we propose a Bayesian approach based on Copula functions to develop the conditional distribution of precipitation forecast needed in deriving a hydrologic model for a sub-basin in the Columbia River Basin. Copula functions are introduced as an alternative approach in capturing the uncertainties related to meteorological forcing. Copulas are multivariate joint distribution of univariate marginal distributions, which are capable to model the joint behavior of variables with any level of correlation and dependency. The method is applied to the monthly forecast of CPC with 0.25x0.25 degree resolution to reproduce the PRISM dataset over 1970-2000. Results are compared with Ensemble Pre-Processor approach as a common procedure used by National Weather Service River forecast centers in reproducing observed climatology during a ten-year verification period (2000-2010).

Quantitative chromatin pattern description in Feulgen-stained nuclei as a diagnostic tool to characterize the oligodendroglial and astroglial components in mixed oligo-astrocytomas.

PubMed

Decaestecker, C; Lopes, B S; Gordower, L; Camby, I; Cras, P; Martin, J J; Kiss, R; VandenBerg, S R; Salmon, I

1997-04-01

The oligoastrocytoma, as a mixed glioma, represents a nosologic dilemma with respect to precisely defining the oligodendroglial and astroglial phenotypes that constitute the neoplastic cell lineages of these tumors. In this study, cell image analysis with Feulgen-stained nuclei was used to distinguish between oligodendroglial and astrocytic phenotypes in oligodendrogliomas and astrocytomas and then applied to mixed oligoastrocytomas. Quantitative features with respect to chromatin pattern (30 variables) and DNA ploidy (8 variables) were evaluated on Feulgen-stained nuclei in a series of 71 gliomas using computer-assisted microscopy. These included 32 oligodendrogliomas (OLG group: 24 grade II and 8 grade III tumors according to the WHO classification), 32 astrocytomas (AST group: 13 grade II and 19 grade III tumors), and 7 oligoastrocytomas (OLGAST group). Initially, image analysis with multivariate statistical analyses (Discriminant Analysis) could identify each glial tumor group. Highly significant statistical differences were obtained distinguishing the morphonuclear features of oligodendrogliomas from those of astrocytomas, regardless of their histological grade. When compared with the 7 mixed oligoastrocytomas under study, 5 exhibited DNA ploidy and chromatin pattern characteristics similar to grade II oligodendrogliomas, I to grade III oligodendrogliomas, and I to grade II astrocytomas. Using multifactorial statistical analyses (Discriminant Analysis combined with Principal Component Analysis). It was possible to quantify the proportion of "typical" glial cell phenotypes that compose grade II and III oligodendrogliomas and grade II and III astrocytomas in each mixed glioma. Cytometric image analysis may be an important adjunct to routine histopathology for the reproducible identification of neoplasms containing a mixture of oligodendroglial and astrocytic phenotypes.

A multivariate ecogeographic analysis of macaque craniodental variation.

PubMed

Grunstra, Nicole D S; Mitteroecker, Philipp; Foley, Robert A

2018-06-01

To infer the ecogeographic conditions that underlie the evolutionary diversification of macaques, we investigated the within- and between-species relationships of craniodental dimensions, geography, and environment in extant macaque species. We studied evolutionary processes by contrasting macroevolutionary patterns, phylogeny, and within-species associations. Sixty-three linear measurements of the permanent dentition and skull along with data about climate, ecology (environment), and spatial geography were collected for 711 specimens of 12 macaque species and analyzed by a multivariate approach. Phylogenetic two-block partial least squares was used to identify patterns of covariance between craniodental and environmental variation. Phylogenetic reduced rank regression was employed to analyze spatial clines in morphological variation. Between-species associations consisted of two distinct multivariate patterns. The first represents overall craniodental size and is negatively associated with temperature and habitat, but positively with latitude. The second pattern shows an antero-posterior tooth size contrast related to diet, rainfall, and habitat productivity. After controlling for phylogeny, however, the latter dimension was diminished. Within-species analyses neither revealed significant association between morphology, environment, and geography, nor evidence of isolation by distance. We found evidence for environmental adaptation in macaque body and craniodental size, primarily driven by selection for thermoregulation. This pattern cannot be explained by the within-species pattern, indicating an evolved genetic basis for the between-species relationship. The dietary signal in relative tooth size, by contrast, can largely be explained by phylogeny. This cautions against adaptive interpretations of phenotype-environment associations when phylogeny is not explicitly modelled. © 2018 Wiley Periodicals, Inc.

Visual Learning Induces Changes in Resting-State fMRI Multivariate Pattern of Information.

PubMed

Guidotti, Roberto; Del Gratta, Cosimo; Baldassarre, Antonello; Romani, Gian Luca; Corbetta, Maurizio

2015-07-08

When measured with functional magnetic resonance imaging (fMRI) in the resting state (R-fMRI), spontaneous activity is correlated between brain regions that are anatomically and functionally related. Learning and/or task performance can induce modulation of the resting synchronization between brain regions. Moreover, at the neuronal level spontaneous brain activity can replay patterns evoked by a previously presented stimulus. Here we test whether visual learning/task performance can induce a change in the patterns of coded information in R-fMRI signals consistent with a role of spontaneous activity in representing task-relevant information. Human subjects underwent R-fMRI before and after perceptual learning on a novel visual shape orientation discrimination task. Task-evoked fMRI patterns to trained versus novel stimuli were recorded after learning was completed, and before the second R-fMRI session. Using multivariate pattern analysis on task-evoked signals, we found patterns in several cortical regions, as follows: visual cortex, V3/V3A/V7; within the default mode network, precuneus, and inferior parietal lobule; and, within the dorsal attention network, intraparietal sulcus, which discriminated between trained and novel visual stimuli. The accuracy of classification was strongly correlated with behavioral performance. Next, we measured multivariate patterns in R-fMRI signals before and after learning. The frequency and similarity of resting states representing the task/visual stimuli states increased post-learning in the same cortical regions recruited by the task. These findings support a representational role of spontaneous brain activity. Copyright © 2015 the authors 0270-6474/15/359786-13$15.00/0.

Analysis of the fluid mechanical sewing machine

NASA Astrophysics Data System (ADS)

Brun, Pierre-Thomas; Audoly, Basile; Ribe, Neil

2012-02-01

A thin thread of viscous fluid falling onto a moving belt generates a surprising variety of patterns, similar to the stitch patterns produced by a traditional sewing machine. By simulating the dynamics of the viscous thread numerically, we can reproduce these patterns and their bifurcations. The results lead us to propose a new classification of the stitch patterns within a unified framework, based on the Fourier spectra of the motion of the point of contact of the thread with the belt. The frequencies of the longitudinal and transverse components of the contact point motion are locked in most cases to simple ratios of the frequency φc of steady coiling on a surface at rest (i.e., the limit of zero belt speed). In particular, the ``alternating loops'' pattern involves the first five multiples of φc/3. The dynamics of the patterns can be described by matching the upper (linear) and the lower (non-linear) portions of the thread. Following this path we propose a toy model that successfully reproduces the observed transitions from the steady dragged configuration to sinusoidal meanders, alternating loops, and the translated coiling pattern as the belt speed is varied.

Crossmodal integration enhances neural representation of task-relevant features in audiovisual face perception.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Liu, Yongjian; Liang, Changhong; Sun, Pei

2015-02-01

Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study, we considered crossmodal integration in audiovisual facial perception and explored its effect on the neural representation of features. The audiovisual stimuli in the experiment consisted of facial movie clips that could be classified into 2 gender categories (male vs. female) or 2 emotion categories (crying vs. laughing). The visual/auditory-only stimuli were created from these movie clips by removing the auditory/visual contents. The subjects needed to make a judgment about the gender/emotion category for each movie clip in the audiovisual, visual-only, or auditory-only stimulus condition as functional magnetic resonance imaging (fMRI) signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audiovisual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audiovisual integration. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

PubMed

Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin

2016-12-01

Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optical Verification Laboratory Demonstration System for High Security Identification Cards

NASA Technical Reports Server (NTRS)

Javidi, Bahram

1997-01-01

Document fraud including unauthorized duplication of identification cards and credit cards is a serious problem facing the government, banks, businesses, and consumers. In addition, counterfeit products such as computer chips, and compact discs, are arriving on our shores in great numbers. With the rapid advances in computers, CCD technology, image processing hardware and software, printers, scanners, and copiers, it is becoming increasingly easy to reproduce pictures, logos, symbols, paper currency, or patterns. These problems have stimulated an interest in research, development and publications in security technology. Some ID cards, credit cards and passports currently use holograms as a security measure to thwart copying. The holograms are inspected by the human eye. In theory, the hologram cannot be reproduced by an unauthorized person using commercially-available optical components; in practice, however, technology has advanced to the point where the holographic image can be acquired from a credit card-photographed or captured with by a CCD camera-and a new hologram synthesized using commercially-available optical components or hologram-producing equipment. Therefore, a pattern that can be read by a conventional light source and a CCD camera can be reproduced. An optical security and anti-copying device that provides significant security improvements over existing security technology was demonstrated. The system can be applied for security verification of credit cards, passports, and other IDs so that they cannot easily be reproduced. We have used a new scheme of complex phase/amplitude patterns that cannot be seen and cannot be copied by an intensity-sensitive detector such as a CCD camera. A random phase mask is bonded to a primary identification pattern which could also be phase encoded. The pattern could be a fingerprint, a picture of a face, or a signature. The proposed optical processing device is designed to identify both the random phase mask and the primary pattern [1-3]. We have demonstrated experimentally an optical processor for security verification of objects, products, and persons. This demonstration is very important to encourage industries to consider the proposed system for research and development.

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…

[Discrimination between pain-induced head movement disturbances after whiplash injuries and their simulation].

PubMed

Berger, M; Lechner-Steinleitner, S; Hoffmann, F; Schönegger, J

1998-12-09

Neck pain after whiplash injury of the cervical spine often induces typical changes in head motion patterns (amplitude, velocity). These changes of kinematics may help to recognize malingerers. We investigated the hypothesis that malingerers are not able to reproduce their simulated head movement disturbances three times. The kinematics of head movements of 23 patients with neck pain after whiplash injury and of 22 healthy subjects trying to act as malingerers were compared. The healthy subjects were informed about the symptomatology of whiplash injury and were asked to simulate painful head movements. Two different kinds of head movements were registered and analyzed by Cervicomotography: (1) the slow free axial head rotation (yaw) and (2) the axial head rotation (yaw) tracking a moving visual target. Each experimental condition was presented three times, expecting the malingerers not to be able to produce as well as to reproduce the same head movement disturbances again and again. In patients, as a consequence of their distinct pain patterns, we expected less variance between the test repetitions. The statistical analysis showed significant differences of the calculated kinematic parameters between both groups and the inability of healthy subjects to simulate and to reproduce convincingly distinct pain patterns.

Development of an Aerosol Surface Inoculation Method for Bacillus Spores ▿

PubMed Central

Lee, Sang Don; Ryan, Shawn P.; Snyder, Emily Gibb

2011-01-01

A method was developed to deposit Bacillus subtilis spores via aerosolization onto various surface materials for biological agent decontamination and detection studies. This new method uses an apparatus coupled with a metered dose inhaler to reproducibly deposit spores onto various surfaces. A metered dose inhaler was loaded with Bacillus subtilis spores, a surrogate for Bacillus anthracis. Five different material surfaces (aluminum, galvanized steel, wood, carpet, and painted wallboard paper) were tested using this spore deposition method. This aerosolization method deposited spores at a concentration of more than 107 CFU per coupon (18-mm diameter) with less than a 50% coefficient of variation, showing that the aerosolization method developed in this study can deposit reproducible numbers of spores onto various surface coupons. Scanning electron microscopy was used to probe the spore deposition patterns on test coupons. The deposition patterns observed following aerosol impaction were compared to those of liquid inoculation. A physical difference in the spore deposition patterns was observed to result from the two different methods. The spore deposition method developed in this study will help prepare spore coupons via aerosolization fast and reproducibly for bench top decontamination and detection studies. PMID:21193670

Development of an aerosol surface inoculation method for bacillus spores.

PubMed

Lee, Sang Don; Ryan, Shawn P; Snyder, Emily Gibb

2011-03-01

A method was developed to deposit Bacillus subtilis spores via aerosolization onto various surface materials for biological agent decontamination and detection studies. This new method uses an apparatus coupled with a metered dose inhaler to reproducibly deposit spores onto various surfaces. A metered dose inhaler was loaded with Bacillus subtilis spores, a surrogate for Bacillus anthracis. Five different material surfaces (aluminum, galvanized steel, wood, carpet, and painted wallboard paper) were tested using this spore deposition method. This aerosolization method deposited spores at a concentration of more than 10(7) CFU per coupon (18-mm diameter) with less than a 50% coefficient of variation, showing that the aerosolization method developed in this study can deposit reproducible numbers of spores onto various surface coupons. Scanning electron microscopy was used to probe the spore deposition patterns on test coupons. The deposition patterns observed following aerosol impaction were compared to those of liquid inoculation. A physical difference in the spore deposition patterns was observed to result from the two different methods. The spore deposition method developed in this study will help prepare spore coupons via aerosolization fast and reproducibly for bench top decontamination and detection studies.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

PubMed

Kragel, Philip A; Labar, Kevin S

2013-08-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Multivariate Pattern Classification Reveals Autonomic and Experiential Representations of Discrete Emotions

PubMed Central

Kragel, Philip A.; LaBar, Kevin S.

2013-01-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PMID:23527508

Association between Dietary Patterns during Pregnancy and Birth Size Measures in a Diverse Population in Southern US

PubMed Central

Colón-Ramos, Uriyoán; Racette, Susan B.; Ganiban, Jody; Nguyen, Thuy G.; Kocak, Mehmet; Carroll, Kecia N.; Völgyi, Eszter; Tylavsky, Frances A.

2015-01-01

Despite increased interest in promoting nutrition during pregnancy, the association between maternal dietary patterns and birth outcomes has been equivocal. We examined maternal dietary patterns during pregnancy as a determinant of offspring’s birth weight-for-length (WLZ), weight-for-age (WAZ), length-for-age (LAZ), and head circumference (HCZ) Z-scores in Southern United States (n = 1151). Maternal diet during pregnancy was assessed by seven dietary patterns. Multivariable linear regression models described the association of WLZ, WAZ, LAZ, and HCZ with diet patterns controlling for other maternal and child characteristics. In bivariate analyses, WAZ and HCZ were significantly lower for processed and processed-Southern compared to healthy dietary patterns, whereas LAZ was significantly higher for these patterns. In the multivariate models, mothers who consumed a healthy-processed dietary pattern had children with significantly higher HCZ compared to the ones who consumed a healthy dietary pattern (HCZ β: 0.36; p = 0.019). No other dietary pattern was significantly associated with any of the birth outcomes. Instead, the major outcome determinants were: African American race, pre-pregnancy BMI, and gestational weight gain. These findings justify further investigation about socio-environmental and genetic factors related to race and birth outcomes in this population. PMID:25690420

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic precipitation, fed into a rainfall-runoff model to derive the flood frequency in the Tirolean Alps in Austria. Given the number of generated events, the simulation framework is able to generate a large variety of rainfall patterns, as well as reproduce the variograms of relevant extreme rainfall events in the region of interest.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Somatic and vicarious pain are represented by dissociable multivariate brain patterns

PubMed Central

Krishnan, Anjali; Woo, Choong-Wan; Chang, Luke J; Ruzic, Luka; Gu, Xiaosi; López-Solà, Marina; Jackson, Philip L; Pujol, Jesús; Fan, Jin; Wager, Tor D

2016-01-01

Understanding how humans represent others’ pain is critical for understanding pro-social behavior. ‘Shared experience’ theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others’ suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sample individuals. Critically, each pattern was at chance in predicting the other experience, demonstrating separate modifiability of both patterns. Somatotopy (upper versus lower limb: 93% accuracy for both conditions) was also distinct, located in somatosensory versus mentalizing-related circuits for somatic and vicarious pain, respectively. Two additional studies demonstrated the generalizability of the somatic pain pattern (which was originally developed on thermal pain) to mechanical and electrical pain, and also demonstrated the replicability of the somatic/vicarious dissociation. These findings suggest possible mechanisms underlying limitations in feeling others’ pain, and present new, more specific, brain targets for studying pain empathy. DOI: http://dx.doi.org/10.7554/eLife.15166.001 PMID:27296895

Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals

EPA Science Inventory

Neuronal assemblies within the Central Nervous System (CNS) produce spontaneous or stimulus-evoked electrophysiological activity that can be monitored and quantified in terms of action potential patterns. Such patterns provide a sensitive endpoint to detect effects of chemicals, ...

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

Molecular Interactions of the Min Protein System Reproduce Spatiotemporal Patterning in Growing and Dividing Escherichia coli Cells.

PubMed

Walsh, James C; Angstmann, Christopher N; Duggin, Iain G; Curmi, Paul M G

2015-01-01

Oscillations of the Min protein system are involved in the correct midcell placement of the divisome during Escherichia coli cell division. Based on molecular interactions of the Min system, we formulated a mathematical model that reproduces Min patterning during cell growth and division. Specifically, the increase in the residence time of MinD attached to the membrane as its own concentration increases, is accounted for by dimerisation of membrane-bound MinD and its interaction with MinE. Simulation of this system generates unparalleled correlation between the waveshape of experimental and theoretical MinD distributions, suggesting that the dominant interactions of the physical system have been successfully incorporated into the model. For cells where MinD is fully-labelled with GFP, the model reproduces the stationary localization of MinD-GFP for short cells, followed by oscillations from pole to pole in larger cells, and the transition to the symmetric distribution during cell filamentation. Cells containing a secondary, GFP-labelled MinD display a contrasting pattern. The model is able to account for these differences, including temporary midcell localization just prior to division, by increasing the rate constant controlling MinD ATPase and heterotetramer dissociation. For both experimental conditions, the model can explain how cell division results in an equal distribution of MinD and MinE in the two daughter cells, and accounts for the temperature dependence of the period of Min oscillations. Thus, we show that while other interactions may be present, they are not needed to reproduce the main characteristics of the Min system in vivo.

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.

Modified two-layer social force model for emergency earthquake evacuation

NASA Astrophysics Data System (ADS)

Zhang, Hao; Liu, Hong; Qin, Xin; Liu, Baoxi

2018-02-01

Studies of crowd behavior with related research on computer simulation provide an effective basis for architectural design and effective crowd management. Based on low-density group organization patterns, a modified two-layer social force model is proposed in this paper to simulate and reproduce a group gathering process. First, this paper studies evacuation videos from the Luan'xian earthquake in 2012, and extends the study of group organization patterns to a higher density. Furthermore, taking full advantage of the strength in crowd gathering simulations, a new method on grouping and guidance is proposed while using crowd dynamics. Second, a real-life grouping situation in earthquake evacuation is simulated and reproduced. Comparing with the fundamental social force model and existing guided crowd model, the modified model reduces congestion time and truly reflects group behaviors. Furthermore, the experiment result also shows that a stable group pattern and a suitable leader could decrease collision and allow a safer evacuation process.

Effects of task complexity on rhythmic reproduction performance in adults.

PubMed

Iannarilli, Flora; Vannozzi, Giuseppe; Iosa, Marco; Pesce, Caterina; Capranica, Laura

2013-02-01

The aim of the present study was to investigate the effect of task complexity on the capability to reproduce rhythmic patterns. Sedentary musically illiterate individuals (age: 34.8±4.2 yrs; M±SD) were administered a rhythmic test including three rhythmic patterns to be reproduced by means of finger-tapping, foot-tapping and walking. For the quantification of subjects' ability in the reproduction of rhythmic patterns, qualitative and quantitative parameters were submitted to analysis. A stereophotogrammetric system was used to reconstruct and evaluate individual performances. The findings indicated a good internal stability of the rhythmic reproduction, suggesting that the present experimental design is suitable to discriminate the participants' rhythmic ability. Qualitative aspects of rhythmic reproduction (i.e., speed of execution and temporal ratios between events) varied as a function of the perceptual-motor requirements of the rhythmic reproduction task, with larger reproduction deviations in the walking task. Copyright © 2013 Elsevier B.V. All rights reserved.

Reproduction of overall spontaneous pain pattern by manual stimulation of active myofascial trigger points in fibromyalgia patients

PubMed Central

2011-01-01

Introduction It has previously been reported that local and referred pain from active myofascial trigger points (MTPs) in the neck and shoulder region contribute to fibromyalgia (FM) pain and that the pain pattern induced from active MTPs can reproduce parts of the spontaneous clinical FM pain pattern. The current study investigated whether the overall spontaneous FM pain pattern can be reproduced by local and referred pain from active MTPs located in different muscles. Methods A spontaneous pain pattern in FM was recorded in 30 FM patients and 30 healthy subjects served as controls. Local and referred pain patterns induced from active (patients) and latent (controls) MTPs were recorded following manual stimulation. The existence of MTPs was confirmed by intramuscular electromyographical registration of spontaneous electrical activity. Results Local and referred pain areas induced from key active MTPs in FM were larger than pain areas from latent MTPs in healthy controls (P < 0.001), but were similar to the overall spontaneous FM pain area in FM (P > 0.05). The induced pain area was positively associated with current spontaneous pain intensity in FM (P < 0.01). The locations of key active MTPs in FM patients were found to have latent MTPs in healthy subjects. The muscles containing key active MTPs in FM are often observed in the muscles of extensor digitorum, trapezius, infraspinatus in the upper part of the body and of quadratus lumborum, gluteus medius in the lower part of the body. Conclusions The overall spontaneous FM pain pattern can be reproduced by mechanical stimulation of active MTPs located in different muscles, suggesting that fibromyalgia pain is largely composed of pain arising from muscle pain and spasm. Targeting active MTPs and related perpetuating factors may be an important strategy in FM pain control. Trial registration ISRCTN ISRCTN43167547. PMID:21426569

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy.

PubMed

Workman, Jerome J

2018-03-01

Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

Detecting Stress Patterns Is Related to Children's Performance on Reading Tasks

ERIC Educational Resources Information Center

Gutierrez-Palma, Nicolas; Raya-Garcia, Manuel; Palma-Reyes, Alfonso

2009-01-01

This paper investigates the relationship between the ability to detect changes in prosody and reading performance in Spanish. Participants were children aged 6-8 years who completed tasks involving reading words, reading pseudowords, stressing pseudowords, and reproducing pseudoword stress patterns. Results showed that the capacity to reproduce…

Evaluation of the U.S. Army’s Aids Education Program

DTIC Science & Technology

1992-03-25

34 scaiac (contlinad) Initial Factor Notd : N. Preatation flatbed : Varimax Notation NedjiM Prai Factor Pattern Notated Factor Pattern Factor StrUCture...material, the graphics contained in this appendix may be reproduced in the form of slides, or enlarged, laminated and bound together for use as a flip

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

NASA Astrophysics Data System (ADS)

Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

2016-10-01

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

Interpreting sulci on hominin endocasts: old hypotheses and new findings

PubMed Central

Falk, Dean

2014-01-01

Paleoneurologists analyze internal casts (endocasts) of fossilized braincases, which provide information about the size, shape and, to a limited degree, sulcal patterns reproduced from impressions left by the surface of the brain. When interpreted in light of comparative data from the brains of living apes and humans, sulcal patterns reproduced on hominin endocasts provide important information for studying the evolution of the cerebral cortex and cognition in human ancestors. Here, new evidence is discussed for the evolution of sulcal patterns associated with cortical reorganization in three parts of the hominin brain: (1) the parietotemporo-occipital association cortex, (2) Broca's speech area, and (3) dorsolateral prefrontal association cortex. Of the three regions, the evidence regarding the last is the clearest. Compared to great apes, Australopithecus endocasts reproduce a clear middle frontal sulcus in the dorsolateral prefrontal cortex that is derived toward the human condition. This finding is consistent with data from comparative cytoarchitectural studies of ape and human brains as well as shape analyses of australopithecine endocasts. The comparative and direct evidence for all three regions suggests that hominin brain reorganization was underway by at least the time of Australopithecus africanus (~2.5 to 3.0 mya), despite the ape-sized brains of these hominins, and that it entailed expansion of both rostral and caudal association cortices. PMID:24822043

Inter-dependent tissue growth and Turing patterning in a model for long bone development

NASA Astrophysics Data System (ADS)

Tanaka, Simon; Iber, Dagmar

2013-10-01

The development of long bones requires a sophisticated spatial organization of cellular signalling, proliferation, and differentiation programs. How such spatial organization emerges on the growing long bone domain is still unresolved. Based on the reported biochemical interactions we developed a regulatory model for the core signalling factors IHH, PTCH1, and PTHrP and included two cell types, proliferating/resting chondrocytes and (pre-)hypertrophic chondrocytes. We show that the reported IHH-PTCH1 interaction gives rise to a Schnakenberg-type Turing kinetics, and that inclusion of PTHrP is important to achieve robust patterning when coupling patterning and tissue dynamics. The model reproduces relevant spatiotemporal gene expression patterns, as well as a number of relevant mutant phenotypes. In summary, we propose that a ligand-receptor based Turing mechanism may control the emergence of patterns during long bone development, with PTHrP as an important mediator to confer patterning robustness when the sensitive Turing system is coupled to the dynamics of a growing and differentiating tissue. We have previously shown that ligand-receptor based Turing mechanisms can also result from BMP-receptor, SHH-receptor, and GDNF-receptor interactions, and that these reproduce the wildtype and mutant patterns during digit formation in limbs and branching morphogenesis in lung and kidneys. Receptor-ligand interactions may thus constitute a general mechanism to generate Turing patterns in nature.

Herschel's Interference Demonstration.

ERIC Educational Resources Information Center

Perkalskis, Benjamin S.; Freeman, J. Reuben

2000-01-01

Describes Herschel's demonstration of interference arising from many coherent rays. Presents a method for students to reproduce this demonstration and obtain beautiful multiple-beam interference patterns. (CCM)

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis.

PubMed

Brindle, R C; Ginty, A T; Jones, A; Phillips, A C; Roseboom, T J; Carroll, D; Painter, R C; de Rooij, S R

2016-12-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure reactivity patterns and hypertension in a large prospective cohort (age range 55-60 years). Four clusters emerged with statistically different systolic and diastolic blood pressure and heart rate reactivity patterns. Cluster 1 was characterised by a relatively exaggerated blood pressure and heart rate response while the blood pressure and heart rate responses of cluster 2 were relatively modest and in line with the sample mean. Cluster 3 was characterised by blunted cardiovascular stress reactivity across all variables and cluster 4, by an exaggerated blood pressure response and modest heart rate response. Membership to cluster 4 conferred an increased risk of hypertension at 5-year follow-up (hazard ratio=2.98 (95% CI: 1.50-5.90), P<0.01) that survived adjustment for a host of potential confounding variables. These results suggest that the cardiac reactivity plays a potentially important role in the link between blood pressure reactivity and hypertension and support the use of multivariate approaches to stress psychophysiology.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Dietary Patterns and Risk of Esophageal Cancer Mortality: The Japan Collaborative Cohort Study.

PubMed

Okada, Emiko; Nakamura, Koshi; Ukawa, Shigekazu; Sakata, Kiyomi; Date, Chigusa; Iso, Hiroyasu; Tamakoshi, Akiko

2016-01-01

Several case-control studies have associated dietary patterns with esophageal cancer (EC) risk, but prospective studies are scarce. We investigated dietary pattern and EC mortality risk associations by smoking status. Participants were 26,562 40- to 79-yr-old Japanese men, who enrolled in the Japan Collaborative Cohort Study between 1988 and 1990. Hazard ratios (HRs) and 95% confidence intervals (CIs) for EC mortality in nonsmokers and smokers were estimated using Cox proportional models. During follow-up (1988-2009), 132 participants died of EC. Using a baseline food frequency questionnaire and factor analysis, vegetable, animal, and dairy product food patterns were identified. EC risk decreased significantly with a higher factor score for the dairy product pattern (Ptrend = 0.042) and was more pronounced in smokers [multivariable HR (4th vs. 1st quartiles) = 0.57, 95% CI: 0.30, 1.09; Ptrend = 0.021]. Neither vegetable nor animal food patterns were significant overall; however, EC risk increased with a higher factor score for the animal food pattern in nonsmokers [multivariable HR (4th vs. 1st quartiles) = 6.01, 95% CI: 1.17, 30.88; Ptrend = 0.021], although the small number of events was a limitation. Our findings suggest a dairy product pattern may reduce EC risk in Japanese men, especially smokers.

The multivariate egg: quantifying within- and among-clutch correlations between maternally derived yolk immunoglobulins and yolk androgens using multivariate mixed models.

PubMed

Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara

2014-03-01

Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

PubMed

Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

2012-03-14

Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia.

PubMed

Horga, Guillermo; Cassidy, Clifford M; Xu, Xiaoyan; Moore, Holly; Slifstein, Mark; Van Snellenberg, Jared X; Abi-Dargham, Anissa

2016-08-01

Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

Interaction of Soil Heavy Metal Pollution with Industrialisation and the Landscape Pattern in Taiyuan City, China

PubMed Central

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. PMID:25251460

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China.

PubMed

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Glial-released proteins in clonal cultures and their modulation by hydrocortisone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arenander, A.T.; de Vellis, J.

Rat glial C6 cells release into the culture medium a reproducible spectrum of soluble proteins of 12 major peaks over a broad molecular weight range as determined by fractionation on SDS-gel electrophoresis. Exposing C6 monolayers to hydrocortisone (HC) results in a selective alteration in the pattern of glial-released protein (GRP). The selective HC-induced increase or decrease in GRP peaks is specific to HC in that 17 ..beta..-estradiol, dibutyryl cyclic AMP, isoproterenol, and melatonin exert either no detectable or a qualitatively different influence on the GRP pattern. The HC influence is dose dependent over a physiological range of concentrations from 10/supmore » -9/ to 10/sup -6/ M. Differences in culture age and in subclones of C6 can influence both the normal and the HC-induced pattern of GRP. The origin of the GRP is unknown, but pattern reproducibility, viability tests, surface labelling studies, and metabolic labelling studies of soluble and particulate compartment proteins and glycoproteins support the position that cell lysis is not an important source of GRP. More importantly, these studies indicate that GRP and HC-induced changes in GRP pattern are physiologically significant aspects of glial cell behavior.« less

fMRI-based Multivariate Pattern Analyses Reveal Imagery Modality and Imagery Content Specific Representations in Primary Somatosensory, Motor and Auditory Cortices.

PubMed

de Borst, Aline W; de Gelder, Beatrice

2017-08-01

Previous studies have shown that the early visual cortex contains content-specific representations of stimuli during visual imagery, and that these representational patterns of imagery content have a perceptual basis. To date, there is little evidence for the presence of a similar organization in the auditory and tactile domains. Using fMRI-based multivariate pattern analyses we showed that primary somatosensory, auditory, motor, and visual cortices are discriminative for imagery of touch versus sound. In the somatosensory, motor and visual cortices the imagery modality discriminative patterns were similar to perception modality discriminative patterns, suggesting that top-down modulations in these regions rely on similar neural representations as bottom-up perceptual processes. Moreover, we found evidence for content-specific representations of the stimuli during auditory imagery in the primary somatosensory and primary motor cortices. Both the imagined emotions and the imagined identities of the auditory stimuli could be successfully classified in these regions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Constraining geostatistical models with hydrological data to improve prediction realism

NASA Astrophysics Data System (ADS)

Demyanov, V.; Rojas, T.; Christie, M.; Arnold, D.

2012-04-01

Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.

Spatiotemporal Patterns of Noise-Driven Confined Actin Waves in Living Cells.

PubMed

Bernitt, Erik; Döbereiner, Hans-Günther

2017-01-27

Cells utilize waves of polymerizing actin to reshape their morphologies, which is central to physiological and pathological processes alike. Here, we force dorsal actin waves to propagate on one-dimensional domains with periodic boundary conditions, which results in striking spatiotemporal patterns with a clear signature of noise-driven dynamics. We show that these patterns can be very closely reproduced with a noise-driven active medium at coherence resonance.

Culture: copying, compression, and conventionality.

PubMed

Tamariz, Mónica; Kirby, Simon

2015-01-01

Through cultural transmission, repeated learning by new individuals transforms cultural information, which tends to become increasingly compressible (Kirby, Cornish, & Smith, ; Smith, Tamariz, & Kirby, ). Existing diffusion chain studies include in their design two processes that could be responsible for this tendency: learning (storing patterns in memory) and reproducing (producing the patterns again). This paper manipulates the presence of learning in a simple iterated drawing design experiment. We find that learning seems to be the causal factor behind the increase in compressibility observed in the transmitted information, while reproducing is a source of random heritable innovations. Only a theory invoking these two aspects of cultural learning will be able to explain human culture's fundamental balance between stability and innovation. Copyright © 2014 Cognitive Science Society, Inc.

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

Scratch Your Brain Where It Itches: Math Games, Tricks and Quick Activities, Book D-1 Algebra.

ERIC Educational Resources Information Center

Brumbaugh, Doug

This resource book for algebra contains games, tricks, and quick activities for the classroom. Categories of activities include puzzlers, patterns, manipulatives, measurement, graphing, and a section that contains reproducible statement and value cards. Twenty one puzzle problems, four pattern activities, and 11 quick activities that engage…

A factor analysis of landscape pattern and structure metrics

Treesearch

Kurt H. Riitters; R.V. O' Neill; C.T. Hunsaker; James D. Wickham; D.H. Yankee; S.P. Timmins; K.B. Jones; B.L. Jackson

1995-01-01

Fifty-five metrics of landscape pattern and structure were calculated for 85 maps of land use and land cover. A multivariate factor analysis was used to identify the common axes (or dimensions) of pattern and structure which were measured by a reduced set of 26 metrics. The first six factors explained about 87% of the variation in the 26 landscape metrics. These...

Vascular pattern formation in plants.

PubMed

Scarpella, Enrico; Helariutta, Ykä

2010-01-01

Reticulate tissue systems exist in most multicellular organisms, and the principles underlying the formation of cellular networks have fascinated philosophers, mathematicians, and biologists for centuries. In particular, the beautiful and varied arrangements of vascular tissues in plants have intrigued mankind since antiquity, yet the organizing signals have remained elusive. Plant vascular tissues form systems of interconnected cell files throughout the plant body. Vascular cells are aligned with one another along continuous lines, and vascular tissues differentiate at reproducible positions within organ environments. However, neither the precise path of vascular differentiation nor the exact geometry of vascular networks is fixed or immutable. Several recent advances converge to reconcile the seemingly conflicting predictability and plasticity of vascular tissue patterns. A control mechanism in which an apical-basal flow of signal establishes a basic coordinate system for body axis formation and vascular strand differentiation, and in which a superimposed level of radial organizing cues elaborates cell patterns, would generate a reproducible tissue configuration in the context of an underlying robust, self-organizing structure, and account for the simultaneous regularity and flexibility of vascular tissue patterns. Copyright 2010 Elsevier Inc. All rights reserved.

Application of reiteration of Hankel singular value decomposition in quality control

NASA Astrophysics Data System (ADS)

Staniszewski, Michał; Skorupa, Agnieszka; Boguszewicz, Łukasz; Michalczuk, Agnieszka; Wereszczyński, Kamil; Wicher, Magdalena; Konopka, Marek; Sokół, Maria; Polański, Andrzej

2017-07-01

Medical centres are obliged to store past medical records, including the results of quality assurance (QA) tests of the medical equipment, which is especially useful in checking reproducibility of medical devices and procedures. Analysis of multivariate time series is an important part of quality control of NMR data. In this work we proposean anomaly detection tool based on Reiteration of Hankel Singular Value Decomposition method. The presented method was compared with external software and authors obtained comparable results.

Referred pain from myofascial trigger points in head, neck, shoulder, and arm muscles reproduces pain symptoms in blue-collar (manual) and white-collar (office) workers.

PubMed

Fernández-de-las-Peñas, César; Gröbli, Christian; Ortega-Santiago, Ricardo; Fischer, Christine Stebler; Boesch, Daniel; Froidevaux, Philippe; Stocker, Lilian; Weissmann, Richard; González-Iglesias, Javier

2012-07-01

To describe the prevalence and referred pain area of trigger points (TrPs) in blue-collar (manual) and white-collar (office) workers, and to analyze if the referred pain pattern elicited from TrPs completely reproduces the overall spontaneous pain pattern. Sixteen (62% women) blue-collar and 19 (75% women) white-collar workers were included in this study. TrPs in the temporalis, masseter, upper trapezius, sternocleidomastoid, splenius capitis, oblique capitis inferior, levator scapulae, scalene, pectoralis major, deltoid, infraspinatus, extensor carpi radialis brevis and longus, extensor digitorum communis, and supinator muscles were examined bilaterally (hyper-sensible tender spot within a palpable taut band, local twitch response with snapping palpation, and elicited referred pain pattern with palpation) by experienced assessors blinded to the participants' condition. TrPs were considered active when the local and referred pain reproduced any symptom and the patient recognized the pain as familiar. The referred pain areas were drawn on anatomic maps, digitized, and measured. Blue-collar workers had a mean of 6 (SD: 3) active and 10 (SD: 5) latent TrPs, whereas white-collar workers had a mean of 6 (SD: 4) active and 11 (SD: 6) latent TrPs (P>0.548). No significant differences in the distribution of active and latent TrPs in the analyzed muscles between groups were found. Active TrPs in the upper trapezius, infraspinatus, levator scapulae, and extensor carpi radialis brevis muscles were the most prevalent in both groups. Significant differences in referred pain areas between muscles (P<0.001) were found; pectoralis major, infraspinatus, upper trapezius, and scalene muscles showed the largest referred pain areas (P<0.01), whereas the temporalis, masseter, and splenius capitis muscles showed the smallest (P<0.05). The combination of the referred pain from TrPs reproduced the overall clinical pain area in all participants. Blue-collar and white-collar workers exhibited a similar number of TrPs in the upper quadrant musculature. The referred pain elicited by active TrPs reproduced the overall pain pattern. The distribution of TrPs was not significantly different between groups. Clinicians should examine for the presence of muscle TrPs in blue-collar and white-collar workers.

Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

NASA Astrophysics Data System (ADS)

Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

2015-12-01

The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide process information. They fall into three basic patterns: a channelized end member, a sheet flow end member, and one intermediate case. These represent the continuum between autogenic bypass or erosion, and net deposition.

Baldovin-Stella stochastic volatility process and Wiener process mixtures

NASA Astrophysics Data System (ADS)

Peirano, P. P.; Challet, D.

2012-08-01

Starting from inhomogeneous time scaling and linear decorrelation between successive price returns, Baldovin and Stella recently proposed a powerful and consistent way to build a model describing the time evolution of a financial index. We first make it fully explicit by using Student distributions instead of power law-truncated Lévy distributions and show that the analytic tractability of the model extends to the larger class of symmetric generalized hyperbolic distributions and provide a full computation of their multivariate characteristic functions; more generally, we show that the stochastic processes arising in this framework are representable as mixtures of Wiener processes. The basic Baldovin and Stella model, while mimicking well volatility relaxation phenomena such as the Omori law, fails to reproduce other stylized facts such as the leverage effect or some time reversal asymmetries. We discuss how to modify the dynamics of this process in order to reproduce real data more accurately.

Optimization of Dissolution Compartments in a Biorelevant Dissolution Apparatus Golem v2, Supported by Multivariate Analysis.

PubMed

Stupák, Ivan; Pavloková, Sylvie; Vysloužil, Jakub; Dohnal, Jiří; Čulen, Martin

2017-11-23

Biorelevant dissolution instruments represent an important tool for pharmaceutical research and development. These instruments are designed to simulate the dissolution of drug formulations in conditions most closely mimicking the gastrointestinal tract. In this work, we focused on the optimization of dissolution compartments/vessels for an updated version of the biorelevant dissolution apparatus-Golem v2. We designed eight compartments of uniform size but different inner geometry. The dissolution performance of the compartments was tested using immediate release caffeine tablets and evaluated by standard statistical methods and principal component analysis. Based on two phases of dissolution testing (using 250 and 100 mL of dissolution medium), we selected two compartment types yielding the highest measurement reproducibility. We also confirmed a statistically ssignificant effect of agitation rate and dissolution volume on the extent of drug dissolved and measurement reproducibility.

Hydrodynamical simulations of the barred spiral galaxy NGC 1300. Dynamical interpretation of observations

NASA Astrophysics Data System (ADS)

Lindblad, P. A. B.; Kristen, H.

1996-09-01

We perform two-dimensional time dependent hydrodynamical simulations of the barred spiral galaxy NGC 1300. The input potential is divided into an axisymmetric part mainly derived from the observed rotation curve, and a perturbing part obtained from near infrared surface photometry of the bar and spiral structure. Self-gravitation of the gas is not taken into account in our modeling. A pure bar perturbed model is unable to reproduce the observations. It was found necessary to add a weak spiral potential to the perturbation, thus suggesting the presence of massive spiral arms in NGC 1300. We find two models, differing mainly in pattern speed, which are able to reproduce the essentials of NGC 1300. The high pattern speed model has {OMEGA}_p_=20km/s/kpc, corresponding to a corotation radius at R_CR_~104"=1.3R_bar_. Furthermore, the adopted rotation curve for this model supports one ILR at R_ILR_~26" and an OLR at R_OLR_~188". The low pattern speed model has {OMEGA}_p_=12km/s/kpc, corresponding to a corotation radius at R_ CR_~190"=2.4R_bar_. The adopted rotation curve for this model, which differs from the fast pattern speed model, supports one ILR at R_ILR_~25" and an OLR at R_OLR_~305". Morphological features, like spiral arms and offset dust lanes, are basically reproduced by both models. They are driven by orbit crowding effects across various resonances, leading to density enhancements. The general velocity structure, as described by HI data and optical long slit measurements, is fairly consistent with the model velocities.

The study of multiphase flow control during odor reproduction

NASA Astrophysics Data System (ADS)

Luo, Dehan; Yu, Hao; Fan, Danjun; He, Meiqiu

2014-04-01

Odor reproduction, is the use of the chemical composition of the basic components of odor recipe, according to a certain proportion, to control the flow of the various components, which make them sufficiently blended to achieve reproduction. In this paper, reproducing method is to find the corresponding liquid flavor, and then based on chemical flavor recipes, using flowmeters to control the chemical composition of the liquid flavor ratio. In the proportional control, the liquid chemical composition is very likely to be volatile, so that the proportional control is multiphase flow control. Measurement of the flow control will directly affect the odor reproducible results. Using electronic nose to obtain reproducible odor data, and then use pattern recognition algorithm to determine reproducible results. The experimental results can be achieved on the process of odor components multiphase flow proportional control parameter adjustment.

Dermoscopic patterns of Melanoma Metastases: inter-observer consistency and accuracy for metastases recognition

PubMed Central

Costa, J.; Ortiz-Ibañez, K.; Salerni, G.; Borges, V.; Carrera, C.; Puig, S.; Malvehy, J.

2013-01-01

Background Cutaneous metastases of malignant melanoma (CMMM) can be confused with other skin lesions. Dermoscopy could be helpful in the differential diagnosis. Objective To describe distinctive dermoscopic patterns that are reproducible and accurate in the identification of CMMM Methods A retrospective study of 146 dermoscopic images of CMMM from 42 patients attending a Melanoma Unit between 2002 and 2009 was performed. Firstly, two investigators established six dermoscopic patterns for CMMM. The correlation of 73 dermoscopic images with their distinctive patterns was assessed by four independent dermatologists to evaluate the reproducibility in the identification of the patterns. Finally, 163 dermoscopic images, including CMMM and non-metastatic lesions, were evaluated by the same four dermatologists to calculate the accuracy of the patterns in the recognition of CMMM. Results Five CMMM dermoscopic patterns had a good inter-observer agreement (blue nevus-like, nevus-like, angioma like, vascular and unspecific). When CMMM were classified according to these patterns, correlation between the investigators and the four dermatologists ranged from κ = 0.56 to 0.7. 71 CMMM, 16 angiomas, 22 blue nevus, 15 malignant melanoma, 11 seborrheic keratosis, 15 melanocytic nevus with globular pattern and 13 pink lesions with vascular pattern were evaluated according to the previously described CMMM dermoscopy patterns, showing an overall sensitivity of 68% (between 54.9–76%) and a specificity of 81% (between 68.6–93.5) for the diagnosis of CMMM. Conclusion Five dermoscopic patterns of CMMM with good inter-observer agreement obtained a high sensitivity and specificity in the diagnosis of metastasis, the accuracy varying according to the experience of the observer. PMID:23495915

A multivariate cure model for left-censored and right-censored data with application to colorectal cancer screening patterns.

PubMed

Hagar, Yolanda C; Harvey, Danielle J; Beckett, Laurel A

2016-08-30

We develop a multivariate cure survival model to estimate lifetime patterns of colorectal cancer screening. Screening data cover long periods of time, with sparse observations for each person. Some events may occur before the study begins or after the study ends, so the data are both left-censored and right-censored, and some individuals are never screened (the 'cured' population). We propose a multivariate parametric cure model that can be used with left-censored and right-censored data. Our model allows for the estimation of the time to screening as well as the average number of times individuals will be screened. We calculate likelihood functions based on the observations for each subject using a distribution that accounts for within-subject correlation and estimate parameters using Markov chain Monte Carlo methods. We apply our methods to the estimation of lifetime colorectal cancer screening behavior in the SEER-Medicare data set. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PubMed Central

Cruchet, Steeve; Gustafson, Kyle; Benton, Richard; Floreano, Dario

2015-01-01

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. PMID:26600381

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

PubMed

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-05-15

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

Influences of environment and disturbance on forest patterns in coastal Oregon watersheds.

Treesearch

Michael C. Wimberly; Thomas A. Spies

2001-01-01

Modern ecology often emphasizes the distinction between traditional theories of stable, environmentally structured communities and a new paradigm of disturbance driven, nonequilibrium dynamics. However, multiple hypotheses for observed vegetation patterns have seldom been explicitly tested. We used multivariate statistics and variation partitioning methods to assess...

Organizational Change, Absenteeism, and Welfare Dependency

ERIC Educational Resources Information Center

Roed, Knut; Fevang, Elisabeth

2007-01-01

Based on Norwegian register data, we set up a multivariate mixed proportional hazard model (MMPH) to analyze nurses' pattern of work, sickness absence, nonemployment, and social insurance dependency from 1992 to 2000, and how that pattern was affected by workplace characteristics. The model is estimated by means of the nonparametric…

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Time-resolved imaging of domain pattern destruction and recovery via nonequilibrium magnetization states

NASA Astrophysics Data System (ADS)

Wessels, Philipp; Ewald, Johannes; Wieland, Marek; Nisius, Thomas; Vogel, Andreas; Viefhaus, Jens; Meier, Guido; Wilhein, Thomas; Drescher, Markus

2014-11-01

The destruction and formation of equilibrium multidomain patterns in permalloy (Ni80Fe20 ) microsquares has been captured using pump-probe x-ray magnetic circular dichroism (XMCD) spectromicroscopy at a new full-field magnetic transmission soft x-ray microscopy endstation with subnanosecond time resolution. The movie sequences show the dynamic magnetization response to intense Oersted field pulses of approximately 200-ps root mean square (rms) duration and the magnetization reorganization to the ground-state domain configuration. The measurements display how a vortex flux-closure magnetization distribution emerges out of a nonequilibrium uniform single-domain state. During the destruction of the initial vortex pattern, we have traced the motion of the central vortex core that is ejected out of the microsquare at high velocities exceeding 1 km/s. A reproducible recovery into a defined final vortex state with stable chirality and polarity could be achieved. Using an additional external bias field, the transient reversal of the square magnetization direction could be monitored and consistently reproduced by micromagnetic simulations.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

Identification of Legionella Species by Random Amplified Polymorphic DNA Profiles

PubMed Central

Lo Presti, François; Riffard, Serge; Vandenesch, François; Etienne, Jerome

1998-01-01

Random amplified polymorphic DNA (RAPD) was used for the identification of Legionella species. Primer SK2 (5′-CGGCGGCGGCGG-3′) and standardized RAPD conditions gave the technique a reproducibility of 93 to 100%, depending on the species tested. Species-specific patterns corresponding to the 42 Legionella species were consequently defined by this method; the patterns were dependent on the recognition of a core of common bands for each species. This specificity was demonstrated by testing 65 type strains and 265 environmental and clinical isolates. No serogroup-specific profiles were obtained. A number of unidentified Legionella isolates potentially corresponding to new species were clustered in four groups. RAPD analysis appears to be a rapid and reproducible technique for identification of Legionella isolates to the species level without further restriction or hybridization. PMID:9774564

Anisotropic mechanical behavior of an injection molded short fiber reinforced thermoplastic

NASA Astrophysics Data System (ADS)

Lopez, Delphine; Thuillier, Sandrine; Bessières, Nicolas; Grohens, Yves

2016-10-01

A short fiber reinforced thermoplastic was injected into a rectangular mold, in order to prepare samples to characterize the mechanical behavior of the material. The injection process was simulated with Moldflow and a cutting pattern was deduced from the predicted fiber orientation, leading to samples with several well-defined orientations with respect to the injection direction. Monotonic tensile tests up to rupture, as well as complex cycles made of loading steps followed by relaxation steps at different strain levels were performed, in order to check the reproducibility for a given orientation. Moreover, the fiber orientation in the central part of the tensile samples was also analyzed with X-ray tomography. The results show that the mechanical behavior for each orientation (among 6) was rather reproducible, thus validating the cutting pattern.

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

Multi-scale Predictability with a New Coupled Non-hydrostatic Global Model over the Arctic Annual Report

DTIC Science & Technology

2013-09-30

tropopause polar vortices, which are prevalent circulation features over the Arctic that play a major role in the evolution of surface pressure anomalies...pressure and tropospheric circulation anomalies and will allow us to answer specific questions regarding its ability to reproduce the appropriate AO... circulation patterns (Fig. 1c). While the mean patterns are favorable regarding the ability of MPAS to encapsulate the overall patterns of these two

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening

PubMed Central

Choi, Insook

2018-01-01

Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the absence of sonification theory, extensibility demonstrates relevant methods across case studies. The interactive sonification framework foregrounds three factors: reproducible system implementation for generating sonification; interactive mechanisms enhancing a listener's multisensory observations; and reproducible data from models that characterize emergent behavior. Supramodal attention research suggests interactive exploration with auditory feedback can generate context for recognizing irregular patterns and transient dynamics. The sonification framework provides circular causality as a signal pathway for modeling a listener interacting with emergent behavior. The extensible sonification model adopts a data acquisition pathway to formalize functional symmetry across three subsystems: Experimental Data Source, Sound Generation, and Guided Exploration. To differentiate time criticality and dimensionality of emerging dynamics, tuning functions are applied between subsystems to maintain scale and symmetry of concurrent processes and temporal dynamics. Tuning functions accommodate sonification design strategies that yield order parameter values to render emerging patterns discoverable as well as rehearsable, to reproduce desired instances for clinical listeners. Case studies are implemented with two computational models, Chua's circuit and Swarm Chemistry social agent simulation, generating data in real-time that exhibits emergent behavior. Heuristic Listening is introduced as an informal model of a listener's clinical attention to data sonification through multisensory interaction in a context of structured inquiry. Three methods are introduced to assess the proposed sonification framework: Listening Scenario classification, data flow Attunement, and Sonification Design Patterns to classify sound control. Case study implementations are assessed against these methods comparing levels of abstraction between experimental data and sound generation. Outcomes demonstrate the framework performance as a reference model for representing experimental implementations, also for identifying common sonification structures having different experimental implementations, identifying common functions implemented in different subsystems, and comparing impact of affordances across multiple implementations of listening scenarios. PMID:29755311

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Interannual tropical Pacific sea surface temperature anomalies teleconnection to Northern Hemisphere atmosphere in November

NASA Astrophysics Data System (ADS)

King, Martin P.; Herceg-Bulić, Ivana; Kucharski, Fred; Keenlyside, Noel

2018-03-01

We investigate the Northern Hemisphere atmospheric circulation anomalies associated to the sea surface temperature (SST) anomalies that are related to the eastern-Pacific and central-Pacific El Nino-Southern Oscillations in the late autumn (November). This research is motivated by the need for improving understanding of the autumn climate conditions which can impact on winter climate, as well as the relative lack of study on the boreal autumn climate processes compared to winter. Using reanalysis and SST datasets available from the late nineteenth century through the recent years, we found that there are two major atmospheric responses; one is a hemispheric-wide wave number-4 pattern, another has a more annular pattern. Both of these project on the East Atlantic pattern (southward-shifted North Atlantic Oscillation) in the Atlantic sector. Which of the patterns is active is suggested to depend on the background mean flow, with the annular anomaly active in the most recent decades, while the wave-4 pattern in the decades before. This switch is associated with a change of correlation sign in the North Pacific. We discuss the robustness of this finding. The ability of two atmospheric general circulation models (ICTP-AGCM and ECHAM-AGCM) to reproduce the teleconnections is also examined. Evidence provided shows that the wave-4 pattern and the East Atlantic pattern signals can be reproduced by the models, while the shift from this to an annular response for the recent years is not found conclusively.

Reproducibility patterns of multiple rapid swallows during high resolution esophageal manometry provide insights into esophageal pathophysiology.

PubMed

Price, L H; Li, Y; Patel, A; Gyawali, C Prakash

2014-05-01

Multiple rapid swallows (MRS) during esophageal high resolution manometry (HRM) assess esophageal neuromuscular integrity by evaluating postdeglutitive inhibition and rebound contraction, but most reports performed only a single MRS sequence. We assessed patterns of MRS reproducibility during clinical HRM in comparison to a normal cohort. Consecutive clinical HRM studies were included if two separate MRS sequences (four to six rapid swallows ≤4 s apart) were successfully performed. Chicago Classification diagnoses were identified; contraction wave abnormalities were additionally recorded. MRS-induced inhibition (contraction ≤3 cm during inhibition phase) and rebound contraction was assessed, and findings compared to 18 controls (28.0 ± 0.7 year, 50.0% female). Reproducibility consisted of similar inhibition and contraction responses with both sequences; discordance was segregated into inhibition and contraction phases. Multiple rapid swallows were successfully performed in 89.3% patients and all controls; 225 subjects (56.2 ± 0.9 year, 62.7% female) met study inclusion criteria. Multiple rapid swallows were reproducible in 76.9% patients and 94.4% controls (inhibition phase: 88.0% vs 94.4%, contraction phase 86.7% vs 100%, respectively, p = ns). A gradient of reproducibility was noted, highest in well-developed motor disorders (achalasia spectrum, hypermotility disorders, and aperistalsis, 91.7-100%, p = ns compared to controls); and lower in lesser motor disorders (contraction wave abnormalities, esophageal body hypomotility) or normal studies (62.2-70.8%, p < 0.0001 compared to well-developed motor disorders). Inhibition phase was most discordant in contraction wave abnormalities, while contraction phase was most discordant when studies were designated normal. Multiple rapid swallows are highly reproducible, especially in well-developed motor disorders, and complement the standard wet swallow manometry protocol. © 2014 John Wiley & Sons Ltd.

Uncertainty analysis of atmospheric deposition simulation of radiocesium and radioiodine from Fukushima Daiichi Nuclear Power Plant

NASA Astrophysics Data System (ADS)

Morino, Yu; Ohara, Toshimasa; Yumimoto, Keiya

2014-05-01

Chemical transport models (CTM) played key roles in understanding the atmospheric behaviors and deposition patterns of radioactive materials emitted from the Fukushima Daiichi nuclear power plant (FDNPP) after the nuclear accident that accompanied the great Tohoku earthquake and tsunami on 11 March 2011. In this study, we assessed uncertainties of atmospheric simulation by comparing observed and simulated deposition of radiocesium (137Cs) and radioiodine (131I). Airborne monitoring survey data were used to assess the model performance of 137Cs deposition patterns. We found that simulation using emissions estimated with a regional-scale (~500 km) CTM better reproduced the observed 137Cs deposition pattern in eastern Japan than simulation using emissions estimated with local-scale (~50 km) or global-scale CTM. In addition, we estimated the emission amount of 137Cs from FDNPP by combining a CTM, a priori source term, and observed deposition data. This is the first use of airborne survey data of 137Cs deposition (more than 16,000 data points) as the observational constraints in inverse modeling. The model simulation driven by a posteriori source term achieved better agreements with 137Cs depositions measured by aircraft survey and at in-situ stations over eastern Japan. Wet deposition module was also evaluated. Simulation using a process-based wet deposition module reproduced the observations well, whereas simulation using scavenging coefficients showed large uncertainties associated with empirical parameters. The best-available simulation reproduced the observed 137Cs deposition rates in high-deposition areas (≥10 kBq m-2) within one order of magnitude. Recently, 131I deposition map was released and helped to evaluate model performance of 131I deposition patterns. Observed 131I/137Cs deposition ratio is higher in areas southwest of FDNPP than northwest of FDNPP, and this behavior was roughly reproduced by a CTM if we assume that released 131I is more in gas phase than particles. Analysis of 131I deposition gives us better constraint for the atmospheric simulation of 131I, which is important in assessing public radiation exposure.

Electronic noses and tongues: Applications for the food and pharmaceutical industries

USDA-ARS?s Scientific Manuscript database

The electronic nose (enose) is designed to crudely mimic the human brain in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern rec...

Possible role of the microbiome in the development of acute malnutrition and implications for food-based strategies to prevent and treat acute malnutrition

USDA-ARS?s Scientific Manuscript database

A pattern of changes in the microbiome composition have been observed in the normal maturation of the human gut. Perturbations from this pattern have been described in malnourished humans and reproduced in animal models of severe malnutrition. Treatment and prevention of malnutrition in the future m...

Erosion patterns in a sediment layer

NASA Astrophysics Data System (ADS)

Daerr, Adrian; Lee, Peter; Lanuza, José; Clément, Éric

2003-06-01

We report here on a laboratory-scale experiment which reproduces a rich variety of natural patterns with few control parameters. In particular, we focus on intriguing rhomboid structures often found on sandy shores and flats. We show that the standard views based on water surface waves do not explain the phenomenon, and we evidence a different mechanism based on mud avalanche instability.

Traveling waves in a magnetized Taylor-Couette flow.

PubMed

Liu, Wei; Goodman, Jeremy; Ji, Hantao

2007-07-01

We investigate numerically a traveling wave pattern observed in experimental magnetized Taylor-Couette flow at low magnetic Reynolds number. By accurately modeling viscous and magnetic boundaries in all directions, we reproduce the experimentally measured wave patterns and their amplitudes. Contrary to previous claims, the waves are shown to be transiently amplified disturbances launched by viscous boundary layers, rather than globally unstable magnetorotational modes.

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical Coherence Tomography.

PubMed

Chen, Binyao; Gao, Enting; Chen, Haoyu; Yang, Jianling; Shi, Fei; Zheng, Ce; Zhu, Weifang; Xiang, Dehui; Chen, Xinjian; Zhang, Mingzhi

2016-01-01

To investigate the profile and determinants of retinal optical intensity in normal subjects using 3D spectral domain optical coherence tomography (SD OCT). A total of 231 eyes from 231 healthy subjects ranging in age from 18 to 80 years were included and underwent a 3D OCT scan. Forty-four eyes were randomly chosen to be scanned by two operators for reproducibility analysis. Distribution of optical intensity of each layer and regions specified by the Early Treatment of Diabetic Retinopathy Study (ETDRS) were investigated by analyzing the OCT raw data with our automatic graph-based algorithm. Univariate and multivariate analyses were performed between retinal optical intensity and sex, age, height, weight, spherical equivalent (SE), axial length, image quality, disc area and rim/disc area ratio (R/D area ratio). For optical intensity measurements, the intraclass correlation coefficient of each layer ranged from 0.815 to 0.941, indicating good reproducibility. Optical intensity was lowest in the central area of retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer and photoreceptor layer, except for the retinal pigment epithelium (RPE). Optical intensity was positively correlated with image quality in all retinal layers (0.553<β<0.851, p<0.01), and negatively correlated with age in most retinal layers (-0.362<β<-0.179, p<0.01), except for the RPE (β = 0.456, p<0.01), outer nuclear layer and photoreceptor layer (p>0.05). There was no relationship between retinal optical intensity and sex, height, weight, SE, axial length, disc area and R/D area ratio. There was a specific pattern of distribution of retinal optical intensity in different regions. The optical intensity was affected by image quality and age. Image quality can be used as a reference for normalization. The effect of age needs to be taken into consideration when using OCT for diagnosis.

Factors associated with disease expression patterns in systemic lupus erythematosus patients: results from LUMINA (LXXVII), a multiethnic US cohort.

PubMed

Ugarte-Gil, M F; Pimentel-Quiroz, V R; Vilá, L M; Reveille, J D; McGwin, G; Alarcón, G S

2017-05-01

Objective The objective of this study was to determine the association of disease expression patterns with demographic and clinical characteristics in SLE. Methods Patients from a multi-ethnic SLE cohort were included. Disease expression patterns were defined as acute SLE and insidious SLE; this group was divided into those who accrued three ACR criteria and then accrued the fourth (insidious pattern A) and those who have one or two and then accrued four criteria (insidious pattern B). Disease activity was ascertained with the SLAM-R and disease damage with SLICC/ACR damage index. Variables were compared using analysis of variance for numeric variables and χ 2 for categorical variables. Multivariable analyses adjusting for possible confounders were performed. Results Six hundred and forty patients were included; the most frequent pattern was the insidious pattern B, with 415 (64.8%) patients, followed by the acute SLE group with 115 (18.0%) and the insidious pattern A with 110 (17.2%) patients. Patients from the insidious pattern A were older at diagnosis (pattern A: 39.8 vs pattern B: 36.7 vs acute: 32.4 years; p < 0.0001), more educated (13.6 vs 13.1 vs 12.1; p = 0.0008) and with a less active disease at baseline (8.8 vs 9.2 vs 10.7; p = 0.0227). Caucasian and Hispanic (Puerto Rico) ethnicities were overrepresented in this group (40.0% vs 27.7% vs 19.1% and 18.2% vs 17.1% vs 9.6%; p = 0.0003). Conclusions More insidious onset is associated with older age, Caucasian ethnicity, higher level of education, and lower disease activity than those with acute onset. However, after multivariable analyses, disease activity was not associated with any disease expression pattern.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Annealing a magnetic cactus into phyllotaxis

NASA Astrophysics Data System (ADS)

Nisoli, Cristiano; Gabor, Nathaniel M.; Lammert, Paul E.; Maynard, J. D.; Crespi, Vincent H.

2010-04-01

The appearance of mathematical regularities in the disposition of leaves on a stem, scales on a pine-cone, and spines on a cactus has puzzled scholars for millennia; similar so-called phyllotactic patterns are seen in self-organized growth, polypeptides, convection, magnetic flux lattices and ion beams. Levitov showed that a cylindrical lattice of repulsive particles can reproduce phyllotaxis under the (unproved) assumption that minimum of energy would be achieved by two-dimensional Bravais lattices. Here we provide experimental and numerical evidence that the Phyllotactic lattice is actually a ground state. When mechanically annealed, our experimental “magnetic cactus” precisely reproduces botanical phyllotaxis, along with domain boundaries (called transitions in Botany) between different phyllotactic patterns. We employ a structural genetic algorithm to explore the more general axially unconstrained case, which reveals multijugate (multiple spirals) as well as monojugate (single-spiral) phyllotaxis.

Extended Eden model reproduces growth of an acellular slime mold.

PubMed

Wagner, G; Halvorsrud, R; Meakin, P

1999-11-01

A stochastic growth model was used to simulate the growth of the acellular slime mold Physarum polycephalum on substrates where the nutrients were confined in separate drops. Growth of Physarum on such substrates was previously studied experimentally and found to produce a range of different growth patterns [Phys. Rev. E 57, 941 (1998)]. The model represented the aging of cluster sites and differed from the original Eden model in that the occupation probability of perimeter sites depended on the time of occupation of adjacent cluster sites. This feature led to a bias in the selection of growth directions. A moderate degree of persistence was found to be crucial to reproduce the biological growth patterns under various conditions. Persistence in growth combined quick propagation in heterogeneous environments with a high probability of locating sources of nutrients.

Extended Eden model reproduces growth of an acellular slime mold

NASA Astrophysics Data System (ADS)

Wagner, Geri; Halvorsrud, Ragnhild; Meakin, Paul

1999-11-01

A stochastic growth model was used to simulate the growth of the acellular slime mold Physarum polycephalum on substrates where the nutrients were confined in separate drops. Growth of Physarum on such substrates was previously studied experimentally and found to produce a range of different growth patterns [Phys. Rev. E 57, 941 (1998)]. The model represented the aging of cluster sites and differed from the original Eden model in that the occupation probability of perimeter sites depended on the time of occupation of adjacent cluster sites. This feature led to a bias in the selection of growth directions. A moderate degree of persistence was found to be crucial to reproduce the biological growth patterns under various conditions. Persistence in growth combined quick propagation in heterogeneous environments with a high probability of locating sources of nutrients.

Reproducibility and diagnostic performance of shear wave elastography in evaluating breast solid mass.

PubMed

Hong, Sun; Woo, Ok Hee; Shin, Hye Seon; Hwang, Soon-Young; Cho, Kyu Ran; Seo, Bo Kyoung

Shear wave elastography (SWE) was performed independently by two radiologists in 264 solid breast masses. The images were reviewed for color overlay pattern (COP) classification by the two radiologists, double blinded to any information. The interobserver agreement of the COP was almost perfect (κ=0.908) and high in E max (ICC=0.89). The AUC value of the COP (0.954) was significantly higher than that of E max (0.915) (p=0.002) but not significantly different from that of E max combined with COP (0.957) (p=0.098). The SWE color overlay pattern and E max of breast masses were highly reproducible. The COP had better diagnostic ability than E max , suggesting that COP may be a more reliable parameter for solid breast mass evaluation. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamical systems techniques reveal the sexual dimorphic nature of motor patterns in birdsong

NASA Astrophysics Data System (ADS)

Mendez, J. M.; Alliende, J. A.; Amador, A.; Mindlin, G. B.

2006-10-01

In this work we analyze the pressure motor patterns used by canaries (Serinus canaria) during song, both in the cases of males and testosterone treated females. We found a qualitative difference between them which was not obvious from the acoustical features of the uttered songs. We also show the diversity of patterns, both for males and females, to be consistent with a recently proposed model for the dynamics of the oscine respiratory system. The model not only allows us to reproduce qualitative features of the different pressure patterns, but also to account for all the diversity of pressure patterns found in females.

Functional MRI follow-up study of language processes in healthy subjects and during recovery in a case of aphasia.

PubMed

Fernandez, Bruno; Cardebat, Dominique; Demonet, Jean-François; Joseph, Pierre Alain; Mazaux, Jean-Michel; Barat, Michel; Allard, Michèle

2004-09-01

The goal of this study was to develop a functional MRI (fMRI) paradigm robust and reproducible enough in healthy subjects to be adapted for a follow-up study aiming at evaluating the anatomical substratum of recovery in poststroke aphasia. Ten right-handed subjects were studied longitudinally using fMRI (7 of them being scanned twice) and compared with a patient with conduction aphasia during the first year of stroke recovery. Controls exhibited reproducible activation patterns between subjects and between sessions during language tasks. In contrast, the patient exhibited dynamic changes in brain activation pattern, particularly in the phonological task, during the 2 fMRI sessions. At 1 month after stroke, language homotopic right areas were recruited, whereas large perilesional left involvement occurred later (12 months). We first demonstrate intersubject robustness and intrasubject reproducibility of our paradigm in 10 healthy subjects and thus its validity in a patient follow-up study over a stroke recovery time course. Indeed, results suggest a spatiotemporal poststroke brain reorganization involving both hemispheres during the recovery course, with an early implication of a new contralateral functional neural network and a later implication of an ipsilateral one.

Testing the Accuracy of Different A-Axis Types for Measuring the Orientation of Bones in the Archaeological and Paleontological Record

PubMed Central

Domínguez-Rodrigo, Manuel; García-Pérez, Alfonso

2013-01-01

Orientation of archaeological and paleontological materials plays a prominent role in the interpretation of site formation processes. Allochthony and authochthony are frequently assumed from orientation patterns or lack thereof. Although it is still debated to what extent orientation of items can be produced in original depositional contexts, the recent use of GIS tools to measure orientations has highlighted several ways of reproducing A-axes with which to address these taphonomic issues. In the present study, the three most relevant A-axis types are compared to test their accuracy in reproducing water current direction. Although results may be similar in specific bone shapes, differences are important in other shapes. As known in engineering working with wind and fluid mechanics (developing shape optimization), longitudinal symmetrical axes (LSA) are the one that best orient structures against or in the same direction of wind and water. The present work shows that this is also the case for bones (regardless of shape), since LSA produce the most accurate estimates of flow direction. This has important consequences for the interpretation of orientation patterns at sites, since this type of axis is still not properly reproduced by GIS available tools. PMID:23874825

Spatial variability in intertidal macroalgal assemblages on the North Portuguese coast: consistence between species and functional group approaches

NASA Astrophysics Data System (ADS)

Veiga, P.; Rubal, M.; Vieira, R.; Arenas, F.; Sousa-Pinto, I.

2013-03-01

Natural assemblages are variable in space and time; therefore, quantification of their variability is imperative to identify relevant scales for investigating natural or anthropogenic processes shaping these assemblages. We studied the variability of intertidal macroalgal assemblages on the North Portuguese coast, considering three spatial scales (from metres to 10 s of kilometres) following a hierarchical design. We tested the hypotheses that (1) spatial pattern will be invariant at all the studied scales and (2) spatial variability of macroalgal assemblages obtained by using species will be consistent with that obtained using functional groups. This was done considering as univariate variables: total biomass and number of taxa as well as biomass of the most important species and functional groups and as multivariate variables the structure of macroalgal assemblages, both considering species and functional groups. Most of the univariate results confirmed the first hypothesis except for the total number of taxa and foliose macroalgae that showed significant variability at the scale of site and area, respectively. In contrast, when multivariate patterns were examined, the first hypothesis was rejected except at the scale of 10 s of kilometres. Both uni- and multivariate results indicated that variation was larger at the smallest scale, and thus, small-scale processes seem to have more effect on spatial variability patterns. Macroalgal assemblages, both considering species and functional groups as surrogate, showed consistent spatial patterns, and therefore, the second hypothesis was confirmed. Consequently, functional groups may be considered a reliable biological surrogate to study changes on macroalgal assemblages at least along the investigated Portuguese coastline.

Brain organization underlying superior mathematical abilities in children with autism.

PubMed

Iuculano, Teresa; Rosenberg-Lee, Miriam; Supekar, Kaustubh; Lynch, Charles J; Khouzam, Amirah; Phillips, Jennifer; Uddin, Lucina Q; Menon, Vinod

2014-02-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.

PubMed

Wang, X; Jiao, Y; Tang, T; Wang, H; Lu, Z

2013-12-19

Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Analysis of factors affecting the accuracy, reproducibility, and interpretation of microbial community carbon source utilization patterns

USGS Publications Warehouse

Haack, S.K.; Garchow, H.; Klug, M.J.; Forney, L.J.

1995-01-01

We determined factors that affect responses of bacterial isolates and model bacterial communities to the 95 carbon substrates in Biolog microliter plates. For isolates and communities of three to six bacterial strains, substrate oxidation rates were typically nonlinear and were delayed by dilution of the inoculum. When inoculum density was controlled, patterns of positive and negative responses exhibited by microbial communities to each of the carbon sources were reproducible. Rates and extents of substrate oxidation by the communities were also reproducible but were not simply the sum of those exhibited by community members when tested separately. Replicates of the same model community clustered when analyzed by principal- components analysis (PCA), and model communities with different compositions were clearly separated un the first PCA axis, which accounted for >60% of the dataset variation. PCA discrimination among different model communities depended on the extent to which specific substrates were oxidized. However, the substrates interpreted by PCA to be most significant in distinguishing the communities changed with reading time, reflecting the nonlinearity of substrate oxidation rates. Although whole-community substrate utilization profiles were reproducible signatures for a given community, the extent of oxidation of specific substrates and the numbers or activities of microorganisms using those substrates in a given community were not correlated. Replicate soil samples varied significantly in the rate and extent of oxidation of seven tested substrates, suggesting microscale heterogeneity in composition of the soil microbial community.

MULTIVARIATE ANALYSIS OF DRINKING BEHAVIOUR IN A RURAL POPULATION

PubMed Central

Mathrubootham, N.; Bashyam, V.S.P.; Shahjahan

1997-01-01

This study was carried out to find out the drinking pattern in a rural population, using multivariate techniques. 386 current users identified in a community were assessed with regard to their drinking behaviours using a structured interview. For purposes of the study the questions were condensed into 46 meaningful variables. In bivariate analysis, 14 variables including dependent variables such as dependence, MAST & CAGE (measuring alcoholic status), Q.F. Index and troubled drinking were found to be significant. Taking these variables and other multivariate techniques too such as ANOVA, correlation, regression analysis and factor analysis were done using both SPSS PC + and HCL magnum mainframe computer with FOCUS package and UNIX systems. Results revealed that number of factors such as drinking style, duration of drinking, pattern of abuse, Q.F. Index and various problems influenced drinking and some of them set up a vicious circle. Factor analysis revealed mainly 3 factors, abuse, dependence and social drinking factors. Dependence could be divided into low/moderate dependence. The implications and practical applications of these tests are also discussed. PMID:21584077

Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests

PubMed Central

Ganz, Melanie; Greve, Douglas N.; Fischl, Bruce; Konukoglu, Ender

2015-01-01

Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data’s multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods’ user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a “knock-out” strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach. PMID:26272728

Connecting Self-Esteem and Achievement: Diversity in Academic Identification and Dis-Identification Patterns among Black College Students

ERIC Educational Resources Information Center

Hope, Elan C.; Chavous, Tabbye M.; Jagers, Robert J.; Sellers, Robert M.

2013-01-01

Using a person-oriented approach, we explored patterns of self-esteem and achievement among 324 Black college students across the freshman college year and identified four academic identification profiles. Multivariate analyses revealed profile differences in academic and psychological outcomes at beginning and end of freshman year (academic…

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

NASA Technical Reports Server (NTRS)

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; Bosilovich, Michael G.; Lee, Jaechoul; Wehner, Michael F.; Collow, Allison

2016-01-01

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

DOE PAGES

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; ...

2016-02-03

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scalemore » patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.« less

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

USGS Publications Warehouse

McKenna, J.E.

2003-01-01

The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher's knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

PubMed Central

2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

NASA Astrophysics Data System (ADS)

Radic, V.; Unglert, K.; Jellinek, M.

2016-12-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2017-01-15

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Atomic Force Microscopy Mechanical Mapping of Micropatterned Cells Shows Adhesion Geometry-Dependent Mechanical Response on Local and Global Scales

PubMed Central

Rigato, Annafrancesca; Rico, Felix; Eghiaian, Frédéric; Piel, Mathieu; Scheuring, Simon

2015-01-01

In multicellular organisms cell shape and organization are dictated by cell-cell or cell-extracellular matrix adhesion interactions. Adhesion complexes crosstalk with the cytoskeleton enabling cells to sense their mechanical environment. Unfortunately, most of cell biology studies, and cell mechanics studies in particular, are conducted on cultured cells adhering to a hard, homogeneous and unconstrained substrate with non-specific adhesion sites – thus far from physiological and reproducible conditions. Here, we grew cells on three different fibronectin patterns with identical overall dimensions but different geometries (▽, T and Y), and investigated their topography and mechanics by atomic force microscopy (AFM). The obtained mechanical maps were reproducible for cells grown on patterns of the same geometry, revealing pattern-specific subcellular differences. We found that local Young’s moduli variations are related to the cell adhesion geometry. Additionally, we detected local changes of cell mechanical properties induced by cytoskeletal drugs. We thus provide a method to quantitatively and systematically investigate cell mechanics and their variations, and present further evidence for a tight relation between cell adhesion and mechanics. PMID:26013956

Atomic Force Microscopy Mechanical Mapping of Micropatterned Cells Shows Adhesion Geometry-Dependent Mechanical Response on Local and Global Scales.

PubMed

Rigato, Annafrancesca; Rico, Felix; Eghiaian, Frédéric; Piel, Mathieu; Scheuring, Simon

2015-06-23

In multicellular organisms, cell shape and organization are dictated by cell-cell or cell-extracellular matrix adhesion interactions. Adhesion complexes crosstalk with the cytoskeleton enabling cells to sense their mechanical environment. Unfortunately, most of cell biology studies, and cell mechanics studies in particular, are conducted on cultured cells adhering to a hard, homogeneous, and unconstrained substrate with nonspecific adhesion sites, thus far from physiological and reproducible conditions. Here, we grew cells on three different fibronectin patterns with identical overall dimensions but different geometries (▽, T, and Y), and investigated their topography and mechanics by atomic force microscopy (AFM). The obtained mechanical maps were reproducible for cells grown on patterns of the same geometry, revealing pattern-specific subcellular differences. We found that local Young's moduli variations are related to the cell adhesion geometry. Additionally, we detected local changes of cell mechanical properties induced by cytoskeletal drugs. We thus provide a method to quantitatively and systematically investigate cell mechanics and their variations, and present further evidence for a tight relation between cell adhesion and mechanics.

A novel method for repeatedly generating speckle patterns used in digital image correlation

NASA Astrophysics Data System (ADS)

Zhang, Juan; Sweedy, Ahmed; Gitzhofer, François; Baroud, Gamal

2018-01-01

Speckle patterns play a key role in Digital Image Correlation (DIC) measurement, and generating an optimal speckle pattern has been the goal for decades now. The usual method of generating a speckle pattern is by manually spraying the paint on the specimen. However, this makes it difficult to reproduce the optimal pattern for maintaining identical testing conditions and achieving consistent DIC results. This study proposed and evaluated a novel method using an atomization system to repeatedly generate speckle patterns. To verify the repeatability of the speckle patterns generated by this system, simulation and experimental studies were systematically performed. The results from both studies showed that the speckle patterns and, accordingly, the DIC measurements become highly accurate and repeatable using the proposed atomization system.

Formation and maintenance of nitrogen-fixing cell patterns in filamentous cyanobacteria.

PubMed

Muñoz-García, Javier; Ares, Saúl

2016-05-31

Cyanobacteria forming one-dimensional filaments are paradigmatic model organisms of the transition between unicellular and multicellular living forms. Under nitrogen-limiting conditions, in filaments of the genus Anabaena, some cells differentiate into heterocysts, which lose the possibility to divide but are able to fix environmental nitrogen for the colony. These heterocysts form a quasiregular pattern in the filament, representing a prototype of patterning and morphogenesis in prokaryotes. Recent years have seen advances in the identification of the molecular mechanism regulating this pattern. We use these data to build a theory on heterocyst pattern formation, for which both genetic regulation and the effects of cell division and filament growth are key components. The theory is based on the interplay of three generic mechanisms: local autoactivation, early long-range inhibition, and late long-range inhibition. These mechanisms can be identified with the dynamics of hetR, patS, and hetN expression. Our theory reproduces quantitatively the experimental dynamics of pattern formation and maintenance for wild type and mutants. We find that hetN alone is not enough to play the role as the late inhibitory mechanism: a second mechanism, hypothetically the products of nitrogen fixation supplied by heterocysts, must also play a role in late long-range inhibition. The preponderance of even intervals between heterocysts arises naturally as a result of the interplay between the timescales of genetic regulation and cell division. We also find that a purely stochastic initiation of the pattern, without a two-stage process, is enough to reproduce experimental observations.

Perceptual aspects of reproduced sound in car cabin acoustics.

PubMed

Kaplanis, Neofytos; Bech, Søren; Tervo, Sakari; Pätynen, Jukka; Lokki, Tapio; van Waterschoot, Toon; Jensen, Søren Holdt

2017-03-01

An experiment was conducted to determine the perceptual effects of car cabin acoustics on the reproduced sound field. In-car measurements were conducted whilst the cabin's interior was physically modified. The captured sound fields were recreated in the laboratory using a three-dimensional loudspeaker array. A panel of expert assessors followed a rapid sensory analysis protocol, the flash profile, to perceptually characterize and evaluate 12 acoustical conditions of the car cabin using individually elicited attributes. A multivariate analysis revealed the panel's consensus and the identified perceptual constructs. Six perceptual constructs characterize the differences between the acoustical conditions of the cabin, related to bass, ambience, transparency, width and envelopment, brightness, and image focus. The current results indicate the importance of several acoustical properties of a car's interior on the perceived sound qualities. Moreover, they signify the capacity of the applied methodology in assessing spectral and spatial properties of automotive environments in laboratory settings using a time-efficient and flexible protocol.

T-pattern analysis for the study of temporal structure of animal and human behavior: a comprehensive review.

PubMed

Casarrubea, M; Jonsson, G K; Faulisi, F; Sorbera, F; Di Giovanni, G; Benigno, A; Crescimanno, G; Magnusson, M S

2015-01-15

A basic tenet in the realm of modern behavioral sciences is that behavior consists of patterns in time. For this reason, investigations of behavior deal with sequences that are not easily perceivable by the unaided observer. This problem calls for improved means of detection, data handling and analysis. This review focuses on the analysis of the temporal structure of behavior carried out by means of a multivariate approach known as T-pattern analysis. Using this technique, recurring sequences of behavioral events, usually hard to detect, can be unveiled and carefully described. T-pattern analysis has been successfully applied in the study of various aspects of human or animal behavior such as behavioral modifications in neuro-psychiatric diseases, route-tracing stereotypy in mice, interaction between human subjects and animal or artificial agents, hormonal-behavioral interactions, patterns of behavior associated with emesis and, in our laboratories, exploration and anxiety-related behaviors in rodents. After describing the theory and concepts of T-pattern analysis, this review will focus on the application of the analysis to the study of the temporal characteristics of behavior in different species from rodents to human beings. This work could represent a useful background for researchers who intend to employ such a refined multivariate approach to the study of behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

An Mobility Typology of US Cities

NASA Astrophysics Data System (ADS)

KC, B.; Stewart, R.; King, A. W.

2017-12-01

Urban mobility is a pressing problem and one growing with urbanization. Urban mobility, for example, accounts for 28 % of all CO2 emissions from road transport and restrictions in urban mobility have economic and social consequences. Occupational flow, movement to and from work, plays a vital role in shaping urban mobility patterns and is dependent on urban infrastructures as well as the geographical distribution of households and occupations. Urban mobility varies among different population subgroups such as race, age, and income in complex multivariate patterns. To explore and quantify these patterns, we use multivariate clustering to build a typology of urban mobility for the Metropolitan Statistical Areas of the United States using the occupational flow data from US Census Bureau's Longitudinal Employer-Household Dynamics- Origin-Destination Employment Statistics. We use characteristics such as work radius, connectivity, and number of jobs for different population subgroups such as income, age, and industry to define the typology, objectively classifying metropolitan areas with similar mobility patterns as belonging to the same mobility type. The mobility typology addresses whether urban areas with similar transportation infrastructure have similar mobility patterns. Additionally, similarities and differences in the mobility typology of the demographic groups provides valuable insights into overall mobility experience which can help transportation planners design equitable and sustainable transportation infrastructures.

Is race erased? Decoding race from patterns of neural activity when skin color is not diagnostic of group boundaries.

PubMed

Ratner, Kyle G; Kaul, Christian; Van Bavel, Jay J

2013-10-01

Several theories suggest that people do not represent race when it does not signify group boundaries. However, race is often associated with visually salient differences in skin tone and facial features. In this study, we investigated whether race could be decoded from distributed patterns of neural activity in the fusiform gyri and early visual cortex when visual features that often covary with race were orthogonal to group membership. To this end, we used multivariate pattern analysis to examine an fMRI dataset that was collected while participants assigned to mixed-race groups categorized own-race and other-race faces as belonging to their newly assigned group. Whereas conventional univariate analyses provided no evidence of race-based responses in the fusiform gyri or early visual cortex, multivariate pattern analysis suggested that race was represented within these regions. Moreover, race was represented in the fusiform gyri to a greater extent than early visual cortex, suggesting that the fusiform gyri results do not merely reflect low-level perceptual information (e.g. color, contrast) from early visual cortex. These findings indicate that patterns of activation within specific regions of the visual cortex may represent race even when overall activation in these regions is not driven by racial information.

Multivariate Analysis as a Method for Evaluating the Conceptual Perceptions of Korean Medicine Students regarding Phlegm Pattern

PubMed Central

Kim, Hyungsuk; Park, Young-Jae; Park, Young-Bae

2013-01-01

Individuals may perceive the concepts in Korean medicine pattern classification differently because it is performed according to the integration of a variety of information. Therefore, analysis about individual perspective is very important for examining the cross-sectional perspective state of Korean medicine concepts and developing both the clinical guideline including diagnosis and the curriculum of Korean medicine colleges. Moreover, because this conceptual difference is thought to begin with college education, it is worthwhile to observe students' viewpoints. So, we suggested multivariate analysis to explore the dimensional structure of Korean medicine students' conceptual perceptions regarding phlegm pattern. We surveyed 326 students divided into 5 groups based on their year of study. Data were analyzed using multidimensional scaling and factor analysis. Within-group difference was the smallest for third-year students, who have received Korean medicine education in full for the first time. With the exception of first-year students, the conceptual map revealed that each group's mean perceptions of phlegm pattern were distributed in almost linear fashion. To determine the effect of education, we investigated the preference rankings and scores of each symptom. We also extracted factors to identify latent variables and to compare the between-group conceptual characteristics regarding phlegm pattern. PMID:24062789

Variable environmental effects on a multicomponent sexually selected trait.

PubMed

Cole, Gemma L; Endler, John A

2015-04-01

Multicomponent signals are made up of interacting elements that generate a functional signaling unit. The interactions between signal components and their effects on individual fitness are not well understood, and the effect of environment is even less so. It is usually assumed that color patterns appear the same in all light environments and that the effects of each color are additive. Using guppies, Poecilia reticulata, we investigated the effect of water color on the interactions between components of sexually selected male coloration. Through behavioral mate choice trials in four different water colors, we estimated the attractiveness of male color patterns, using multivariate fitness estimates and overall signal contrast. Our results show that females exhibit preferences that favor groups of colors rather than individual colors independently and that each environment favors different color combinations. We found that these effects are consistent with female guppies selecting entire color patterns on the basis of overall visual contrast. This suggests that both individuals and populations inhabiting different light environments will be subject to divergent, multivariate selection. Although the appearance of color patterns changes with light environment, achromatic components change little, suggesting that these could function in species recognition or other aspects of communication that must work across environments. Consequently, we predict different phylogenetic patterns between chromatic and achromatic signals within the same clades.

qFeature

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Retrospective Analysis of Radiological Recurrence Patterns in Glioblastoma, Their Prognostic Value And Association to Postoperative Infarct Volume.

PubMed

Bette, Stefanie; Barz, Melanie; Huber, Thomas; Straube, Christoph; Schmidt-Graf, Friederike; Combs, Stephanie E; Delbridge, Claire; Gerhardt, Julia; Zimmer, Claus; Meyer, Bernhard; Kirschke, Jan S; Boeckh-Behrens, Tobias; Wiestler, Benedikt; Gempt, Jens

2018-03-14

Recent studies suggested that postoperative hypoxia might trigger invasive tumor growth, resulting in diffuse/multifocal recurrence patterns. Aim of this study was to analyze distinct recurrence patterns and their association to postoperative infarct volume and outcome. 526 consecutive glioblastoma patients were analyzed, of which 129 met our inclusion criteria: initial tumor diagnosis, surgery, postoperative diffusion-weighted imaging and tumor recurrence during follow-up. Distinct patterns of contrast-enhancement at initial diagnosis and at first tumor recurrence (multifocal growth/progression, contact to dura/ventricle, ependymal spread, local/distant recurrence) were recorded by two blinded neuroradiologists. The association of radiological patterns to survival and postoperative infarct volume was analyzed by uni-/multivariate survival analyses and binary logistic regression analysis. With increasing postoperative infarct volume, patients were significantly more likely to develop multifocal recurrence, recurrence with contact to ventricle and contact to dura. Patients with multifocal recurrence (Hazard Ratio (HR) 1.99, P = 0.010) had significantly shorter OS, patients with recurrent tumor with contact to ventricle (HR 1.85, P = 0.036), ependymal spread (HR 2.97, P = 0.004) and distant recurrence (HR 1.75, P = 0.019) significantly shorter post-progression survival in multivariate analyses including well-established prognostic factors like age, Karnofsky Performance Score (KPS), therapy, extent of resection and patterns of primary tumors. Postoperative infarct volume might initiate hypoxia-mediated aggressive tumor growth resulting in multifocal and diffuse recurrence patterns and impaired survival.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

PubMed

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

PubMed Central

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes.

PubMed

Casanova, Ramon; Espeland, Mark A; Goveas, Joseph S; Davatzikos, Christos; Gaussoin, Sarah A; Maldjian, Joseph A; Brunner, Robert L; Kuller, Lewis H; Johnson, Karen C; Mysiw, W Jerry; Wagner, Benjamin; Resnick, Susan M

2011-05-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers. Copyright © 2011 Elsevier Inc. All rights reserved.

Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients.

PubMed

Wang, Yinyan; Wang, Kai; Wang, Jiangfei; Li, Shaowu; Ma, Jun; Dai, Jianping; Jiang, Tao

2016-04-01

Contrast enhancement observable on magnetic resonance (MR) images reflects the destructive features of malignant gliomas. This study aimed to investigate the relationship between radiologic patterns of tumor enhancement, extent of resection, and prognosis in patients with anaplastic gliomas (AGs). Clinical data from 268 patients with histologically confirmed AGs were retrospectively analyzed. Contrast enhancement patterns were classified based on preoperative T1-contrast MR images. Univariate and multivariate analyses were performed to evaluate the prognostic value of MR enhancement patterns on progression-free survival (PFS) and overall survival (OS). The pattern of tumor contrast enhancement was associated with the extent of surgical resection in AGs. A gross total resection was more likely to be achieved for AGs with focal enhancement than those with diffuse (p = 0.001) or ring-like (p = 0.024) enhancement. Additionally, patients with focal-enhanced AGs had a significantly longer PFS and OS than those with diffuse (log-rank, p = 0.025 and p = 0.031, respectively) or ring-like (log-rank, p = 0.008 and p = 0.011, respectively) enhanced AGs. Furthermore, multivariate analysis identified the pattern of tumor enhancement as a significant predictor of PFS (p = 0.016, hazard ratio [HR] = 1.485) and OS (p = 0.030, HR = 1.446). Our results suggested that the contrast enhancement pattern on preoperative MR images was associated with the extent of resection and predictive of survival outcomes in AG patients.

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes

PubMed Central

Casanova, Ramon; Espeland, Mark A.; Goveas, Joseph S.; Davatzikos, Christos; Gaussoin, Sarah A.; Maldjian, Joseph A.; Brunner, Robert L.; Kuller, Lewis H.; Johnson, Karen C.; Mysiw, W. Jerry; Wagner, Benjamin; Resnick, Susan M.

2011-01-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years, however it is unknown whether this results in a broad-based characteristic pattern of effects. Structural MRI was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L1 penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and non-users. PMID:21292420

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

PubMed

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time.

PubMed

Leonardi, Nora; Shirer, William R; Greicius, Michael D; Van De Ville, Dimitri

2014-12-01

Resting-state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k-means clustering and sliding-window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task-based, and resting-state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject-driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting-state dFC. © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

Replaying evolutionary transitions from the dental fossil record

PubMed Central

Harjunmaa, Enni; Seidel, Kerstin; Häkkinen, Teemu; Renvoisé, Elodie; Corfe, Ian J.; Kallonen, Aki; Zhang, Zhao-Qun; Evans, Alistair R.; Mikkola, Marja L.; Salazar-Ciudad, Isaac; Klein, Ophir D.; Jernvall, Jukka

2014-01-01

The evolutionary relationships of extinct species are ascertained primarily through the analysis of morphological characters. Character inter-dependencies can have a substantial effect on evolutionary interpretations, but the developmental underpinnings of character inter-dependence remain obscure because experiments frequently do not provide detailed resolution of morphological characters. Here we show experimentally and computationally how gradual modification of development differentially affects characters in the mouse dentition. We found that intermediate phenotypes could be produced by gradually adding ectodysplasin A (EDA) protein in culture to tooth explants carrying a null mutation in the tooth-patterning gene Eda. By identifying development-based character interdependencies, we show how to predict morphological patterns of teeth among mammalian species. Finally, in vivo inhibition of sonic hedgehog signalling in Eda null teeth enabled us to reproduce characters deep in the rodent ancestry. Taken together, evolutionarily informative transitions can be experimentally reproduced, thereby providing development-based expectations for character state transitions used in evolutionary studies. PMID:25079326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parlinski, K.; Hashi, Y.; Tsunekawa, S.

A model of lanthanum orthoniobate which possesses a ferroelastic tetragonal-monoclinic phase transition is proposed. It contains only one particle per unit cell, but it is constructed consistently with symmetry changes at the phase transition. The model parameters are chosen to reproduce the bare soft mode, degree of deformation of the tetragonal unit cell to monoclinic one, and the phase transition temperature. The ferroelastic system with free boundary conditions was simulated by the molecular dynamics technique, and the second order phase transition was reproduced. The studied annealing process shows formation of the stripe lenticular domain pattern, which has been interrupted bymore » appearance of a temporary band of perpendicularly oriented lenticular domains. The maps contain W{sup {prime}}-type domain walls whose orientations are fixed only by interplay of potential parameters and not by symmetry elements. The simulated domain pattern has the same features as those observed by transmission electron microscopy. {copyright} {ital 1997 Materials Research Society.}« less

Reactive solute transport in acidic streams

USGS Publications Warehouse

Broshears, R.E.

1996-01-01

Spatial and temporal profiles of Ph and concentrations of toxic metals in streams affected by acid mine drainage are the result of the interplay of physical and biogeochemical processes. This paper describes a reactive solute transport model that provides a physically and thermodynamically quantitative interpretation of these profiles. The model combines a transport module that includes advection-dispersion and transient storage with a geochemical speciation module based on MINTEQA2. Input to the model includes stream hydrologic properties derived from tracer-dilution experiments, headwater and lateral inflow concentrations analyzed in field samples, and a thermodynamic database. Simulations reproduced the general features of steady-state patterns of observed pH and concentrations of aluminum and sulfate in St. Kevin Gulch, an acid mine drainage stream near Leadville, Colorado. These patterns were altered temporarily by injection of sodium carbonate into the stream. A transient simulation reproduced the observed effects of the base injection.

Human mobility: Models and applications

NASA Astrophysics Data System (ADS)

Barbosa, Hugo; Barthelemy, Marc; Ghoshal, Gourab; James, Charlotte R.; Lenormand, Maxime; Louail, Thomas; Menezes, Ronaldo; Ramasco, José J.; Simini, Filippo; Tomasini, Marcello

2018-03-01

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.

Nucleosynthesis in Hot Bubbles of SNe-Origin of EMP Stars: HNe or SNe ?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Izutani, Natsuko; Umeda, Hideyuki; Yoshida, Takashi

2010-08-12

The observational trends of extremely metal-poor (EMP) stars reflect SN nucleosynthesis of Population III, or almost metal-free stars. The observation of EMP stars can be reproduced by HNe, not by normal SNe. However, if the innermost neutron-rich or proton-rich matter is ejected, the abundance patterns of ejected matter are changed, and there is a possibility that normal SNe can also reproduce the observations of EMP stars. In this paper, we calculate nucleosynthesis with various Y{sub e} and entropy taking into account neutrino processes. We investigate whether normal SNe with this innermost matter can reproduce the observations of EMP stars. Wemore » find that neutron-rich (Y{sub e} = 0.45-0.50) and proton-rich (Y{sub e} = 0.51-0.55) matters can improve Zn and Co, but tend to overproduce other Fe-peak elements. On the other hand, HNe can naturally reproduce the observations of EMP stars.« less

Pattern Classification of Endocervical Adenocarcinoma: Reproducibility and Review of Criteria

PubMed Central

Rutgers, Joanne K.L.; Roma, Andres; Park, Kay; Zaino, Richard J.; Johnson, Abbey; Alvarado, Isabel; Daya, Dean; Rasty, Golnar; Longacre, Teri; Ronnett, Brigitte; Silva, Elvio

2017-01-01

Previously, our international team proposed a 3-tiered pattern classification (Pattern Classification) system for endocervical adenocarcinoma of the usual type that correlates with nodal disease and recurrence. Pattern Classification- A have well demarcated glands lacking destructive stromal invasion or lymphovascular invasion (lymphovascular invasion), Pattern Classification- B show localized, limited destructive invasion arising from A-type glands, and Pattern Classification- C have diffuse destructive stromal invasion, significant (filling a 4× field) confluence, or solid architecture. 24 Pattern Classification-A, 22 Pattern Classification-B, 38 Pattern Classification-C from the tumor set used in the original description were chosen using the reference diagnosis (reference diagnosis) originally established. 1 H&E slide per case was reviewed by 7 gynecologic pathologists, 4 from the original study. Kappa statistics were prepared, and cases with discrepancies reviewed. We found a majority agreement with reference diagnosis in 81% of cases, with complete or near complete (6 of 7) agreement in 50%. Overall concordance was 74%. Overall Kappa (agreement among pathologists) was .488 (moderate agreement). Pattern Classification- B has lowest kappa, and agreement is not improved by combining B+C. 6 of 7 reviewers had substantial agreement by weighted kappas (>.6), with one reviewer accounting for the majority of cases under or overcalled by 2 tiers. Confluence filling a 4× field, labyrinthine glands, or solid architecture accounted for undercalling other reference diagnosis-C cases. Missing a few individually infiltrative cells was the most common cause of undercalling reference diagnosis- B. Small foci of inflamed, loose or desmoplastic stroma lacking infiltrative tumor cells in reference diagnosis-A appeared to account for those cases up-graded to Pattern Classification-B. In summary, an overall concordance of 74% indicates that the criteria can be reproducibly applied by gynecologic pathologists. Further refinement of criteria should allow use of this powerful classification system to delineate which cervical adenocarcinomas can be safely treated conservatively. PMID:27255163

Dewetting of patterned solid films: Towards a predictive modelling approach

NASA Astrophysics Data System (ADS)

Trautmann, M.; Cheynis, F.; Leroy, F.; Curiotto, S.; Pierre-Louis, O.; Müller, P.

2017-06-01

Owing to its ability to produce an assembly of nanoislands with controllable size and locations, the solid state dewetting of patterned films has recently received great attention. A simple Kinetic Monte Carlo model based on two reduced energetic parameters allows one to reproduce experimental observations of the dewetting morphological evolution of patterned films of Si(001) on SiO2 (or SOI for Silicon-on-Insulator) with various pattern designs. Thus, it is now possible to use KMC to drive further experiments and to optimize the pattern shapes to reach a desired dewetted structure. Comparisons between KMC simulations and dewetting experiments, at least for wire-shaped patterns, show that the prevailing dewetting mechanism depends on the wire width.

Creation of Synthetic Surface Temperature and Precipitation Ensembles Through A Computationally Efficient, Mixed Method Approach

NASA Astrophysics Data System (ADS)

Hartin, C.; Lynch, C.; Kravitz, B.; Link, R. P.; Bond-Lamberty, B. P.

2017-12-01

Typically, uncertainty quantification of internal variability relies on large ensembles of climate model runs under multiple forcing scenarios or perturbations in a parameter space. Computationally efficient, standard pattern scaling techniques only generate one realization and do not capture the complicated dynamics of the climate system (i.e., stochastic variations with a frequency-domain structure). In this study, we generate large ensembles of climate data with spatially and temporally coherent variability across a subselection of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. First, for each CMIP5 model we apply a pattern emulation approach to derive the model response to external forcing. We take all the spatial and temporal variability that isn't explained by the emulator and decompose it into non-physically based structures through use of empirical orthogonal functions (EOFs). Then, we perform a Fourier decomposition of the EOF projection coefficients to capture the input fields' temporal autocorrelation so that our new emulated patterns reproduce the proper timescales of climate response and "memory" in the climate system. Through this 3-step process, we derive computationally efficient climate projections consistent with CMIP5 model trends and modes of variability, which address a number of deficiencies inherent in the ability of pattern scaling to reproduce complex climate model behavior.

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Dietary Patterns and Body Mass Index in Children with Autism and Typically Developing Children

PubMed Central

Evans, E. Whitney; Must, Aviva; Anderson, Sarah E.; Curtin, Carol; Scampini, Renee; Maslin, Melissa; Bandini, Linda

2012-01-01

To determine whether dietary patterns (juice and sweetened non-dairy beverages, fruits, vegetables, fruits & vegetables, snack foods, and kid’s meals) and associations between dietary patterns and body mass index (BMI) differed between 53 children with autism spectrum disorders (ASD) and 58 typically developing children, ages 3 to 11, multivariate regression models including interaction terms were used. Children with ASD were found to consume significantly more daily servings of sweetened beverages (2.6 versus 1.7, p=0.03) and snack foods (4.0 versus 3.0, p=0.01) and significantly fewer daily servings of fruits and vegetables (3.1 versus 4.4, p=0.006) than typically developing children. There was no evidence of statistical interaction between any of the dietary patterns and BMI z-score with autism status. Among all children, fruits and vegetables (p=0.004) and fruits alone (p=0.005) were positively associated with BMI z-score in our multivariate models. Children with ASD consume more energy-dense foods than typically developing children; however, in our sample, only fruits and vegetables were positively associated with BMI z-score. PMID:22936951

The neural basis of visual word form processing: a multivariate investigation.

PubMed

Nestor, Adrian; Behrmann, Marlene; Plaut, David C

2013-07-01

Current research on the neurobiological bases of reading points to the privileged role of a ventral cortical network in visual word processing. However, the properties of this network and, in particular, its selectivity for orthographic stimuli such as words and pseudowords remain topics of significant debate. Here, we approached this issue from a novel perspective by applying pattern-based analyses to functional magnetic resonance imaging data. Specifically, we examined whether, where and how, orthographic stimuli elicit distinct patterns of activation in the human cortex. First, at the category level, multivariate mapping found extensive sensitivity throughout the ventral cortex for words relative to false-font strings. Secondly, at the identity level, the multi-voxel pattern classification provided direct evidence that different pseudowords are encoded by distinct neural patterns. Thirdly, a comparison of pseudoword and face identification revealed that both stimulus types exploit common neural resources within the ventral cortical network. These results provide novel evidence regarding the involvement of the left ventral cortex in orthographic stimulus processing and shed light on its selectivity and discriminability profile. In particular, our findings support the existence of sublexical orthographic representations within the left ventral cortex while arguing for the continuity of reading with other visual recognition skills.

Multivariate Cholesky models of human female fertility patterns in the NLSY.

PubMed

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Pattern formation and collective effects in populations of magnetic microswimmers

NASA Astrophysics Data System (ADS)

Vach, Peter J.; Walker, Debora; Fischer, Peer; Fratzl, Peter; Faivre, Damien

2017-03-01

Self-propelled particles are one prototype of synthetic active matter used to understand complex biological processes, such as the coordination of movement in bacterial colonies or schools of fishes. Collective patterns such as clusters were observed for such systems, reproducing features of biological organization. However, one limitation of this model is that the synthetic assemblies are made of identical individuals. Here we introduce an active system based on magnetic particles at colloidal scales. We use identical but also randomly-shaped magnetic micropropellers and show that they exhibit dynamic and reversible pattern formation.

Uniform modeling of bacterial colony patterns with varying nutrient and substrate

NASA Astrophysics Data System (ADS)

Schwarcz, Deborah; Levine, Herbert; Ben-Jacob, Eshel; Ariel, Gil

2016-04-01

Bacteria develop complex patterns depending on growth condition. For example, Bacillus subtilis exhibit five different patterns depending on substrate hardness and nutrient concentration. We present a unified integro-differential model that reproduces the entire experimentally observed morphology diagram at varying nutrient concentrations and substrate hardness. The model allows a comprehensive and quantitative comparison between experimental and numerical variables and parameters, such as colony growth rate, nutrient concentration and diffusion constants. As a result, the role of the different physical mechanisms underlying and regulating the growth of the colony can be evaluated.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Random sampling causes the low reproducibility of rare eukaryotic OTUs in Illumina COI metabarcoding.

PubMed

Leray, Matthieu; Knowlton, Nancy

2017-01-01

DNA metabarcoding, the PCR-based profiling of natural communities, is becoming the method of choice for biodiversity monitoring because it circumvents some of the limitations inherent to traditional ecological surveys. However, potential sources of bias that can affect the reproducibility of this method remain to be quantified. The interpretation of differences in patterns of sequence abundance and the ecological relevance of rare sequences remain particularly uncertain. Here we used one artificial mock community to explore the significance of abundance patterns and disentangle the effects of two potential biases on data reproducibility: indexed PCR primers and random sampling during Illumina MiSeq sequencing. We amplified a short fragment of the mitochondrial Cytochrome c Oxidase Subunit I (COI) for a single mock sample containing equimolar amounts of total genomic DNA from 34 marine invertebrates belonging to six phyla. We used seven indexed broad-range primers and sequenced the resulting library on two consecutive Illumina MiSeq runs. The total number of Operational Taxonomic Units (OTUs) was ∼4 times higher than expected based on the composition of the mock sample. Moreover, the total number of reads for the 34 components of the mock sample differed by up to three orders of magnitude. However, 79 out of 86 of the unexpected OTUs were represented by <10 sequences that did not appear consistently across replicates. Our data suggest that random sampling of rare OTUs (e.g., small associated fauna such as parasites) accounted for most of variation in OTU presence-absence, whereas biases associated with indexed PCRs accounted for a larger amount of variation in relative abundance patterns. These results suggest that random sampling during sequencing leads to the low reproducibility of rare OTUs. We suggest that the strategy for handling rare OTUs should depend on the objectives of the study. Systematic removal of rare OTUs may avoid inflating diversity based on common β descriptors but will exclude positive records of taxa that are functionally important. Our results further reinforce the need for technical replicates (parallel PCR and sequencing from the same sample) in metabarcoding experimental designs. Data reproducibility should be determined empirically as it will depend upon the sequencing depth, the type of sample, the sequence analysis pipeline, and the number of replicates. Moreover, estimating relative biomasses or abundances based on read counts remains elusive at the OTU level.

An investigation of the antidepressant action of xiaoyaosan in rats using ultra performance liquid chromatography-mass spectrometry combined with metabonomics.

PubMed

Gao, Xiao-Xia; Cui, Jie; Zheng, Xing-Yu; Li, Zhen-Yu; Choi, Young-Hae; Zhou, Yu-Zhi; Tian, Jun-Sheng; Xing, Jie; Tan, Xiao-Jie; Du, Guan-Hua; Qin, Xue-Mei

2013-07-01

A rapid, highly sensitive, and selective method was applied in a non-invasive way to investigate the antidepressant action of Xiaoyaosan (XYS) using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) and chemometrics. Many significantly altered metabolites were used to explain the mechanism. Venlafaxine HCl and fluoxetine HCl were used as chemical positive control drugs with a relatively clear mechanism of action to evaluate the efficiency and to predict the mechanism of action of XYS. Urine obtained from rats subjected to chronic unpredictable mild stress (CUMS) was analyzed by UPLC-MS. Distinct changes in the pattern of metabolites in the rat urine after CUMS production and drug intervention were observed using partial least squares-discriminant analysis. The results of behavioral tests and multivariate analysis showed that CUMS was successfully reproduced, and a moderate-dose XYS produced significant therapeutic effects in the rodent model, equivalent to those of the positive control drugs, venlafaxine HCl and fluoxetine HCl. Metabolites with significant changes induced by CUMS were identified, and 17 biomarker candidates for stress and drug intervention were identified. The therapeutic effect of XYS on depression may involve regulation of the dysfunctions of energy metabolism, amino acid metabolism, and gut microflora changes. Metabonomic methods are valuable tools for measuring efficacy and mechanisms of action in the study of traditional Chinese medicines. Copyright © 2012 John Wiley & Sons, Ltd.

Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hannah, Walter M.; Maloney, Eric D.; Pritchard, Michael S.

Hindcast simulations of MJO events during the dynamics of the MJO (DYNAMO) field campaign are conducted with two models, one with conventional parameterization (CAM5) and a comparable model that utilizes superparameterization (SP–CAM). SP–CAM is shown to produce a qualitatively better reproduction of the fluctuations of precipitation and low–level zonal wind associated with the first two DYNAMO MJO events compared to CAM5. Interestingly, skill metrics using the real–time multivariate MJO index (RMM) suggest the opposite conclusion that CAM5 has more skill than SP–CAM. This inconsistency can be explained by a systematic increase of RMM amplitude with lead time, which results frommore » a drift of the large–scale wind field in SP–CAM that projects strongly onto the RMM index. CAM5 hindcasts exhibit a contraction of the moisture distribution, in which extreme wet and dry conditions become less frequent with lead time. SP–CAM hindcasts better reproduce the observed moisture distribution, but also have stronger drift patterns of moisture budget terms, such as an increase in drying by meridional advection in SP–CAM. This advection tendency in SP–CAM appears to be associated with enhanced off–equatorial synoptic eddy activity with lead time. In conclusion, systematic drift moisture tendencies in SP–CAM are of similar magnitude to intraseasonal moisture tendencies, and therefore are important for understanding MJO prediction skill.« less

Finding Major Patterns of Aging Process by Data Synchronization

NASA Astrophysics Data System (ADS)

Miyano, Takaya; Tsutsui, Takako

We developed a method for extracting feature patterns from multivariate data using a network of coupled phase oscillators subject to an analogue of the Kuramoto model for collective synchronization. Our method may be called data synchronization. We applied data synchronization to the care-needs-certification data, provided by Otsu City as a historical old city near Kyoto City, in the Japanese public long-term care insurance program to find the trend of the major patterns of the aging process for elderly people needing nursing care.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

StimDuino: an Arduino-based electrophysiological stimulus isolator.

PubMed

Sheinin, Anton; Lavi, Ayal; Michaelevski, Izhak

2015-03-30

Electrical stimulus isolator is a widely used device in electrophysiology. The timing of the stimulus application is usually automated and controlled by the external device or acquisition software; however, the intensity of the stimulus is adjusted manually. Inaccuracy, lack of reproducibility and no automation of the experimental protocol are disadvantages of the manual adjustment. To overcome these shortcomings, we developed StimDuino, an inexpensive Arduino-controlled stimulus isolator allowing highly accurate, reproducible automated setting of the stimulation current. The intensity of the stimulation current delivered by StimDuino is controlled by Arduino, an open-source microcontroller development platform. The automatic stimulation patterns are software-controlled and the parameters are set from Matlab-coded simple, intuitive and user-friendly graphical user interface. The software also allows remote control of the device over the network. Electrical current measurements showed that StimDuino produces the requested current output with high accuracy. In both hippocampal slice and in vivo recordings, the fEPSP measurements obtained with StimDuino and the commercial stimulus isolators showed high correlation. Commercial stimulus isolators are manually managed, while StimDuino generates automatic stimulation patterns with increasing current intensity. The pattern is utilized for the input-output relationship analysis, necessary for assessment of excitability. In contrast to StimuDuino, not all commercial devices are capable for remote control of the parameters and stimulation process. StimDuino-generated automation of the input-output relationship assessment eliminates need for the current intensity manually adjusting, improves stimulation reproducibility, accuracy and allows on-site and remote control of the stimulation parameters. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Investigation of the utility of colorectal function tests and Rome II criteria in dyssynergic defecation (Anismus).

PubMed

Rao, S S C; Mudipalli, R S; Stessman, M; Zimmerman, B

2004-10-01

Although 30-50% of constipated patients exhibit dyssynergia, an optimal method of diagnosis is unclear. Recently, consensus criteria have been proposed but their utility is unknown. To examine the diagnostic yield of colorectal tests, reproducibility of manometry and utility of Rome II criteria. A total of 100 patients with difficult defecation were prospectively evaluated with anorectal manometry, balloon expulsion, colonic transit and defecography. Fifty-three patients had repeat manometry. During attempted defecation, 30 showed normal and 70 one of three abnormal manometric patterns. Forty-six patients fulfilled Rome criteria and showed paradoxical anal contraction (type I) or impaired anal relaxation (type III) with adequate propulsion. However, 24 (34%) showed impaired propulsion (type II). Forty-five (64%) had slow transit, 42 (60%) impaired balloon expulsion and 26 (37%) abnormal defecography. Defecography provided no additional discriminant utility. Evidence of dyssynergia was reproducible in 51 of 53 patients. Symptoms alone could not differentiate dyssynergic subtypes or patients. Dyssynergic patients exhibited three patterns that were reproducible: paradoxical contraction, impaired propulsion and impaired relaxation. Although useful, Rome II criteria may be insufficient to identify or subclassify dyssynergic defecation. Symptoms together with abnormal manometry, abnormal balloon expulsion or colonic marker retention are necessary to optimally identify patients with difficult defecation.

Model of rhythmic ball bouncing using a visually controlled neural oscillator.

PubMed

Avrin, Guillaume; Siegler, Isabelle A; Makarov, Maria; Rodriguez-Ayerbe, Pedro

2017-10-01

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture's ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment. NEW & NOTEWORTHY The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment. Copyright © 2017 the American Physiological Society.

A Method for Generating Natural and User-Defined Sniffing Patterns in Anesthetized or Reduced Preparations

PubMed Central

Carey, Ryan M.; Wachowiak, Matt

2009-01-01

Sniffing has long been thought to play a critical role in shaping neural responses to odorants at multiple levels of the nervous system. However, it has been difficult to systematically examine how particular parameters of sniffing behavior shape odorant-evoked activity, in large part because of the complexity of sniffing behavior and the difficulty in reproducing this behavior in an anesthetized or reduced preparation. Here we present a method for generating naturalistic sniffing patterns in such preparations. The method involves a nasal ventilator whose movement is controlled by an analog command voltage. The command signal may consist of intranasal pressure transients recorded from awake rats and mice or user-defined waveforms. This “sniff playback” device generates intranasal pressure and airflow transients in anesthetized animals that approximate those recorded from the awake animal and are reproducible across trials and across preparations. The device accurately reproduces command waveforms over an amplitude range of approximately 1 log unit and up to frequencies of approximately 12 Hz. Further, odorant-evoked neural activity imaged during sniff playback appears similar to that seen in awake animals. This method should prove useful in investigating how the parameters of odorant sampling shape neural responses in a variety of experimental settings. PMID:18791186

Simulations of temporal patterns of oral airflow in men and women using a two-mass model of the vocal folds under dynamic control

NASA Astrophysics Data System (ADS)

Lucero, Jorge C.; Koenig, Laura L.

2005-03-01

In this study we use a low-dimensional laryngeal model to reproduce temporal variations in oral airflow produced by speakers in the vicinity of an abduction gesture. It attempts to characterize these temporal patterns in terms of biomechanical parameters such as glottal area, vocal fold stiffness, subglottal pressure, and gender differences in laryngeal dimensions. A two-mass model of the vocal folds coupled to a two-tube approximation of the vocal tract is fitted to oral airflow records measured in men and women during the production of /aha/ utterances, using the subglottal pressure, glottal width, and Q factor as control parameters. The results show that the model is capable of reproducing the airflow records with good approximation. A nonlinear damping characteristics is needed, to reproduce the flow variation at glottal abduction. Devoicing is achieved by the combined action of vocal fold abduction, the decrease of subglottal pressure, and the increase of vocal fold tension. In general, the female larynx has a more restricted region of vocal fold oscillation than the male one. This would explain the more frequent devoicing in glottal abduction-adduction gestures for /h/ in running speech by women, compared to men. .

Patterns of linkage disequilibrium at PARK16 may explain variances in genetic association studies.

PubMed

Li, Huihua; Teo, Yik-Ying; Tan, Eng-King

2015-09-01

Reproducing genomewide association studies findings in different populations is challenging, because the reproducibility fundamentally relies on the similar patterns of linkage disequilibrium between the unknown causal variants and the genotyped single-nucleotide polymorphisms (SNPs). The PARK16 locus was reported to alter the risk of Parkinson's disease (PD) in genomewide association studies in Japanese and Caucasians. We evaluated the regional linkage disequilibrium pattern at PARK16 locus in Caucasians, Japanese, and Chinese from HapMap and Chinese, Malays, and Indians from the Singapore Genome Variation Project, using the traditional heatmaps and targeted analysis of PARK16 gene via Monte Carlo simulation through varLD scores of these ethnic groups. One hundred SNPs in Caucasians, 95 SNPs in Chinese, 78 SNPs in Japanese from HapMap, 86 SNPs in Chinese, 99 SNPs in Indians, and 97 SNPs in Malays from the Singapore Genome Variation Project were included. Our targeted analysis showed that the linkage disequilibrium pattern of SNPs close to rs947211 was similar in Caucasians and Asians, including Chinese, Japanese, and Malay (all P > 0.0001), whereas different linkage disequilibrium patterns around rs823128, rs823156, and rs708730 were found between Caucasians and these Asian groups (all P < 0.0001). Our study suggests a higher chance to detect the association between rs947211 and PD in Chinese, Malay, and other Caucasian groups because of the similar linkage disequilibrium pattern around rs947211. The associations between rs823128/rs823156/rs708730 and PD are more likely to be replicated in Chinese and Malay populations. © 2015 International Parkinson and Movement Disorder Society.

Dynamic response and transfer function of social systems: A neuro-inspired model of collective human activity patterns.

PubMed

Lymperopoulos, Ilias N

2017-10-01

The interaction of social networks with the external environment gives rise to non-stationary activity patterns reflecting the temporal structure and strength of exogenous influences that drive social dynamical processes far from an equilibrium state. Following a neuro-inspired approach, based on the dynamics of a passive neuronal membrane, and the firing rate dynamics of single neurons and neuronal populations, we build a state-of-the-art model of the collective social response to exogenous interventions. In this regard, we analyze online activity patterns with a view to determining the transfer function of social systems, that is, the dynamic relationship between external influences and the resulting activity. To this end, first we estimate the impulse response (Green's function) of collective activity, and then we show that the convolution of the impulse response with a time-varying external influence field accurately reproduces empirical activity patterns. To capture the dynamics of collective activity when the generating process is in a state of statistical equilibrium, we incorporate into the model a noisy input convolved with the impulse response function, thus precisely reproducing the fluctuations of stationary collective activity around a resting value. The outstanding goodness-of-fit of the model results to empirical observations, indicates that the model explains human activity patterns generated by time-dependent external influences in various socio-economic contexts. The proposed model can be used for inferring the temporal structure and strength of external influences, as well as the inertia of collective social activity. Furthermore, it can potentially predict social activity patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simulating the Pattern of Right-Hemisphere-Damaged Patients for the Processing of the Alternative Metaphorical Meanings of Words: Evidence in Favor of a Cognitive Resources Hypothesis

ERIC Educational Resources Information Center

Monetta, Laura; Ouellet-Plamondon, Clairelaine; Joanette, Yves

2006-01-01

Lately, many studies have suggested that communication impairments in brain-damaged individuals might be explained--at least in part--in terms of cognitive resource allocation. Reproducing a clinical pattern in normal subjects by using a dual-task treatment might be a way of evaluating the role of cognitive resources in the right hemisphere's…

Men and women show similar survival outcome in stage IV breast cancer.

PubMed

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

An optical authentication system based on imaging of excitation-selected lanthanide luminescence.

PubMed

Carro-Temboury, Miguel R; Arppe, Riikka; Vosch, Tom; Sørensen, Thomas Just

2018-01-01

Secure data encryption relies heavily on one-way functions, and copy protection relies on features that are difficult to reproduce. We present an optical authentication system based on lanthanide luminescence from physical one-way functions or physical unclonable functions (PUFs). They cannot be reproduced and thus enable unbreakable encryption. Further, PUFs will prevent counterfeiting if tags with unique PUFs are grafted onto products. We have developed an authentication system that comprises a hardware reader, image analysis, and authentication software and physical keys that we demonstrate as an anticounterfeiting system. The physical keys are PUFs made from random patterns of taggants in polymer films on glass that can be imaged following selected excitation of particular lanthanide(III) ions doped into the individual taggants. This form of excitation-selected imaging ensures that by using at least two lanthanide(III) ion dopants, the random patterns cannot be copied, because the excitation selection will fail when using any other emitter. With the developed reader and software, the random patterns are read and digitized, which allows a digital pattern to be stored. This digital pattern or digital key can be used to authenticate the physical key in anticounterfeiting or to encrypt any message. The PUF key was produced with a staggering nominal encoding capacity of 7 3600 . Although the encoding capacity of the realized authentication system reduces to 6 × 10 104 , it is more than sufficient to completely preclude counterfeiting of products.

A Simulation Study of Mutations in the Genetic Regulatory Hierarchy for Butterfly Eyespot Focus Determination

PubMed Central

Marcus, Jeffrey M.; Evans, Travis M.

2008-01-01

The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. A recent computational model tested genetic regulatory hierarchies hypothesized to underlie the formation of butterfly eyespot foci (Evans and Marcus, 2006). The computational model demonstrated that one proposed hierarchy was incapable of reproducing the known patterns of gene expression associated with eyespot focus determination in wild-type butterflies, but that two slightly modified alternative hierarchies were capable of reproducing all of the known gene expressions patterns. Here we extend the computational models previously implemented in Delphi 2.0 to two mutants derived from the squinting bush brown butterfly (Bicyclus anynana). These two mutants, comet and Cyclops, have aberrantly shaped eyespot foci that are produced by different mechanisms. The comet mutation appears to produce a modified interaction between the wing margin and the eyespot focus that results in a series of comet-shaped eyespot foci. The Cyclops mutation causes the failure of wing vein formation between two adjacent wing-cells and the fusion of two adjacent eyespot foci to form a single large elongated focus in their place. The computational approach to modeling pattern formation in these mutants allows us to make predictions about patterns of gene expression, which are largely unstudied in butterfly mutants. It also suggests a critical experiment that will allow us to distinguish between two hypothesized genetic regulatory hierarchies that may underlie all butterfly eyespot foci. PMID:18586070

Geometric Analyses of Rotational Faults.

ERIC Educational Resources Information Center

Schwert, Donald Peters; Peck, Wesley David

1986-01-01

Describes the use of analysis of rotational faults in undergraduate structural geology laboratories to provide students with applications of both orthographic and stereographic techniques. A demonstration problem is described, and an orthographic/stereographic solution and a reproducible black model demonstration pattern are provided. (TW)

Linked Sex Differences in Cognition and Functional Connectivity in Youth.

PubMed

Satterthwaite, Theodore D; Wolf, Daniel H; Roalf, David R; Ruparel, Kosha; Erus, Guray; Vandekar, Simon; Gennatas, Efstathios D; Elliott, Mark A; Smith, Alex; Hakonarson, Hakon; Verma, Ragini; Davatzikos, Christos; Gur, Raquel E; Gur, Ruben C

2015-09-01

Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

PubMed

Mendlik, Thomas; Gobiet, Andreas

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics. The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.

Easy Fabrication of Thin Membranes with Through Holes. Application to Protein Patterning

PubMed Central

Arasi, Bakya; Gauthier, Nils; Viasnoff, Virgile

2012-01-01

Since protein patterning on 2D surfaces has emerged as an important tool in cell biology, the development of easy patterning methods has gained importance in biology labs. In this paper we present a simple, rapid and reliable technique to fabricate thin layers of UV curable polymer with through holes. These membranes are as easy to fabricate as microcontact printing stamps and can be readily used for stencil patterning. We show how this microfabrication scheme allows highly reproducible and highly homogeneous protein patterning with micron sized resolution on surfaces as large as 10 cm2. Using these stencils, fragile proteins were patterned without loss of function in a fully hydrated state. We further demonstrate how intricate patterns of multiple proteins can be achieved by stacking the stencil membranes. We termed this approach microserigraphy. PMID:22952944

Spiral and never-settling patterns in active systems

NASA Astrophysics Data System (ADS)

Yang, X.; Marenduzzo, D.; Marchetti, M. C.

2014-01-01

We present a combined numerical and analytical study of pattern formation in an active system where particles align, possess a density-dependent motility, and are subject to a logistic reaction. The model can describe suspensions of reproducing bacteria, as well as polymerizing actomyosin gels in vitro or in vivo. In the disordered phase, we find that motility suppression and growth compete to yield stable or blinking patterns, which, when dense enough, acquire internal orientational ordering to give asters or spirals. We predict these may be observed within chemotactic aggregates in bacterial fluids. In the ordered phase, the reaction term leads to previously unobserved never-settling patterns which can provide a simple framework to understand the formation of motile and spiral patterns in intracellular actin systems.

Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations

PubMed Central

Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.

2015-01-01

Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy.

PubMed

Gu, Yue; Miao, Shuo; Han, Junxia; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

2018-06-01

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Coupling human mobility and social ties.

PubMed

Toole, Jameson L; Herrera-Yaqüe, Carlos; Schneider, Christian M; González, Marta C

2015-04-06

Studies using massive, passively collected data from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion and organizational dynamics. More recently, these data have come tagged with geographical information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns among social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behaviour. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare its ability to reproduce empirical measurements with two additional models of mobility. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Novel Wounding Device Suitable for Quantitative Biochemical Analysis of Wound Healing and Regeneration of Cultured Epithelium

PubMed Central

Lan, Rongpei; Geng, Hui; Hwang, Yoon; Mishra, Pramod; Skloss, Wayne L.; Sprague, Eugene A.; Saikumar, Pothana; Venkatachalam, Manjeri

2010-01-01

We describe the fabrication and use of an in vitro wounding device that denudes cultured epithelium in patterns designed to leave behind strips or islands of cells sufficiently narrow or small to ensure that all remaining cells become rapidly activated and then migrate, dedifferentiate and proliferate in near synchrony. The design ensures that signals specific to regenerating cells do not become diluted by quiescent differentiated cells that are not affected by wound induced activation. The device consists of a flat circular disk of rubber engraved to produce alternating ridges and grooves in patterns of concentric circles or parallel lines. The disk is mounted at the end of a pneumatically controlled piston assembly. Application of controlled pressure and circular or linear movement of the disk on cultures produced highly reproducible wounding patterns. The near synchronous regenerative activity of cell bands or islands permitted the collection of samples large enough for biochemical studies to sensitively detect alterations involving mRNA for several early response genes and protein phosphorylation in major signaling pathways. The method is versatile, easy to use and reproducible, and should facilitate biochemical, proteomic and genomic studies of wound induced regeneration of cultured epithelium. PMID:20230600

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

Phylogenetic patterns of climatic, habitat and trophic niches in a European avian assemblage

PubMed Central

Pearman, Peter B; Lavergne, Sébastien; Roquet, Cristina; Wüest, Rafael; Zimmermann, Niklaus E; Thuiller, Wilfried

2014-01-01

Aim The origins of ecological diversity in continental species assemblages have long intrigued biogeographers. We apply phylogenetic comparative analyses to disentangle the evolutionary patterns of ecological niches in an assemblage of European birds. We compare phylogenetic patterns in trophic, habitat and climatic niche components. Location Europe. Methods From polygon range maps and handbook data we inferred the realized climatic, habitat and trophic niches of 405 species of breeding birds in Europe. We fitted Pagel's lambda and kappa statistics, and conducted analyses of disparity through time to compare temporal patterns of ecological diversification on all niche axes together. All observed patterns were compared with expectations based on neutral (Brownian) models of niche divergence. Results In this assemblage, patterns of phylogenetic signal (lambda) suggest that related species resemble each other less in regard to their climatic and habitat niches than they do in their trophic niche. Kappa estimates show that ecological divergence does not gradually increase with divergence time, and that this punctualism is stronger in climatic niches than in habitat and trophic niches. Observed niche disparity markedly exceeds levels expected from a Brownian model of ecological diversification, thus providing no evidence for past phylogenetic niche conservatism in these multivariate niches. Levels of multivariate disparity are greatest for the climatic niche, followed by disparity of the habitat and the trophic niches. Main conclusions Phylogenetic patterns in the three niche components differ within this avian assemblage. Variation in evolutionary rates (degree of gradualism, constancy through the tree) and/or non-random macroecological sampling probably lead here to differences in the phylogenetic structure of niche components. Testing hypotheses on the origin of these patterns requires more complete phylogenetic trees of the birds, and extended ecological data on different niche components for all bird species. PMID:24790525

Eating patterns and energy and nutrient intakes of US women.

PubMed

Haines, P S; Hungerford, D W; Popkin, B M; Guilkey, D K

1992-06-01

A longitudinal multivariate analysis was used to determine whether differences in energy and nutrient intakes were present for women classified into different eating patterns. Ten multidimensional eating patterns were created based on the proportion of energy consumed at home and at seven away-from-home locations. Data were from 1,120 women aged 19 through 50 years who were surveyed up to six times over a 1-year period as part of the 1985 Continuing Survey of Food Intake by Individuals, US Department of Agriculture. Data from 5,993 days were analyzed. To examine differences in energy and nutrient intakes, longitudinal multivariate analyses were used to control for eating pattern and factors such as demographics, season, and day of week. Younger women in the Fast Food eating pattern consumed the greatest intakes of energy, total fat, saturated fat, cholesterol, and sodium. Well-educated, higher-income women in the Restaurant pattern consumed diets with the highest overall fat density. Nutrient densities for dietary fiber, calcium, vitamin C, and folacin were particularly low in away-from-home eating patterns. In contrast, moderately educated, middle-aged and middle-income women in the Home Mixed eating pattern (70% at home, 30% away from home) consumed the most healthful diets. We conclude that knowledge of demographics such as income and education is not enough to target dietary interventions. Rather, educational efforts must consider both demographics and the location of away-from-home eating. This will allow development of behavioral change strategies that consider food choices dictated by the eating environment as well as personal knowledge and attitude factors related to adoption of healthful food choices.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Test-retest stability of the Task and Ego Orientation Questionnaire.

PubMed

Lane, Andrew M; Nevill, Alan M; Bowes, Neal; Fox, Kenneth R

2005-09-01

Establishing stability, defined as observing minimal measurement error in a test-retest assessment, is vital to validating psychometric tools. Correlational methods, such as Pearson product-moment, intraclass, and kappa are tests of association or consistency, whereas stability or reproducibility (regarded here as synonymous) assesses the agreement between test-retest scores. Indexes of reproducibility using the Task and Ego Orientation in Sport Questionnaire (TEOSQ; Duda & Nicholls, 1992) were investigated using correlational (Pearson product-moment, intraclass, and kappa) methods, repeated measures multivariate analysis of variance, and calculating the proportion of agreement within a referent value of +/-1 as suggested by Nevill, Lane, Kilgour, Bowes, and Whyte (2001). Two hundred thirteen soccer players completed the TEOSQ on two occasions, 1 week apart. Correlation analyses indicated a stronger test-retest correlation for the Ego subscale than the Task subscale. Multivariate analysis of variance indicated stability for ego items but with significant increases in four task items. The proportion of test-retest agreement scores indicated that all ego items reported relatively poor stability statistics with test-retest scores within a range of +/-1, ranging from 82.7-86.9%. By contrast, all task items showed test-retest difference scores ranging from 92.5-99%, although further analysis indicated that four task subscale items increased significantly. Findings illustrated that correlational methods (Pearson product-moment, intraclass, and kappa) are influenced by the range in scores, and calculating the proportion of agreement of test-retest differences with a referent value of +/-1 could provide additional insight into the stability of the questionnaire. It is suggested that the item-by-item proportion of agreement method proposed by Nevill et al. (2001) should be used to supplement existing methods and could be especially helpful in identifying rogue items in the initial stages of psychometric questionnaire validation.

A mathematical basis for plant patterning derived from physico-chemical phenomena.

PubMed

Beleyur, Thejasvi; Abdul Kareem, Valiya Kadavu; Shaji, Anil; Prasad, Kalika

2013-04-01

The position of leaves and flowers along the stem axis generates a specific pattern, known as phyllotaxis. A growing body of evidence emerging from recent computational modeling and experimental studies suggests that regulators controlling phyllotaxis are chemical, e.g. the plant growth hormone auxin and its dynamic accumulation pattern by polar auxin transport, and physical, e.g. mechanical properties of the cell. Here we present comprehensive views on how chemical and physical properties of cells regulate the pattern of leaf initiation. We further compare different computational modeling studies to understand their scope in reproducing the observed patterns. Despite a plethora of experimental studies on phyllotaxis, understanding of molecular mechanisms of pattern initiation in plants remains fragmentary. Live imaging of growth dynamics and physicochemical properties at the shoot apex of mutants displaying stable changes from one pattern to another should provide mechanistic insights into organ initiation patterns. Copyright © 2013 WILEY Periodicals, Inc.

Revealing representational content with pattern-information fMRI--an introductory guide.

PubMed

Mur, Marieke; Bandettini, Peter A; Kriegeskorte, Nikolaus

2009-03-01

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

NASA Astrophysics Data System (ADS)

Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

2013-12-01

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.

CHARACTERIZATION OF CRYPTOSPORIDIUM PARVUM BY MATRIX-ASSISTED LASER DESORPTION -- IONIZATION TIME OF FLIGHT MASS SPECTROMETRY

EPA Science Inventory

Matrix assisted laser desorption/ionization (MALDI) mass spectrometry was used to investigate whole and freeze thawed Cryptosporidium parvum oocysts. Whole oocysts revealed some mass spectral features. Reproducible patterns of spectral markers and increased sensitivity were obtai...

SSMILes: Investigating Various Volcanic Eruptions and Volcano Heights.

ERIC Educational Resources Information Center

Wagner-Pine, Linda; Keith, Donna Graham

1994-01-01

Presents an integrated math/science activity that shows students the differences among the three types of volcanoes using observation, classification, graphing, sorting, problem solving, measurement, averages, pattern relationships, calculators, computers, and research skills. Includes reproducible student worksheet. Lists 13 teacher resources.…

Evolution of Patterns in Rotating Bénard Convection

NASA Astrophysics Data System (ADS)

Fantz, M.; Friedrich, R.; Bestehorn, M.; Haken, H.

We present an extension of the Swift-Hohenberg equation to the case of a high Prandtl number Bénard experiment in rotating fluid containers. For the case of circular containers we find complex spatio-temporal behaviour at Taylor numbers smaller than the critical one for the onset of the Küppers-Lortz instability. Furthermore, above the critical Taylor number the experimentally well-known time dependent and spatially disordered patterns in form of local patches of rolls are reproduced.

Use of Amplified Fragment Length Polymorphisms for Typing Corynebacterium diphtheriae

PubMed Central

De Zoysa, Aruni; Efstratiou, Androulla

2000-01-01

Amplified fragment length polymorphism (AFLP) was investigated for the differentiation of Corynebacterium diphtheriae isolates. Analysis using Taxotron revealed 10 distinct AFLP profiles among 57 isolates. Strains with ribotype patterns D1, D4, and D12 could not be distinguished; however, the technique discriminated isolates of ribotype patterns D3, D6, and D7 further. AFLP was rapid, fairly inexpensive, and reproducible and could be used as an alternative to ribotyping. PMID:11015416

Selective PEGylation of Parylene-C/SiO2 Substrates for Improved Astrocyte Cell Patterning.

PubMed

Raos, B J; Doyle, C S; Simpson, M C; Graham, E S; Unsworth, C P

2018-02-09

Controlling the spatial distribution of glia and neurons in in vitro culture offers the opportunity to study how cellular interactions contribute to large scale network behaviour. A recently developed approach to cell-patterning uses differential adsorption of animal-serum protein on parylene-C and SiO 2 surfaces to enable patterning of neurons and glia. Serum, however, is typically poorly defined and generates reproducibility challenges. Alternative activation methods are highly desirable to enable patterning without relying on animal serum. We take advantage of the innate contrasting surface chemistries of parylene-C and SiO 2 to enable selective bonding of polyethylene glycol SiO 2 surfaces, i.e. PEGylation, rendering them almost completely repulsive to cell adhesion. As the reagents used in the PEGylation protocol are chemically defined, the reproducibility and batch-to-batch variability complications associated with the used of animal serum are avoided. We report that PEGylated parylene-C/SiO 2 substrates achieve a contrast in astrocyte density of 65:1 whereas the standard serum-immersion protocol results in a contrast of 5.6:1. Furthermore, single-cell isolation was significantly improved on PEGylated substrates when astrocytes were grown on close-proximity parylene-C nodes, whereas isolation was limited on serum-activated substrates due tolerance for cell adhesion on serum-adsorbed SiO 2 surfaces.

Patient training in respiratory-gated radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kini, Vijay R.; Vedam, Subrahmanya S.; Keall, Paul J.

2003-03-31

Respiratory gating is used to counter the effects of organ motion during radiotherapy for chest tumors. The effects of variations in patient breathing patterns during a single treatment and from day to day are unknown. We evaluated the feasibility of using patient training tools and their effect on the breathing cycle regularity and reproducibility during respiratory-gated radiotherapy. To monitor respiratory patterns, we used a component of a commercially available respiratory-gated radiotherapy system (Real Time Position Management (RPM) System, Varian Oncology Systems, Palo Alto, CA 94304). This passive marker video tracking system consists of reflective markers placed on the patient's chestmore » or abdomen, which are detected by a wall-mounted video camera. Software installed on a PC interfaced to this camera detects the marker motion digitally and records it. The marker position as a function of time serves as the motion signal that may be used to trigger imaging or treatment. The training tools used were audio prompting and visual feedback, with free breathing as a control. The audio prompting method used instructions to 'breathe in' or 'breathe out' at periodic intervals deduced from patients' own breathing patterns. In the visual feedback method, patients were shown a real-time trace of their abdominal wall motion due to breathing. Using this, they were asked to maintain a constant amplitude of motion. Motion traces of the abdominal wall were recorded for each patient for various maneuvers. Free breathing showed a variable amplitude and frequency. Audio prompting resulted in a reproducible frequency; however, the variability and the magnitude of amplitude increased. Visual feedback gave a better control over the amplitude but showed minor variations in frequency. We concluded that training improves the reproducibility of amplitude and frequency of patient breathing cycles. This may increase the accuracy of respiratory-gated radiation therapy.« less

Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

PubMed

Anderson, John R

2012-03-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. Copyright © 2011 Elsevier Ltd. All rights reserved.

From sensation to perception: Using multivariate classification of visual illusions to identify neural correlates of conscious awareness in space and time.

PubMed

Hogendoorn, Hinze

2015-01-01

An important goal of cognitive neuroscience is understanding the neural underpinnings of conscious awareness. Although the low-level processing of sensory input is well understood in most modalities, it remains a challenge to understand how the brain translates such input into conscious awareness. Here, I argue that the application of multivariate pattern classification techniques to neuroimaging data acquired while observers experience perceptual illusions provides a unique way to dissociate sensory mechanisms from mechanisms underlying conscious awareness. Using this approach, it is possible to directly compare patterns of neural activity that correspond to the contents of awareness, independent from changes in sensory input, and to track these neural representations over time at high temporal resolution. I highlight five recent studies using this approach, and provide practical considerations and limitations for future implementations.

Narcissistic Personality Disorder and the Structure of Common Mental Disorders.

PubMed

Eaton, Nicholas R; Rodriguez-Seijas, Craig; Krueger, Robert F; Campbell, W Keith; Grant, Bridget F; Hasin, Deborah S

2017-08-01

Narcissistic personality disorder (NPD) shows high rates of comorbidity with mood, anxiety, substance use, and other personality disorders. Previous bivariate comorbidity investigations have left NPD multivariate comorbidity patterns poorly understood. Structural psychopathology research suggests that two transdiagnostic factors, internalizing (with distress and fear subfactors) and externalizing, account for comorbidity among common mental disorders. NPD has rarely been evaluated within this framework, with studies producing equivocal results. We investigated how NPD related to other mental disorders in the internalizing-externalizing model using diagnoses from a nationally representative sample (N = 34,653). NPD was best conceptualized as a distress disorder. NPD variance accounted for by transdiagnostic factors was modest, suggesting its variance is largely unique in the context of other common mental disorders. Results clarify NPD multivariate comorbidity, suggest avenues for classification and clinical endeavors, and highlight the need to understand vulnerable and grandiose narcissism subtypes' comorbidity patterns and structural relations.

The basis of orientation decoding in human primary visual cortex: fine- or coarse-scale biases?

PubMed

Maloney, Ryan T

2015-01-01

Orientation signals in human primary visual cortex (V1) can be reliably decoded from the multivariate pattern of activity as measured with functional magnetic resonance imaging (fMRI). The precise underlying source of these decoded signals (whether by orientation biases at a fine or coarse scale in cortex) remains a matter of some controversy, however. Freeman and colleagues (J Neurosci 33: 19695-19703, 2013) recently showed that the accuracy of decoding of spiral patterns in V1 can be predicted by a voxel's preferred spatial position (the population receptive field) and its coarse orientation preference, suggesting that coarse-scale biases are sufficient for orientation decoding. Whether they are also necessary for decoding remains an open question, and one with implications for the broader interpretation of multivariate decoding results in fMRI studies. Copyright © 2015 the American Physiological Society.

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

NASA Technical Reports Server (NTRS)

Magri, Christopher

1994-01-01

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Quantitative architectural analysis: a new approach to cortical mapping.

PubMed

Schleicher, A; Palomero-Gallagher, N; Morosan, P; Eickhoff, S B; Kowalski, T; de Vos, K; Amunts, K; Zilles, K

2005-12-01

Recent progress in anatomical and functional MRI has revived the demand for a reliable, topographic map of the human cerebral cortex. Till date, interpretations of specific activations found in functional imaging studies and their topographical analysis in a spatial reference system are, often, still based on classical architectonic maps. The most commonly used reference atlas is that of Brodmann and his successors, despite its severe inherent drawbacks. One obvious weakness in traditional, architectural mapping is the subjective nature of localising borders between cortical areas, by means of a purely visual, microscopical examination of histological specimens. To overcome this limitation, more objective, quantitative mapping procedures have been established in the past years. The quantification of the neocortical, laminar pattern by defining intensity line profiles across the cortical layers, has a long tradition. During the last years, this method has been extended to enable a reliable, reproducible mapping of the cortex based on image analysis and multivariate statistics. Methodological approaches to such algorithm-based, cortical mapping were published for various architectural modalities. In our contribution, principles of algorithm-based mapping are described for cyto- and receptorarchitecture. In a cytoarchitectural parcellation of the human auditory cortex, using a sliding window procedure, the classical areal pattern of the human superior temporal gyrus was modified by a replacing of Brodmann's areas 41, 42, 22 and parts of area 21, with a novel, more detailed map. An extension and optimisation of the sliding window procedure to the specific requirements of receptorarchitectonic mapping, is also described using the macaque central sulcus and adjacent superior parietal lobule as a second, biologically independent example. Algorithm-based mapping procedures, however, are not limited to these two architectural modalities, but can be applied to all images in which a laminar cortical pattern can be detected and quantified, e.g. myeloarchitectonic and in vivo high resolution MR imaging. Defining cortical borders, based on changes in cortical lamination in high resolution, in vivo structural MR images will result in a rapid increase of our knowledge on the structural parcellation of the human cerebral cortex.

Neural Network Computing and Natural Language Processing.

ERIC Educational Resources Information Center

Borchardt, Frank

1988-01-01

Considers the application of neural network concepts to traditional natural language processing and demonstrates that neural network computing architecture can: (1) learn from actual spoken language; (2) observe rules of pronunciation; and (3) reproduce sounds from the patterns derived by its own processes. (Author/CB)

A scoring metric for multivariate data for reproducibility analysis using chemometric methods

PubMed Central

Sheen, David A.; de Carvalho Rocha, Werickson Fortunato; Lippa, Katrice A.; Bearden, Daniel W.

2017-01-01

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory’s individual performance. PMID:28694553

Metabolic profiling of body fluids and multivariate data analysis.

PubMed

Trezzi, Jean-Pierre; Jäger, Christian; Galozzi, Sara; Barkovits, Katalin; Marcus, Katrin; Mollenhauer, Brit; Hiller, Karsten

2017-01-01

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC-MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC-MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: •Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process•Small sample volume required•Rapid and cost-effective processing of biological samples•Logistic regression based determination of biomarker signatures for in-depth data analysis.

Matching consumer feeding behaviours and resource traits: a fourth-corner problem in food-web theory.

PubMed

Monteiro, Angelo Barbosa; Faria, Lucas Del Bianco

2018-06-06

For decades, food web theory has proposed phenomenological models for the underlying structure of ecological networks. Generally, these models rely on latent niche variables that match the feeding behaviour of consumers with their resource traits. In this paper, we used a comprehensive database to evaluate different hypotheses on the best dependency structure of trait-matching patterns between consumers and resource traits. We found that consumer feeding behaviours had complex interactions with resource traits; however, few dimensions (i.e. latent variables) could reproduce the trait-matching patterns. We discuss our findings in the light of three food web models designed to reproduce the multidimensionality of food web data; additionally, we discuss how using species traits clarify food webs beyond species pairwise interactions and enable studies to infer ecological generality at larger scales, despite potential taxonomic differences, variations in ecological conditions and differences in species abundance between communities. © 2018 John Wiley & Sons Ltd/CNRS.

Canonical Genetic Signatures of the Adult Human Brain

PubMed Central

Hawrylycz, Michael; Miller, Jeremy A.; Menon, Vilas; Feng, David; Dolbeare, Tim; Guillozet-Bongaarts, Angela L.; Jegga, Anil G.; Aronow, Bruce J.; Lee, Chang-Kyu; Bernard, Amy; Glasser, Matthew F.; Dierker, Donna L.; Menche, Jörge; Szafer, Aaron; Collman, Forrest; Grange, Pascal; Berman, Kenneth A.; Mihalas, Stefan; Yao, Zizhen; Stewart, Lance; Barabási, Albert-László; Schulkin, Jay; Phillips, John; Ng, Lydia; Dang, Chinh; Haynor, David R.; Jones, Allan; Van Essen, David C.; Koch, Christof; Lein, Ed

2015-01-01

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure, and function. We applied a correlation-based metric of “differential stability” (DS) to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing meso-scale genetic organization. The highest DS genes are highly biologically relevant, with enrichment for brain-related biological annotations, disease associations, drug targets, and literature citations. Using high DS genes we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components, and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely-patterned genes displayed dramatic shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry. PMID:26571460

Analysis of dynamically stable patterns in a maze-like corridor using the Wasserstein metric.

PubMed

Ishiwata, Ryosuke; Kinukawa, Ryota; Sugiyama, Yuki

2018-04-23

The two-dimensional optimal velocity (2d-OV) model represents a dissipative system with asymmetric interactions, thus being suitable to reproduce behaviours such as pedestrian dynamics and the collective motion of living organisms. In this study, we found that particles in the 2d-OV model form optimal patterns in a maze-like corridor. Then, we estimated the stability of such patterns using the Wasserstein metric. Furthermore, we mapped these patterns into the Wasserstein metric space and represented them as points in a plane. As a result, we discovered that the stability of the dynamical patterns is strongly affected by the model sensitivity, which controls the motion of each particle. In addition, we verified the existence of two stable macroscopic patterns which were cohesive, stable, and appeared regularly over the time evolution of the model.

Dietary patterns and risk of death and progression to ESRD in individuals with CKD: a cohort study.

PubMed

Gutiérrez, Orlando M; Muntner, Paul; Rizk, Dana V; McClellan, William M; Warnock, David G; Newby, P K; Judd, Suzanne E

2014-08-01

Nutrition is linked strongly with health outcomes in chronic kidney disease (CKD). However, few studies have examined relationships between dietary patterns and health outcomes in persons with CKD. Observational cohort study. 3,972 participants with CKD (defined as estimated glomerular filtration rate < 60 mL/min/1.73 m2 or albumin-creatinine ratio ≥ 30 mg/g at baseline) from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study, a prospective cohort study of 30,239 black and white adults at least 45 years of age. 5 empirically derived dietary patterns identified by factor analysis: "convenience" (Chinese and Mexican foods, pizza, and other mixed dishes), "plant-based" (fruits and vegetables), "sweets/fats" (sugary foods), "Southern" (fried foods, organ meats, and sweetened beverages), and "alcohol/salads" (alcohol, green-leafy vegetables, and salad dressing). All-cause mortality and end-stage renal disease (ESRD). 816 deaths and 141 ESRD events were observed over approximately 6 years of follow-up. There were no statistically significant associations of convenience, sweets/fats, or alcohol/salads pattern scores with all-cause mortality after multivariable adjustment. In Cox regression models adjusted for sociodemographic factors, energy intake, comorbid conditions, and baseline kidney function, higher plant-based pattern scores (indicating greater consistency with the pattern) were associated with lower risk of mortality (HR comparing fourth to first quartile, 0.77; 95% CI, 0.61-0.97), whereas higher Southern pattern scores were associated with greater risk of mortality (HR comparing fourth to first quartile, 1.51; 95% CI, 1.19-1.92). There were no associations of dietary patterns with incident ESRD in multivariable-adjusted models. Missing dietary pattern data, potential residual confounding from lifestyle factors. A Southern dietary pattern rich in processed and fried foods was associated independently with mortality in persons with CKD. In contrast, a diet rich in fruits and vegetables appeared to be protective. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Enhanced reproducibility of SADI web service workflows with Galaxy and Docker.

PubMed

Aranguren, Mikel Egaña; Wilkinson, Mark D

2015-01-01

Semantic Web technologies have been widely applied in the life sciences, for example by data providers such as OpenLifeData and through web services frameworks such as SADI. The recently reported OpenLifeData2SADI project offers access to the vast OpenLifeData data store through SADI services. This article describes how to merge data retrieved from OpenLifeData2SADI with other SADI services using the Galaxy bioinformatics analysis platform, thus making this semantic data more amenable to complex analyses. This is demonstrated using a working example, which is made distributable and reproducible through a Docker image that includes SADI tools, along with the data and workflows that constitute the demonstration. The combination of Galaxy and Docker offers a solution for faithfully reproducing and sharing complex data retrieval and analysis workflows based on the SADI Semantic web service design patterns.

The Signature of Southern Hemisphere Atmospheric Circulation Patterns in Antarctic Precipitation

PubMed Central

Thompson, David W. J.; van den Broeke, Michiel R.

2017-01-01

Abstract We provide the first comprehensive analysis of the relationships between large‐scale patterns of Southern Hemisphere climate variability and the detailed structure of Antarctic precipitation. We examine linkages between the high spatial resolution precipitation from a regional atmospheric model and four patterns of large‐scale Southern Hemisphere climate variability: the southern baroclinic annular mode, the southern annular mode, and the two Pacific‐South American teleconnection patterns. Variations in all four patterns influence the spatial configuration of precipitation over Antarctica, consistent with their signatures in high‐latitude meridional moisture fluxes. They impact not only the mean but also the incidence of extreme precipitation events. Current coupled‐climate models are able to reproduce all four patterns of atmospheric variability but struggle to correctly replicate their regional impacts on Antarctic climate. Thus, linking these patterns directly to Antarctic precipitation variability may allow a better estimate of future changes in precipitation than using model output alone. PMID:29398735

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Deriving the pattern speed using dynamical modelling of gas flows in barred galaxies .

NASA Astrophysics Data System (ADS)

Pérez, I.; Freeman, K. C.; Fux, R.; Zurita, A.

In this paper we analyse the methodology to derive the bar pattern speed from dynamical simulations. The results are robust to the changes in the vertical-scale height and in the mass-to-light (M/L) ratios. There is a small range of parameters for which the kinematics can be fitted. We have also taken into account the use of different type of dynamical modelling and the effect of using 2-D vs 1-D models in deriving the pattern speeds. We conclude that the derivation of the bar streaming motions and strength and position of shocks is not greatly affected by the fluid dynamical model used. We show new results on the derivation of the pattern speed for NGC 1530. The best fit pattern speed is around 10 km s-1 kpc-1 , which corresponds to a R_cor/R_bar = 1.4, implying a slower bar than previously derived from more indirect assumptions. With this pattern speed, the global and most local kinematic features are beautifully reproduced. However, the simulations fail to reproduce the velocity gradients close to some bright HII regions in the bar. We have shown from the study of the H{alpha } equivalent widths that the HII regions that are located further away from the bar dust-lane in its leading side, downstream from the main bar dust-lane, are older than the rest by 1.5-2.5 Myr. In addition, a clear spatial correlation was found between the location of HII regions, dust spurs on the trailing side of the bar dust-lane, and the loci of maximum velocity gradients parallel to the bar major axis.

A discrete model of Drosophila eggshell patterning reveals cell-autonomous and juxtacrine effects.

PubMed

Fauré, Adrien; Vreede, Barbara M I; Sucena, Elio; Chaouiya, Claudine

2014-03-01

The Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.

Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Sahimi, Muhammad

2016-03-01

In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.

The VCD pattern of the ν(C=O) bands in isoindolinones.

PubMed

Rode, Joanna Ewa; Lyczko, Krzysztof; Jawiczuk, Magdalena; Kawęcki, Robert; Stańczyk, Wojciech; Jaglińska, Agnieszka; Dobrowolski, Jan Cz

2018-05-18

The IR and VCD spectra of both enantiomers of Me, iPr, nBu, Ph and CH2Ph substituted isoindolinones in solution and KBr pellets were measured and interpreted with DFT calculations. The spectra in solution revealed no important differences in the C=O stretching vibration region while the interpretation of very distinct spectra taken in pellets required determining the crystal structures. The studied compounds crystallized in the P212121 (Me, iPr, CH2Ph), P31 (nBu), and P21 (Ph) space groups. We found that the quality of simulated spectra strongly depends on the substituent, the structure of the molecular cluster assumed, basis set, and use of the dispersion correction. The IR spectra can be reproduced well based on the simplest linear arrangement of hydrogen-bonded chains mimicking the molecular arrangement in the crystals. We found no common approach to reproduce all the registered VCD spectra in the crystal phase. For the Me and nBu isoindolinones, the VCD pattern was the best reproduced by full optimization of the selected large molecular clusters. For iPr, Ph and CH2Ph derivatives optimizing only the position of H-atoms in a fragment frozen as in the crystal provides the best results. Such an approach can reduce the computation time from months to one week. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biofilm development of an opportunistic model bacterium analysed at high spatiotemporal resolution in the framework of a precise flow cell

PubMed Central

Lim, Chun Ping; Mai, Phuong Nguyen Quoc; Roizman Sade, Dan; Lam, Yee Cheong; Cohen, Yehuda

2016-01-01

Life of bacteria is governed by the physical dimensions of life in microscales, which is dominated by fast diffusion and flow at low Reynolds numbers. Microbial biofilms are structurally and functionally heterogeneous and their development is suggested to be interactively related to their microenvironments. In this study, we were guided by the challenging requirements of precise tools and engineered procedures to achieve reproducible experiments at high spatial and temporal resolutions. Here, we developed a robust precise engineering approach allowing for the quantification of real-time, high-content imaging of biofilm behaviour under well-controlled flow conditions. Through the merging of engineering and microbial ecology, we present a rigorous methodology to quantify biofilm development at resolutions of single micrometre and single minute, using a newly developed flow cell. We designed and fabricated a high-precision flow cell to create defined and reproducible flow conditions. We applied high-content confocal laser scanning microscopy and developed image quantification using a model biofilm of a defined opportunistic strain, Pseudomonas putida OUS82. We observed complex patterns in the early events of biofilm formation, which were followed by total dispersal. These patterns were closely related to the flow conditions. These biofilm behavioural phenomena were found to be highly reproducible, despite the heterogeneous nature of biofilm. PMID:28721252

Maternal dietary patterns in pregnancy and the association with small-for-gestational-age infants.

PubMed

Thompson, John M D; Wall, Clare; Becroft, David M O; Robinson, Elizabeth; Wild, Chris J; Mitchell, Edwin A

2010-06-01

Maternal nutritional status before and during pregnancy is important for the growth and development of the fetus. The effects of pre-pregnancy nutrition (estimated by maternal size) are well documented. There is little information in today's Western society on the effect of maternal nutrition during pregnancy on the fetus. The aim of the study was to describe dietary patterns of a cohort of mothers during pregnancy (using principal components analysis with a varimax rotation) and assess the effect of these dietary patterns on the risk of delivering a small-for-gestational-age (SGA) baby. The study was a case-control study investigating factors related to SGA. The population was 1714 subjects in Auckland, New Zealand, born between October 1995 and November 1997, about half of whom were born SGA ( < or = 10th percentile for sex and gestation). Maternal dietary information was collected using FFQ after delivery for the first and last months of pregnancy. Three dietary patterns (traditional, junk and fusion) were defined. Factors associated with these dietary patterns when examined in multivariable analyses included marital status, maternal weight, maternal age and ethnicity. In multivariable analysis, mothers who had higher 'traditional' diet scores in early pregnancy were less likely to deliver a SGA infant (OR = 0.86; 95 % CI 0.75, 0.99). Maternal diet, particularly in early pregnancy, is important for the development of the fetus. Socio-demographic factors tend to be significantly related to dietary patterns, suggesting that extra resources may be necessary for disadvantaged mothers to ensure good nutrition in pregnancy.

Prepregnancy adherence to dietary patterns and lower risk of gestational diabetes mellitus123

PubMed Central

Tobias, Deirdre K; Zhang, Cuilin; Chavarro, Jorge; Bowers, Katherine; Rich-Edwards, Janet; Rosner, Bernard; Mozaffarian, Dariush; Hu, Frank B

2012-01-01

Background: Previous studies observed inverse associations of adherence to the alternate Mediterranean (aMED), Dietary Approaches to Stop Hypertension (DASH), and alternate Healthy Eating Index (aHEI) dietary patterns with risk of type 2 diabetes; however, their associations with gestational diabetes mellitus (GDM) risk are unknown. Objective: This study aimed to assess usual prepregnancy adherence to well-known dietary patterns and GDM risk. Design: Our study included 21,376 singleton live births reported from 15,254 participants of the Nurses’ Health Study II cohort between 1991 and 2001. Pregnancies were free of prepregnancy chronic disease or previous GDM. Prepregnancy dietary pattern adherence scores were computed based on participants’ usual intake of the patterns’ components, assessed with a validated food-frequency questionnaire. Multivariable logistic regressions with generalized estimating equations were used to estimate the RRs and 95% CIs. Results: Incident first-time GDM was reported in 872 pregnancies. All 3 scores were inversely associated with GDM risk after adjustment for several covariables. In a comparison of the multivariable risk of GDM in participants in the fourth and first quartiles of dietary pattern adherence scores, aMED was associated with a 24% lower risk (RR: 0.76; 95% CI: 0.60, 0.95; P-trend = 0.004), DASH with a 34% lower risk (RR: 0.66; 95% CI: 0.53, 0.82; P-trend = 0.0005), and aHEI with a 46% lower risk (RR: 0.54; 95% CI: 0.43, 0.68; P-trend < 0.0001). Conclusion: Prepregnancy adherence to healthful dietary patterns is significantly associated with a lower risk of GDM. PMID:22760563

Decoding complex flow-field patterns in visual working memory.

PubMed

Christophel, Thomas B; Haynes, John-Dylan

2014-05-01

There has been a long history of research on visual working memory. Whereas early studies have focused on the role of lateral prefrontal cortex in the storage of sensory information, this has been challenged by research in humans that has directly assessed the encoding of perceptual contents, pointing towards a role of visual and parietal regions during storage. In a previous study we used pattern classification to investigate the storage of complex visual color patterns across delay periods. This revealed coding of such contents in early visual and parietal brain regions. Here we aim to investigate whether the involvement of visual and parietal cortex is also observable for other types of complex, visuo-spatial pattern stimuli. Specifically, we used a combination of fMRI and multivariate classification to investigate the retention of complex flow-field stimuli defined by the spatial patterning of motion trajectories of random dots. Subjects were trained to memorize the precise spatial layout of these stimuli and to retain this information during an extended delay. We used a multivariate decoding approach to identify brain regions where spatial patterns of activity encoded the memorized stimuli. Content-specific memory signals were observable in motion sensitive visual area MT+ and in posterior parietal cortex that might encode spatial information in a modality independent manner. Interestingly, we also found information about the memorized visual stimulus in somatosensory cortex, suggesting a potential crossmodal contribution to memory. Our findings thus indicate that working memory storage of visual percepts might be distributed across unimodal, multimodal and even crossmodal brain regions. Copyright © 2014 Elsevier Inc. All rights reserved.

Dietary Patterns, n-3 Fatty Acids Intake from Seafood and High Levels of Anxiety Symptoms during Pregnancy: Findings from the Avon Longitudinal Study of Parents and Children

PubMed Central

Vaz, Juliana dos Santos; Kac, Gilberto; Emmett, Pauline; Davis, John M.; Golding, Jean; Hibbeln, Joseph R.

2013-01-01

Background Little is known about relationships between dietary patterns, n-3 polyunsaturated fatty acids (PUFA) intake and excessive anxiety during pregnancy. Objective To examine whether dietary patterns and n-3 PUFA intake from seafood are associated with high levels of anxiety during pregnancy. Design Pregnant women enrolled from 1991–1992 in ALSPAC (n 9,530). Dietary patterns were established from a food frequency questionnaire using principal component analysis. Total intake of n-3 PUFA (grams/week) from seafood was also examined. Symptoms of anxiety were measured at 32 weeks of gestation with the Crown-Crisp Experiential Index; scores ≥9 corresponding to the 85th percentile was defined as high anxiety symptoms. Multivariate logistic regression models were used to estimate the OR and 95% CI, adjusted by socioeconomic and lifestyle variables. Results Multivariate results showed that women in the highest tertile of the health-conscious (OR 0.77; 0.65–0.93) and the traditional (OR 0.84; 0.73–0.97) pattern scores were less likely to report high levels of anxiety symptoms. Women in the highest tertile of the vegetarian pattern score (OR 1.25; 1.08–1.44) were more likely to have high levels of anxiety, as well as those with no n-3 PUFA intake from seafood (OR 1.53; 1.25–1.87) when compared with those with intake of >1.5 grams/week. Conclusions The present study provides evidence of a relationship between dietary patterns, fish intake or n-3 PUFA intake from seafood and symptoms of anxiety in pregnancy, and suggests that dietary interventions could be used to reduce high anxiety symptoms during pregnancy. PMID:23874437

Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

SIMULATION AND VISUALIZATION OF FLOW PATTERN IN MICROARRAYS FOR LIQUID PHASE OLIGONUCLEOTIDE AND PEPTIDE SYNTHESIS

PubMed Central

O-Charoen, Sirimon; Srivannavit, Onnop; Gulari, Erdogan

2008-01-01

Microfluidic microarrays have been developed for economical and rapid parallel synthesis of oligonucleotide and peptide libraries. For a synthesis system to be reproducible and uniform, it is crucial to have a uniform reagent delivery throughout the system. Computational fluid dynamics (CFD) is used to model and simulate the microfluidic microarrays to study geometrical effects on flow patterns. By proper design geometry, flow uniformity could be obtained in every microreactor in the microarrays. PMID:17480053

Probing Atmospheric Electric Fields in Thunderstorms through Radio Emission from Cosmic-Ray-Induced Air Showers.

PubMed

Schellart, P; Trinh, T N G; Buitink, S; Corstanje, A; Enriquez, J E; Falcke, H; Hörandel, J R; Nelles, A; Rachen, J P; Rossetto, L; Scholten, O; Ter Veen, S; Thoudam, S; Ebert, U; Koehn, C; Rutjes, C; Alexov, A; Anderson, J M; Avruch, I M; Bentum, M J; Bernardi, G; Best, P; Bonafede, A; Breitling, F; Broderick, J W; Brüggen, M; Butcher, H R; Ciardi, B; de Geus, E; de Vos, M; Duscha, S; Eislöffel, J; Fallows, R A; Frieswijk, W; Garrett, M A; Grießmeier, J; Gunst, A W; Heald, G; Hessels, J W T; Hoeft, M; Holties, H A; Juette, E; Kondratiev, V I; Kuniyoshi, M; Kuper, G; Mann, G; McFadden, R; McKay-Bukowski, D; McKean, J P; Mevius, M; Moldon, J; Norden, M J; Orru, E; Paas, H; Pandey-Pommier, M; Pizzo, R; Polatidis, A G; Reich, W; Röttgering, H; Scaife, A M M; Schwarz, D J; Serylak, M; Smirnov, O; Steinmetz, M; Swinbank, J; Tagger, M; Tasse, C; Toribio, M C; van Weeren, R J; Vermeulen, R; Vocks, C; Wise, M W; Wucknitz, O; Zarka, P

2015-04-24

We present measurements of radio emission from cosmic ray air showers that took place during thunderstorms. The intensity and polarization patterns of these air showers are radically different from those measured during fair-weather conditions. With the use of a simple two-layer model for the atmospheric electric field, these patterns can be well reproduced by state-of-the-art simulation codes. This in turn provides a novel way to study atmospheric electric fields.

Characterization of Disease-Related Covariance Topographies with SSMPCA Toolbox: Effects of Spatial Normalization and PET Scanners

PubMed Central

Peng, Shichun; Ma, Yilong; Spetsieris, Phoebe G; Mattis, Paul; Feigin, Andrew; Dhawan, Vijay; Eidelberg, David

2013-01-01

In order to generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [18F]fluorodeoxyglucose PET scans from PD patients and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5 and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in PD patients imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. PMID:23671030

Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners.

PubMed

Peng, Shichun; Ma, Yilong; Spetsieris, Phoebe G; Mattis, Paul; Feigin, Andrew; Dhawan, Vijay; Eidelberg, David

2014-05-01

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. Copyright © 2013 Wiley Periodicals, Inc.

Development of a reproducible method for determining quantity of water and its configuration in a marsh landscape

USGS Publications Warehouse

Suir, Glenn M.; Evers, D. Elaine; Steyer, Gregory D.; Sasser, Charles E.

2013-01-01

Coastal Louisiana is a dynamic and ever-changing landscape. From 1956 to 2010, over 3,734 km2 of Louisiana's coastal wetlands have been lost due to a combination of natural and human-induced activities. The resulting landscape constitutes a mosaic of conditions from highly deteriorated to relatively stable with intact landmasses. Understanding how and why coastal landscapes change over time is critical to restoration and rehabilitation efforts. Historically, changes in marsh pattern (i.e., size and spatial distribution of marsh landmasses and water bodies) have been distinguished using visual identification by individual researchers. Difficulties associated with this approach include subjective interpretation, uncertain reproducibility, and laborious techniques. In order to minimize these limitations, this study aims to expand existing tools and techniques via a computer-based method, which uses geospatial technologies for determining shifts in landscape patterns. Our method is based on a raster framework and uses landscape statistics to develop conditions and thresholds for a marsh classification scheme. The classification scheme incorporates land and water classified imagery and a two-part classification system: (1) ratio of water to land, and (2) configuration and connectivity of water within wetland landscapes to evaluate changes in marsh patterns. This analysis system can also be used to trace trajectories in landscape patterns through space and time. Overall, our method provides a more automated means of quantifying landscape patterns and may serve as a reliable landscape evaluation tool for future investigations of wetland ecosystem processes in the northern Gulf of Mexico.

In vivo Study of the Accuracy of Dual-arch Impressions.

PubMed

de Lima, Luciana Martinelli Santayana; Borges, Gilberto Antonio; Junior, Luiz Henrique Burnett; Spohr, Ana Maria

2014-06-01

This study evaluated in vivo the accuracy of metal (Smart®) and plastic (Triple Tray®) dual-arch trays used with vinyl polysiloxane (Flexitime®), in the putty/wash viscosity, as well as polyether (Impregum Soft®) in the regular viscosity. In one patient, an implant-level transfer was screwed on an implant in the mandibular right first molar, serving as a pattern. Ten impressions were made with each tray and impression material. The impressions were poured with Type IV gypsum. The width and height of the pattern and casts were measured in a profile projector (Nikon). The results were submitted to Student's t-test for one sample (α = 0.05). For the width distance, the plastic dual-arch trays with vinyl polysiloxane (4.513 mm) and with polyether (4.531 mm) were statistically wider than the pattern (4.489 mm). The metal dual-arch tray with vinyl polysiloxane (4.504 mm) and with polyether (4.500 mm) did not differ statistically from the pattern. For the height distance, only the metal dual-arch tray with polyether (2.253 mm) differed statistically from the pattern (2.310 mm). The metal dual-arch tray with vinyl polysiloxane, in the putty/wash viscosities, reproduced casts with less distortion in comparison with the same technique with the plastic dual-arch tray. The plastic or metal dual-arch trays with polyether reproduced cast with greater distortion. How to cite the article: Santayana de Lima LM, Borges GA, Burnett LH Jr, Spohr AM. In vivo study of the accuracy of dual-arch impressions. J Int Oral Health 2014;6(3):50-5.

A simulation study of mutations in the genetic regulatory hierarchy for butterfly eyespot focus determination.

PubMed

Marcus, Jeffrey M; Evans, Travis M

2008-09-01

The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. A recent computational model tested genetic regulatory hierarchies hypothesized to underlie the formation of butterfly eyespot foci [Evans, T.M., Marcus, J.M., 2006. A simulation study of the genetic regulatory hierarchy for butterfly eyespot focus determination. Evol. Dev. 8, 273-283]. The computational model demonstrated that one proposed hierarchy was incapable of reproducing the known patterns of gene expression associated with eyespot focus determination in wild-type butterflies, but that two slightly modified alternative hierarchies were capable of reproducing all of the known gene expressions patterns. Here we extend the computational models previously implemented in Delphi 2.0 to two mutants derived from the squinting bush brown butterfly (Bicyclus anynana). These two mutants, comet and Cyclops, have aberrantly shaped eyespot foci that are produced by different mechanisms. The comet mutation appears to produce a modified interaction between the wing margin and the eyespot focus that results in a series of comet-shaped eyespot foci. The Cyclops mutation causes the failure of wing vein formation between two adjacent wing-cells and the fusion of two adjacent eyespot foci to form a single large elongated focus in their place. The computational approach to modeling pattern formation in these mutants allows us to make predictions about patterns of gene expression, which are largely unstudied in butterfly mutants. It also suggests a critical experiment that will allow us to distinguish between two hypothesized genetic regulatory hierarchies that may underlie all butterfly eyespot foci.

The intrinsic dependence structure of peak, volume, duration, and average intensity of hyetographs and hydrographs

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.

2013-06-01

The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp, volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link Xp,V,D, and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between Xp,V,D, and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of Xp,V,D, and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, David E.; Coble, Jamie B.; Jordan, David V.

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicatemore » changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.« less

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings.

PubMed

Malaquias, José B; Ramalho, Francisco S; Dos S Dias, Carlos T; Brugger, Bruno P; S Lira, Aline Cristina; Wilcken, Carlos F; Pachú, Jéssica K S; Zanuncio, José C

2017-02-09

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

PubMed Central

Malaquias, José B.; Ramalho, Francisco S.; dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-01-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied. PMID:28181503

Combinatorial techniques to efficiently investigate and optimize organic thin film processing and properties.

PubMed

Wieberger, Florian; Kolb, Tristan; Neuber, Christian; Ober, Christopher K; Schmidt, Hans-Werner

2013-04-08

In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

NASA Astrophysics Data System (ADS)

Malaquias, José B.; Ramalho, Francisco S.; Dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-02-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Patterns of differentiation among endangered pondberry populations

Treesearch

Craig S Echt; Dennis Deemer; Danny Gustafson

2011-01-01

Pondberry, Lindera melissifolia, is an endangered and partially clonally reproducing shrub species found in isolated populations that inhabit seasonally wet depressions in forested areas of the lower Mississippi River alluvial valley and southeastern regions of the United States. With eleven microsatellite loci, we quantified population genetic differentiation and...

TexTile Math: Multicultural Explorations through Patterns. Grades 3-6.

ERIC Educational Resources Information Center

Franco, Betsy

This book features 34 reproducible student activities exploring textile design through a combination of mathematics, art, and multicultural education. Using colorful squares and triangles, students explore geometry, numbers, area, fractions, logic, and discrete mathematics, while incorporating multicultural themes in the study. The teacher page…

Derivative component analysis for mass spectral serum proteomic profiles.

PubMed

Han, Henry

2014-01-01

As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage. In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers. Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis. Our findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

NASA Astrophysics Data System (ADS)

Vittal, H.; Singh, Jitendra; Kumar, Pankaj; Karmakar, Subhankar

2015-06-01

In watershed management, flood frequency analysis (FFA) is performed to quantify the risk of flooding at different spatial locations and also to provide guidelines for determining the design periods of flood control structures. The traditional FFA was extensively performed by considering univariate scenario for both at-site and regional estimation of return periods. However, due to inherent mutual dependence of the flood variables or characteristics [i.e., peak flow (P), flood volume (V) and flood duration (D), which are random in nature], analysis has been further extended to multivariate scenario, with some restrictive assumptions. To overcome the assumption of same family of marginal density function for all flood variables, the concept of copula has been introduced. Although, the advancement from univariate to multivariate analyses drew formidable attention to the FFA research community, the basic limitation was that the analyses were performed with the implementation of only parametric family of distributions. The aim of the current study is to emphasize the importance of nonparametric approaches in the field of multivariate FFA; however, the nonparametric distribution may not always be a good-fit and capable of replacing well-implemented multivariate parametric and multivariate copula-based applications. Nevertheless, the potential of obtaining best-fit using nonparametric distributions might be improved because such distributions reproduce the sample's characteristics, resulting in more accurate estimations of the multivariate return period. Hence, the current study shows the importance of conjugating multivariate nonparametric approach with multivariate parametric and copula-based approaches, thereby results in a comprehensive framework for complete at-site FFA. Although the proposed framework is designed for at-site FFA, this approach can also be applied to regional FFA because regional estimations ideally include at-site estimations. The framework is based on the following steps: (i) comprehensive trend analysis to assess nonstationarity in the observed data; (ii) selection of the best-fit univariate marginal distribution with a comprehensive set of parametric and nonparametric distributions for the flood variables; (iii) multivariate frequency analyses with parametric, copula-based and nonparametric approaches; and (iv) estimation of joint and various conditional return periods. The proposed framework for frequency analysis is demonstrated using 110 years of observed data from Allegheny River at Salamanca, New York, USA. The results show that for both univariate and multivariate cases, the nonparametric Gaussian kernel provides the best estimate. Further, we perform FFA for twenty major rivers over continental USA, which shows for seven rivers, all the flood variables followed nonparametric Gaussian kernel; whereas for other rivers, parametric distributions provide the best-fit either for one or two flood variables. Thus the summary of results shows that the nonparametric method cannot substitute the parametric and copula-based approaches, but should be considered during any at-site FFA to provide the broadest choices for best estimation of the flood return periods.

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

Critical aspects of substrate nanopatterning for the ordered growth of GaN nanocolumns.

PubMed

Barbagini, Francesca; Bengoechea-Encabo, Ana; Albert, Steven; Martinez, Javier; Sanchez García, Miguel Angel; Trampert, Achim; Calleja, Enrique

2011-12-14

Precise and reproducible surface nanopatterning is the key for a successful ordered growth of GaN nanocolumns. In this work, we point out the main technological issues related to the patterning process, mainly surface roughness and cleaning, and mask adhesion to the substrate. We found that each of these factors, process-related, has a dramatic impact on the subsequent selective growth of the columns inside the patterned holes. We compare the performance of e-beam lithography, colloidal lithography, and focused ion beam in the fabrication of hole-patterned masks for ordered columnar growth. These results are applicable to the ordered growth of nanocolumns of different materials.

Patterns of sediment dispersion coastwise the State of Bahia - Brazil.

PubMed

Bittencourt; Dominguez; Martin; Silva

2000-06-01

Using the average directions of the main wave-fronts which approach the coast of Bahia State - coinciding with that of the main wind occurring in the area - and of their periods, we define a wave climate model based on the construction of refraction diagrams. The resulting model of sediment transport was able to reproduce, in a general way, the sediment dispersion patterns furnished by geomorphic indicators of the littoral drift. These dispersion patterns control the generation of different types of sediment accumulations and of coastal stretches under erosion. We demonstrate that the presence of the Abrolhos and Corumbaú Point coral reefs is an important factor controlling the sediment dispersion patterns, since them act as a large protection against the waves action.

Characterization of geostationary particle signatures based on the 'injection boundary' model

NASA Technical Reports Server (NTRS)

Mauk, B. H.; Meng, C.-I.

1983-01-01

A simplified analytical procedure is used to characterize the details of geostationary particle signatures, in order to lend support to the 'injection boundary' concept. The signatures are generated by the time-of-flight effects evolving from an initial sharply defined, double spiraled boundary configuration. Complex and highly variable dispersion patterns often observed by geostationary satellites are successfully reproduced through the exclusive use of the most fundamental convection configuration characteristics. Many of the details of the patterns have not been previously presented. It is concluded that most of the dynamical dispersion features can be mapped to the double spiral boundary without further ad hoc assumptions, and that predicted and observed dispersion patterns exhibit symmetries distinct from those associated with the quasi-stationary particle convection patterns.

Western Dietary Pattern Increases, and Prudent Dietary Pattern Decreases, Risk of Incident Diverticulitis in a Prospective Cohort Study.

PubMed

Strate, Lisa L; Keeley, Brieze R; Cao, Yin; Wu, Kana; Giovannucci, Edward L; Chan, Andrew T

2017-04-01

Dietary fiber is implicated as a risk factor for diverticulitis. Analyses of dietary patterns may provide information on risk beyond those of individual foods or nutrients. We examined whether major dietary patterns are associated with risk of incident diverticulitis. We performed a prospective cohort study of 46,295 men who were free of diverticulitis and known diverticulosis in 1986 (baseline) using data from the Health Professionals Follow-Up Study. Each study participant completed a detailed medical and dietary questionnaire at baseline. We sent supplemental questionnaires to men reporting incident diverticulitis on biennial follow-up questionnaires. We assessed diet every 4 years using a validated food frequency questionnaire. Western (high in red meat, refined grains, and high-fat dairy) and prudent (high in fruits, vegetables, and whole grains) dietary patterns were identified using principal component analysis. Follow-up time accrued from the date of return of the baseline questionnaire in 1986 until a diagnosis of diverticulitis, diverticulosis or diverticular bleeding; death; or December 31, 2012. The primary end point was incident diverticulitis. During 894,468 person years of follow-up, we identified 1063 incident cases of diverticulitis. After adjustment for other risk factors, men in the highest quintile of Western dietary pattern score had a multivariate hazard ratio of 1.55 (95% CI, 1.20-1.99) for diverticulitis compared to men in the lowest quintile. High vs low prudent scores were associated with decreased risk of diverticulitis (multivariate hazard ratio, 0.74; 95% CI, 0.60-0.91). The association between dietary patterns and diverticulitis was predominantly attributable to intake of fiber and red meat. In a prospective cohort study of 46,295 men, a Western dietary pattern was associated with increased risk of diverticulitis, and a prudent pattern was associated with decreased risk. These data can guide dietary interventions for the prevention of diverticulitis. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Strategy for an Association Study of the Intestinal Microbiome and Brain Metabolome Across the Lifespan of Rats.

PubMed

Chen, Tianlu; You, Yijun; Xie, Guoxiang; Zheng, Xiaojiao; Zhao, Aihua; Liu, Jiajian; Zhao, Qing; Wang, Shouli; Huang, Fengjie; Rajani, Cynthia; Wang, Congcong; Chen, Shaoqiu; Ni, Yan; Yu, Herbert; Deng, Youping; Wang, Xiaoyan; Jia, Wei

2018-02-20

There is increased appreciation for the diverse roles of the microbiome-gut-brain axis on mammalian growth and health throughout the lifespan. Numerous studies have demonstrated that the gut microbiome and their metabolites are extensively involved in the communication between brain and gut. Association study of brain metabolome and gut microbiome is an active field offering large amounts of information on the interaction of microbiome, brain and gut but data size and complicated hierarchical relationships were found to be major obstacles to the formation of significant, reproducible conclusions. This study addressed a two-level strategy of brain metabolome and gut microbiome association analysis of male Wistar rats in the process of growth, employing several analytical platforms and various bioinformatics methods. Trajectory analysis showed that the age-related brain metabolome and gut microbiome had similarity in overall alteration patterns. Four high taxonomical level correlated pairs of "metabolite type-bacterial phylum", including "lipids-Spirochaetes", "free fatty acids (FFAs)-Firmicutes", "bile acids (BAs)-Firmicutes", and "Neurotransmitters-Bacteroidetes", were screened out based on unit- and multivariant correlation analysis and function analysis. Four groups of specific "metabolite-bacterium" association pairs from within the above high level key pairs were further identified. The key correlation pairs were validated by an independent animal study. This two-level strategy is effective in identifying principal correlations in big data sets obtained from the systematic multiomics study, furthering our understanding on the lifelong connection between brain and gut.

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Gu, Yue; Miao, Shuo; Han, Junxia; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

2018-06-01

Objective. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. Approach. FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. Main results. The results showed that 86.0% (p<0.001 ) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. Significance. This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Complex codon usage pattern and compositional features of retroviruses.

PubMed

RoyChoudhury, Sourav; Mukherjee, Debaprasad

2013-01-01

Retroviruses infect a wide range of organisms including humans. Among them, HIV-1, which causes AIDS, has now become a major threat for world health. Some of these viruses are also potential gene transfer vectors. In this study, the patterns of synonymous codon usage in retroviruses have been studied through multivariate statistical methods on ORFs sequences from the available 56 retroviruses. The principal determinant for evolution of the codon usage pattern in retroviruses seemed to be the compositional constraints, while selection for translation of the viral genes plays a secondary role. This was further supported by multivariate analysis on relative synonymous codon usage. Thus, it seems that mutational bias might have dominated role over translational selection in shaping the codon usage of retroviruses. Codon adaptation index was used to identify translationally optimal codons among genes from retroviruses. The comparative analysis of the preferred and optimal codons among different retroviral groups revealed that four codons GAA, AAA, AGA, and GGA were significantly more frequent in most of the retroviral genes inspite of some differences. Cluster analysis also revealed that phylogenetically related groups of retroviruses have probably evolved their codon usage in a concerted manner under the influence of their nucleotide composition.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Simple Models of SL-9 Impact Plumes

NASA Astrophysics Data System (ADS)

Harrington, J.; Deming, L. D.

1996-09-01

The impacts of the larger fragments of Comet Shomaker-Levy 9 on Jupiter left debris patterns of consistent appearance, likely caused by the landing of the observed impact plumes. Realistic fluid simulations of impact plume evolution may take months to years for even single computer runs. To provide guidance for these models and to elucidate the most basic aspects of the plumes, debris patterns, and their ultimate effect on the atmosphere, we have developed simple models that reproduce many of the key features. These Monte-Carlo models divide the plume into discrete mass elements, assign to them a velocity distribution based on numerical impact models, and follow their ballistic trajectories until they hit the planet. If particles go no higher than the observed ~ 3,000 km plume heights, they cannot reach the observed crescent pattern located ~ 10,000 km from the impact sites unless they slide horizontally after ballistic flight. By introducing parameterized sliding or higher trajectories, we can reproduce most of the observed impact features, including the central streak, the crescent, and the ephemeral ring located ~ 30,000 km from the impact sites. We also keep track of the amounts of energy and momentum delivered to the atmosphere as a function of time and location, for use in atmospheric models (D. Deming and J. Harrington, this meeting).

On the function of stress rhythms in speech: evidence of a link with grouping effects on serial memory.

PubMed

Boucher, Victor J

2006-01-01

Language learning requires a capacity to recall novel series of speech sounds. Research shows that prosodic marks create grouping effects enhancing serial recall. However, any restriction on memory affecting the reproduction of prosody would limit the set of patterns that could be learned and subsequently used in speech. By implication, grouping effects of prosody would also be limited to reproducible patterns. This view of the role of prosody and the contribution of memory processes in the organization of prosodic patterns is examined by evaluating the correspondence between a reported tendency to restrict stress intervals in speech and size limits on stress-grouping effects. French speech is used where stress defines the endpoints of groups. In Experiment 1, 40 speakers recalled novel series of syllables containing stress-groups of varying size. Recall was not enhanced by groupings exceeding four syllables, which corresponded to a restriction on the reproducibility of stress-groups. In Experiment 2, the subjects produced given sentences containing phrases of differing length. The results show a strong tendency to insert stress within phrases that exceed four syllables. Since prosody can arise in the recall of syntactically unstructured lists, the results offer initial support for viewing memory processes as a factor of stress-rhythm organization.

Automated measurement and classification of pulmonary blood-flow velocity patterns using phase-contrast MRI and correlation analysis.

PubMed

van Amerom, Joshua F P; Kellenberger, Christian J; Yoo, Shi-Joon; Macgowan, Christopher K

2009-01-01

An automated method was evaluated to detect blood flow in small pulmonary arteries and classify each as artery or vein, based on a temporal correlation analysis of their blood-flow velocity patterns. The method was evaluated using velocity-sensitive phase-contrast magnetic resonance data collected in vitro with a pulsatile flow phantom and in vivo in 11 human volunteers. The accuracy of the method was validated in vitro, which showed relative velocity errors of 12% at low spatial resolution (four voxels per diameter), but was reduced to 5% at increased spatial resolution (16 voxels per diameter). The performance of the method was evaluated in vivo according to its reproducibility and agreement with manual velocity measurements by an experienced radiologist. In all volunteers, the correlation analysis was able to detect and segment peripheral pulmonary vessels and distinguish arterial from venous velocity patterns. The intrasubject variability of repeated measurements was approximately 10% of peak velocity, or 2.8 cm/s root-mean-variance, demonstrating the high reproducibility of the method. Excellent agreement was obtained between the correlation analysis and radiologist measurements of pulmonary velocities, with a correlation of R2=0.98 (P<.001) and a slope of 0.99+/-0.01.

Hebbian Plasticity Realigns Grid Cell Activity with External Sensory Cues in Continuous Attractor Models

PubMed Central

Mulas, Marcello; Waniek, Nicolai; Conradt, Jörg

2016-01-01

After the discovery of grid cells, which are an essential component to understand how the mammalian brain encodes spatial information, three main classes of computational models were proposed in order to explain their working principles. Amongst them, the one based on continuous attractor networks (CAN), is promising in terms of biological plausibility and suitable for robotic applications. However, in its current formulation, it is unable to reproduce important electrophysiological findings and cannot be used to perform path integration for long periods of time. In fact, in absence of an appropriate resetting mechanism, the accumulation of errors over time due to the noise intrinsic in velocity estimation and neural computation prevents CAN models to reproduce stable spatial grid patterns. In this paper, we propose an extension of the CAN model using Hebbian plasticity to anchor grid cell activity to environmental landmarks. To validate our approach we used as input to the neural simulations both artificial data and real data recorded from a robotic setup. The additional neural mechanism can not only anchor grid patterns to external sensory cues but also recall grid patterns generated in previously explored environments. These results might be instrumental for next generation bio-inspired robotic navigation algorithms that take advantage of neural computation in order to cope with complex and dynamic environments. PMID:26924979

How a life-like system emerges from a simplistic particle motion law.

PubMed

Schmickl, Thomas; Stefanec, Martin; Crailsheim, Karl

2016-11-30

Self-structuring patterns can be observed all over the universe, from galaxies to molecules to living matter, yet their emergence is waiting for full understanding. We discovered a simple motion law for moving and interacting self-propelled particles leading to a self-structuring, self-reproducing and self-sustaining life-like system. The patterns emerging within this system resemble patterns found in living organisms. The emergent cells we found show a distinct life cycle and even create their own ecosystem from scratch. These structures grow and reproduce on their own, show self-driven behavior and interact with each other. Here we analyze the macroscopic properties of the emerging ecology, as well as the microscopic properties of the mechanism that leads to it. Basic properties of the emerging structures (size distributions, longevity) are analyzed as well as their resilience against sensor or actuation noise. Finally, we explore parameter space for potential other candidates of life. The generality and simplicity of the motion law provokes the thought that one fundamental rule, described by one simple equation yields various structures in nature: it may work on different time- and size scales, ranging from the self-structuring universe, to emergence of living beings, down to the emergent subatomic formation of matter.

Limitations of BCC_CSM's ability to predict summer precipitation over East Asia and the Northwestern Pacific

NASA Astrophysics Data System (ADS)

Gong, Zhiqiang; Dogar, Muhammad Mubashar Ahmad; Qiao, Shaobo; Hu, Po; Feng, Guolin

2017-09-01

This study examines the ability of the Beijing Climate Center Climate System Model (BCC_CSM) to predict the meridional pattern of summer precipitation over East Asia-Northwest Pacific (EA-NWP) and its East Asia-Pacific (EAP) teleconnection. The differences of summer precipitation modes of the empirical orthogonal function and the bias of atmospheric circulations over EA-NWP are analyzed to determine the reason for the precipitation prediction errors. Results indicate that the BCC_CSM could not reproduce the positive-negative-positive meridional tripole pattern from south to north that differs markedly from that observed over the last 20 years. This failure can be attributed to the bias of the BCC_CSM hindcasts of the summer EAP teleconnection and the low predictability of 500 hPa at the mid-high latitude lobe of the EAP. Meanwhile, the BCC_CSM hindcasts' deficiencies of atmospheric responses to SST anomalies over the Indonesia maritime continent (IMC) resulted in opposite and geographically shifted geopotential anomalies at 500 hPa as well as wind and vorticity anomalies at 850 hPa, rendering the BCC_CSM unable to correctly reproduce the EAP teleconnection pattern. Understanding these two problems will help further improve BCC_CSM's summer precipitation forecasting ability over EA-NWP.

Simple rules govern the patterns of Arctic sea ice melt ponds

NASA Astrophysics Data System (ADS)

Popovic, P.; Cael, B. B.; Abbot, D. S.; Silber, M.

2017-12-01

Climate change, amplified in the far north, has led to a rapid sea ice decline in recent years. Melt ponds that form on the surface of Arctic sea ice in the summer significantly lower the ice albedo, thereby accelerating ice melt. Pond geometry controls the details of this crucial feedback. However, currently it is unclear how to model this intricate geometry. Here we show that an extremely simple model of voids surrounding randomly sized and placed overlapping circles reproduces the essential features of pond patterns. The model has only two parameters, circle scale and the fraction of the surface covered by voids, and we choose them by comparing the model to pond images. Using these parameters the void model robustly reproduces all of the examined pond features such as the ponds' area-perimeter relationship and the area-abundance relationship over nearly 7 orders of magnitude. By analyzing airborne photographs of sea ice, we also find that the typical pond scale is surprisingly constant across different years, regions, and ice types. These results demonstrate that the geometric and abundance patterns of Arctic melt ponds can be simply described, and can guide future models of Arctic melt ponds to improve predictions of how sea ice will respond to Arctic warming.

How a life-like system emerges from a simple particle motion law

PubMed Central

Schmickl, Thomas; Stefanec, Martin; Crailsheim, Karl

2016-01-01

Self-structuring patterns can be observed all over the universe, from galaxies to molecules to living matter, yet their emergence is waiting for full understanding. We discovered a simple motion law for moving and interacting self-propelled particles leading to a self-structuring, self-reproducing and self-sustaining life-like system. The patterns emerging within this system resemble patterns found in living organisms. The emergent cells we found show a distinct life cycle and even create their own ecosystem from scratch. These structures grow and reproduce on their own, show self-driven behavior and interact with each other. Here we analyze the macroscopic properties of the emerging ecology, as well as the microscopic properties of the mechanism that leads to it. Basic properties of the emerging structures (size distributions, longevity) are analyzed as well as their resilience against sensor or actuation noise. Finally, we explore parameter space for potential other candidates of life. The generality and simplicity of the motion law provokes the thought that one fundamental rule, described by one simple equation yields various structures in nature: it may work on different time- and size scales, ranging from the self-structuring universe, to emergence of living beings, down to the emergent subatomic formation of matter. PMID:27901107

Pattern selection in solidification

NASA Technical Reports Server (NTRS)

Langer, J. S.

1984-01-01

Directional solidification of alloys produces a wide variety of cellular or lamellar structures which, depending upon growth conditions, may be reproducibly regular or may behave chaotically. It is not well understood how these patterns are selected and controlled or even whether there ever exist sharp selection mechanisms. A related phenomenon is the spatial propagation of a pattern into a system which has been caused to become unstable against pattern-forming deformations. This phenomenon has some features in common with the propagation of sidebranching modes in dendritic solidification. In a class of one-dimensional models, the nonlinear system can be shown to select the propagating mode in which the leading edge of the pattern is just marginally stable. This stability principle, when applicable, predicts both the speed of propagation and the geometrical characteristics of the pattern which forms behind the moving front. A boundary-layer model for fully two or three dimensional solidification problems appears to exhibit similar mathematical behavior.

Hands-on Science: Wildcatters.

ERIC Educational Resources Information Center

Markle, Sandra

1988-01-01

A science unit illustrates the concept of scientific predictions by using how geologists predict where to drill for oil as an example. In a related exercise, everyday items such as bricks, sand, and marbles introduce permeability. Other activities demonstrate how to base predictions on established patterns. A reproducible page is provided. (JL)

The Red and White Yeast Lab: An Introduction to Science as a Process.

ERIC Educational Resources Information Center

White, Brian T.

1999-01-01

Describes an experimental system based on an engineered strain of bakers' yeast that is designed to involve students in the process by which scientific knowledge is generated. Students are asked to determine why the yeast grow to form a reproducible pattern of red and white. (WRM)

Bilingual Performance on Nonword Repetition in Spanish and English

ERIC Educational Resources Information Center

Summers, Connie; Bohman, Thomas M.; Gillam, Ronald B.; Pena, Elizabeth D.; Bedore, Lisa M.

2010-01-01

Background: Nonword repetition (NWR) involves the ability to perceive, store, recall and reproduce phonological sequences. These same abilities play a role in word and morpheme learning. Cross-linguistic studies of performance on NWR tasks, word learning, and morpheme learning yield patterns of increased performance on all three tasks as a…

Computerized recognition of persons by EEG spectral patterns.

PubMed

Stassen, H H

1980-07-01

Modified techniques of communication theory in connection with multivariate statistical procedures were applied to a sample of 82 patients for the purpose of defining EEG spectral patterns and for solving the relevant classification problems. Ten measurements per patient were made and it could be shown that a subject can be characterized and be recognized by his EEG spectral pattern with high reliability and a confidence probability of almost 90%. This result is valid not only for normal adults but also for schizophrenic patients, implying a close relationship between the EEG spectral pattern and the individual person. At the moment the nature of this relationship is not clear; in particular the supposed relationship to psychopathology could not be proved.

The challenges of neural mind-reading paradigms.

PubMed

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

DENBRAN: A basic program for a significance test for multivariate normality of clusters from branching patterns in dendrograms

NASA Astrophysics Data System (ADS)

Sneath, P. H. A.

A BASIC program is presented for significance tests to determine whether a dendrogram is derived from clustering of points that belong to a single multivariate normal distribution. The significance tests are based on statistics of the Kolmogorov—Smirnov type, obtained by comparing the observed cumulative graph of branch levels with a graph for the hypothesis of multivariate normality. The program also permits testing whether the dendrogram could be from a cluster of lower dimensionality due to character correlations. The program makes provision for three similarity coefficients, (1) Euclidean distances, (2) squared Euclidean distances, and (3) Simple Matching Coefficients, and for five cluster methods (1) WPGMA, (2) UPGMA, (3) Single Linkage (or Minimum Spanning Trees), (4) Complete Linkage, and (5) Ward's Increase in Sums of Squares. The program is entitled DENBRAN.

Patterns of Activity in A Global Model of A Solar Active Region

NASA Technical Reports Server (NTRS)

Bradshaw, S. J.; Viall, N. M.

2016-01-01

In this work we investigate the global activity patterns predicted from a model active region heated by distributions of nanoflares that have a range of frequencies. What differs is the average frequency of the distributions. The activity patterns are manifested in time lag maps of narrow-band instrument channel pairs. We combine hydrodynamic and forward modeling codes with a magnetic field extrapolation to create a model active region and apply the time lag method to synthetic observations. Our aim is not to reproduce a particular set of observations in detail, but to recover some typical properties and patterns observed in active regions. Our key findings are the following. (1) Cooling dominates the time lag signature and the time lags between the channel pairs are generally consistent with observed values. (2) Shorter coronal loops in the core cool more quickly than longer loops at the periphery. (3) All channel pairs show zero time lag when the line of sight passes through coronal loop footpoints. (4) There is strong evidence that plasma must be re-energized on a timescale comparable to the cooling timescale to reproduce the observed coronal activity, but it is likely that a relatively broad spectrum of heating frequencies are operating across active regions. (5) Due to their highly dynamic nature, we find nanoflare trains produce zero time lags along entire flux tubes in our model active region that are seen between the same channel pairs in observed active regions.

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Arabidopsis phenotyping through Geometric Morphometrics.

PubMed

Manacorda, Carlos A; Asurmendi, Sebastian

2018-06-18

Recently, much technical progress was achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it now possible to extract shape and size parameters for genetic, physiological and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations amongst groups and measure them in shape distance units. Here, a particular scheme of landmarks placement on Arabidopsis rosette images is proposed to study shape variation in the case of viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

Multivariate analysis of DSC-XRD simultaneous measurement data: a study of multistage crystalline structure changes in a linear poly(ethylene imine) thin film.

PubMed

Kakuda, Hiroyuki; Okada, Tetsuo; Otsuka, Makoto; Katsumoto, Yukiteru; Hasegawa, Takeshi

2009-01-01

A multivariate analytical technique has been applied to the analysis of simultaneous measurement data from differential scanning calorimetry (DSC) and X-ray diffraction (XRD) in order to study thermal changes in crystalline structure of a linear poly(ethylene imine) (LPEI) film. A large number of XRD patterns generated from the simultaneous measurements were subjected to an augmented alternative least-squares (ALS) regression analysis, and the XRD patterns were readily decomposed into chemically independent XRD patterns and their thermal profiles were also obtained at the same time. The decomposed XRD patterns and the profiles were useful in discussing the minute peaks in the DSC. The analytical results revealed the following changes of polymorphisms in detail: An LPEI film prepared by casting an aqueous solution was composed of sesquihydrate and hemihydrate crystals. The sesquihydrate one was lost at an early stage of heating, and the film changed into an amorphous state. Once the sesquihydrate was lost by heating, it was not recovered even when it was cooled back to room temperature. When the sample was heated again, structural changes were found between the hemihydrate and the amorphous components. In this manner, the simultaneous DSC-XRD measurements combined with ALS analysis proved to be powerful for obtaining a better understanding of the thermally induced changes of the crystalline structure in a polymer film.

Obesity in trauma patients: correlations of body mass index with outcomes, injury patterns, and complications.

PubMed

Evans, David C; Stawicki, Stanislaw P A; Davido, H Tracy; Eiferman, Daniel

2011-08-01

Current understanding of the effects of obesity on trauma patients is incomplete. We hypothesized that among older trauma patients, obese patients differ from nonobese patients in injury patterns, complications, and mortality. Patients older than 45 years old presenting to a Level I trauma center were included in this retrospective database analysis (n = 461). Body mass index (BMI) groups were defined as underweight less than 18.5 kg/m(2), normal 18.5 to 24.9 kg/m(2), overweight 25.0 to 29.9 kg/m(2), or obese greater than 30 kg/m(2). Injury patterns, complications, and outcomes were analyzed using univariate analyses, multivariate logistic regression, and Kaplan-Meier survival analysis. Higher BMI is associated with a higher incidence of torso injury and proximal upper extremity injuries in blunt trauma (n = 410). All other injury patterns and complications (except anemia) were similar between BMI groups. The underweight (BMI less than 18.5 kg/m(2)) group had significantly lower 90-day survival than other groups (P < 0.05). BMI is not a predictor of morbidity or mortality in multivariate analysis. Among older blunt trauma patients, increasing BMI is associated with higher rates of torso and proximal upper extremity injuries. Our study suggests that obesity is not an independent risk factor for complications or mortality after trauma in older patients. Conversely, underweight trauma patients had a lower 90-day survival.

A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale

NASA Astrophysics Data System (ADS)

Berchet, Antoine; Zink, Katrin; Muller, Clive; Oettl, Dietmar; Brunner, Juerg; Emmenegger, Lukas; Brunner, Dominik

2017-06-01

A cost-effective method is presented allowing to simulate the air flow and pollutant dispersion in a whole city over multiple years at the building-resolving scale with hourly time resolution. This combination of high resolution and long time span is critically needed for epidemiological studies and for air pollution control, but still poses a great challenge for current state-of-the-art modelling techniques. The presented method relies on the pre-computation of a discrete set of possible weather situations and corresponding steady-state flow and dispersion patterns. The most suitable situation for any given hour is then selected by matching the simulated wind patterns to meteorological observations in and around the city. The catalogue of pre-computed situations corresponds to different large-scale forcings in terms of wind speed, wind direction and stability. A meteorological model converts these forcings into realistic mesoscale flow patterns accounting for the effects of topography and land-use contrasts in a domain covering the city and its surroundings. These mesoscale patterns serve as boundary conditions for a microscale urban flow model which finally drives a Lagrangian air pollutant dispersion model. The method is demonstrated with the modelling system GRAMM/GRAL v14.8 for two Swiss cities in complex terrain, Zurich and Lausanne. The mesoscale flow patterns in the two regions of interest, dominated by land-lake breezes and driven by the partly steep topography, are well reproduced in the simulations matched to in situ observations. In particular, the combination of wind measurements at different locations around the city appeared to be a robust approach to deduce the stability class for the boundary layer within the city. This information is critical for predicting the temporal variability of pollution concentration within the city, regarding their relationship with the intensity of horizontal and vertical dispersion and of turbulence. In the vicinity of sources, the 5 m resolution chosen in our set-up is not always sufficient to reproduce the very steep concentration gradients, pointing at additional cost optimisations in the method required to make higher resolutions affordable. Nevertheless, the catalogue-based methodology allows reproducing concentration variability very consistently further away from emission sources, hence for most parts of the city.

The chemiluminescence based Ziplex automated workstation focus array reproduces ovarian cancer Affymetrix GeneChip expression profiles.

PubMed

Quinn, Michael C J; Wilson, Daniel J; Young, Fiona; Dempsey, Adam A; Arcand, Suzanna L; Birch, Ashley H; Wojnarowicz, Paulina M; Provencher, Diane; Mes-Masson, Anne-Marie; Englert, David; Tonin, Patricia N

2009-07-06

As gene expression signatures may serve as biomarkers, there is a need to develop technologies based on mRNA expression patterns that are adaptable for translational research. Xceed Molecular has recently developed a Ziplex technology, that can assay for gene expression of a discrete number of genes as a focused array. The present study has evaluated the reproducibility of the Ziplex system as applied to ovarian cancer research of genes shown to exhibit distinct expression profiles initially assessed by Affymetrix GeneChip analyses. The new chemiluminescence-based Ziplex gene expression array technology was evaluated for the expression of 93 genes selected based on their Affymetrix GeneChip profiles as applied to ovarian cancer research. Probe design was based on the Affymetrix target sequence that favors the 3' UTR of transcripts in order to maximize reproducibility across platforms. Gene expression analysis was performed using the Ziplex Automated Workstation. Statistical analyses were performed to evaluate reproducibility of both the magnitude of expression and differences between normal and tumor samples by correlation analyses, fold change differences and statistical significance testing. Expressions of 82 of 93 (88.2%) genes were highly correlated (p < 0.01) in a comparison of the two platforms. Overall, 75 of 93 (80.6%) genes exhibited consistent results in normal versus tumor tissue comparisons for both platforms (p < 0.001). The fold change differences were concordant for 87 of 93 (94%) genes, where there was agreement between the platforms regarding statistical significance for 71 (76%) of 87 genes. There was a strong agreement between the two platforms as shown by comparisons of log2 fold differences of gene expression between tumor versus normal samples (R = 0.93) and by Bland-Altman analysis, where greater than 90% of expression values fell within the 95% limits of agreement. Overall concordance of gene expression patterns based on correlations, statistical significance between tumor and normal ovary data, and fold changes was consistent between the Ziplex and Affymetrix platforms. The reproducibility and ease-of-use of the technology suggests that the Ziplex array is a suitable platform for translational research.

Mimicking muscle activity with electrical stimulation

NASA Astrophysics Data System (ADS)

Johnson, Lise A.; Fuglevand, Andrew J.

2011-02-01

Functional electrical stimulation is a rehabilitation technology that can restore some degree of motor function in individuals who have sustained a spinal cord injury or stroke. One way to identify the spatio-temporal patterns of muscle stimulation needed to elicit complex upper limb movements is to use electromyographic (EMG) activity recorded from able-bodied subjects as a template for electrical stimulation. However, this requires a transfer function to convert the recorded (or predicted) EMG signals into an appropriate pattern of electrical stimulation. Here we develop a generalized transfer function that maps EMG activity into a stimulation pattern that modulates muscle output by varying both the pulse frequency and the pulse amplitude. We show that the stimulation patterns produced by this transfer function mimic the active state measured by EMG insofar as they reproduce with good fidelity the complex patterns of joint torque and joint displacement.

Methylation oligonucleotide microarray: a novel tool to analyze methylation patterns

NASA Astrophysics Data System (ADS)

Hou, Peng; Ji, Meiju; He, Nongyao; Lu, Zuhong

2003-04-01

A new technique to analyze methylation patterns in several adjacent CpG sites was developed and reported here. We selected a 336bp segment of the 5"-untranslated region and the first exon of the p16Ink4a gene, which include the most densely packed CpG fragment of the islands containing 32 CpG dinucleotides, as the investigated target. The probes that include all types of methylation patterns were designed to fabricate a DNA microarray to determine the methylation patterns of seven adjacent CpG dinucleotides sites. High accuracy and reproducibility were observed in several parallel experiments. The results led us to the conclusion that the methylation oligonucleotide microarray can be applied as a novel and powerful tool to map methylation patterns and changes in multiple CpG island loci in a variety of tumors.

Distribution of Total Depressive Symptoms Scores and Each Depressive Symptom Item in a Sample of Japanese Employees.

PubMed

Tomitaka, Shinichiro; Kawasaki, Yohei; Ide, Kazuki; Yamada, Hiroshi; Miyake, Hirotsugu; Furukawa, Toshiaki A; Furukaw, Toshiaki A

2016-01-01

In a previous study, we reported that the distribution of total depressive symptoms scores according to the Center for Epidemiologic Studies Depression Scale (CES-D) in a general population is stable throughout middle adulthood and follows an exponential pattern except for at the lowest end of the symptom score. Furthermore, the individual distributions of 16 negative symptom items of the CES-D exhibit a common mathematical pattern. To confirm the reproducibility of these findings, we investigated the distribution of total depressive symptoms scores and 16 negative symptom items in a sample of Japanese employees. We analyzed 7624 employees aged 20-59 years who had participated in the Northern Japan Occupational Health Promotion Centers Collaboration Study for Mental Health. Depressive symptoms were assessed using the CES-D. The CES-D contains 20 items, each of which is scored in four grades: "rarely," "some," "much," and "most of the time." The descriptive statistics and frequency curves of the distributions were then compared according to age group. The distribution of total depressive symptoms scores appeared to be stable from 30-59 years. The right tail of the distribution for ages 30-59 years exhibited a linear pattern with a log-normal scale. The distributions of the 16 individual negative symptom items of the CES-D exhibited a common mathematical pattern which displayed different distributions with a boundary at "some." The distributions of the 16 negative symptom items from "some" to "most" followed a linear pattern with a log-normal scale. The distributions of the total depressive symptoms scores and individual negative symptom items in a Japanese occupational setting show the same patterns as those observed in a general population. These results show that the specific mathematical patterns of the distributions of total depressive symptoms scores and individual negative symptom items can be reproduced in an occupational population.

Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.

PubMed

Balachandar, Arjun; Prescott, Steven A

2018-05-01

Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of K V 1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (K V 1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed. © 2018 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Transparency, usability, and reproducibility: Guiding principles for improving comparative databases using primates as examples.

PubMed

Borries, Carola; Sandel, Aaron A; Koenig, Andreas; Fernandez-Duque, Eduardo; Kamilar, Jason M; Amoroso, Caroline R; Barton, Robert A; Bray, Joel; Di Fiore, Anthony; Gilby, Ian C; Gordon, Adam D; Mundry, Roger; Port, Markus; Powell, Lauren E; Pusey, Anne E; Spriggs, Amanda; Nunn, Charles L

2016-09-01

Recent decades have seen rapid development of new analytical methods to investigate patterns of interspecific variation. Yet these cutting-edge statistical analyses often rely on data of questionable origin, varying accuracy, and weak comparability, which seem to have reduced the reproducibility of studies. It is time to improve the transparency of comparative data while also making these improved data more widely available. We, the authors, met to discuss how transparency, usability, and reproducibility of comparative data can best be achieved. We propose four guiding principles: 1) data identification with explicit operational definitions and complete descriptions of methods; 2) inclusion of metadata that capture key characteristics of the data, such as sample size, geographic coordinates, and nutrient availability (for example, captive versus wild animals); 3) documentation of the original reference for each datum; and 4) facilitation of effective interactions with the data via user friendly and transparent interfaces. We urge reviewers, editors, publishers, database developers and users, funding agencies, researchers publishing their primary data, and those performing comparative analyses to embrace these standards to increase the transparency, usability, and reproducibility of comparative studies. © 2016 Wiley Periodicals, Inc.

Comparison of Allogeneic and Syngeneic Rat Glioma Models by Using MRI and Histopathologic Evaluation.

PubMed

Biasibetti, Elena; Valazza, Alberto; Capucchio, Maria T; Annovazzi, Laura; Battaglia, Luigi; Chirio, Daniela; Gallarate, Marina; Mellai, Marta; Muntoni, Elisabetta; Peira, Elena; Riganti, Chiara; Schiffer, Davide; Panciani, Pierpaolo; Lanotte, Michele

2017-03-01

Research in neurooncology traditionally requires appropriate in vivo animal models, on which therapeutic strategies are tested before human trials are designed and proceed. Several reproducible animal experimental models, in which human physiologic conditions can be mimicked, are available for studying glioblastoma multiforme. In an ideal rat model, the tumor is of glial origin, grows in predictable and reproducible patterns, closely resembles human gliomas histopathologically, and is weakly or nonimmunogenic. In the current study, we used MRI and histopathologic evaluation to compare the most widely used allogeneic rat glioma model, C6-Wistar, with the F98-Fischer syngeneic rat glioma model in terms of percentage tumor growth or regression and growth rate. In vivo MRI demonstrated considerable variation in tumor volume and frequency between the 2 rat models despite the same stereotactic implantation technique. Faster and more reproducible glioma growth occurred in the immunoresponsive environment of the F98-Fischer model, because the immune response is minimized toward syngeneic cells. The marked inability of the C6-Wistar allogeneic system to generate a reproducible model and the episodes of spontaneous tumor regression with this system may have been due to the increased humoral and cellular immune responses after tumor implantation.

Disease Characteristics, Patterns of Care, and Survival in Very Elderly Patients with Diffuse Large B-Cell Lymphoma

PubMed Central

Williams, Jessica N.; Rai, Ashish; Lipscomb, Joseph; Koff, Jean L.; Nastoupil, Loretta J.; Flowers, Christopher R.

2015-01-01

Background Although rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is considered standard therapy for diffuse large B-cell lymphoma (DLBCL), patterns of use and the impact of R-CHOP on survival in patients >80 years are less clear. Methods We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare database to characterize presentation, treatment, and survival patterns in DLBCL patients diagnosed from 2002–2009. Chi-squared tests compared characteristics and initial treatments of DLBCL patients >80 years and ≤80 years. Multivariable logistic regression models examined factors associated with treatment selection in patients >80 years; standard and propensity score-adjusted multivariable Cox proportional hazards models examined relationships between treatment regimen, treatment duration, and survival. Results Among 4,635 patients with DLBCL, 1,156 (25%) were >80 years. Patients >80 were less likely to receive R-CHOP and more likely to be observed or receive rituximab, cyclophosphamide, vincristine, and prednisone (R-CVP); both p<0.0001. Marital status, stage, disease site, performance status, radiation therapy, and growth factor support were associated with initial R-CHOP in patients >80. In propensity score-matched multivariable Cox proportional hazards models examining relationships between treatment regimen and survival, R-CHOP was the only regimen associated with improved OS (hazard ratio (HR) = 0.45, 95% confidence interval (CI) = 0.33–0.62) and LRS (HR=0.58, 95% CI 0.38–0.88). Conclusions Although DLBCL patients >80 years were less likely to receive R-CHOP, this regimen conferred the longest survival and should be considered for this population. Further studies are needed to characterize the impact of DLBCL treatment on quality of life in this age group. PMID:25675909

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators.

PubMed

Astolfi, L; Cincotti, F; Mattia, D; De Vico Fallani, F; Tocci, A; Colosimo, A; Salinari, S; Marciani, M G; Hesse, W; Witte, H; Ursino, M; Zavaglia, M; Babiloni, F

2008-03-01

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat.

PubMed

Eisenbarth, Hedwig; Chang, Luke J; Wager, Tor D

2016-11-23

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR = 0.54, r SCL = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR→SCL = 0.21 and r SCL→HR = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical-subcortical interactions. Copyright © 2016 the authors 0270-6474/16/3611987-12$15.00/0.

Large-Scale Atmospheric Circulation Patterns Associated with Temperature Extremes as a Basis for Model Evaluation: Methodological Overview and Results

NASA Astrophysics Data System (ADS)

Loikith, P. C.; Broccoli, A. J.; Waliser, D. E.; Lintner, B. R.; Neelin, J. D.

2015-12-01

Anomalous large-scale circulation patterns often play a key role in the occurrence of temperature extremes. For example, large-scale circulation can drive horizontal temperature advection or influence local processes that lead to extreme temperatures, such as by inhibiting moderating sea breezes, promoting downslope adiabatic warming, and affecting the development of cloud cover. Additionally, large-scale circulation can influence the shape of temperature distribution tails, with important implications for the magnitude of future changes in extremes. As a result of the prominent role these patterns play in the occurrence and character of extremes, the way in which temperature extremes change in the future will be highly influenced by if and how these patterns change. It is therefore critical to identify and understand the key patterns associated with extremes at local to regional scales in the current climate and to use this foundation as a target for climate model validation. This presentation provides an overview of recent and ongoing work aimed at developing and applying novel approaches to identifying and describing the large-scale circulation patterns associated with temperature extremes in observations and using this foundation to evaluate state-of-the-art global and regional climate models. Emphasis is given to anomalies in sea level pressure and 500 hPa geopotential height over North America using several methods to identify circulation patterns, including self-organizing maps and composite analysis. Overall, evaluation results suggest that models are able to reproduce observed patterns associated with temperature extremes with reasonable fidelity in many cases. Model skill is often highest when and where synoptic-scale processes are the dominant mechanisms for extremes, and lower where sub-grid scale processes (such as those related to topography) are important. Where model skill in reproducing these patterns is high, it can be inferred that extremes are being simulated for plausible physical reasons, boosting confidence in future projections of temperature extremes. Conversely, where model skill is identified to be lower, caution should be exercised in interpreting future projections.

Short sleep duration as an independent predictor of cardiovascular events in Japanese patients with hypertension.

PubMed

Eguchi, Kazuo; Pickering, Thomas G; Schwartz, Joseph E; Hoshide, Satoshi; Ishikawa, Joji; Ishikawa, Shizukiyo; Shimada, Kazuyuki; Kario, Kazuomi

2008-11-10

It is not known whether short duration of sleep is a predictor of future cardiovascular events in patients with hypertension. To test the hypothesis that short duration of sleep is independently associated with incident cardiovascular diseases (CVD), we performed ambulatory blood pressure (BP) monitoring in 1255 subjects with hypertension (mean [SD] age, 70.4 [9.9] years) and followed them for a mean period of 50 (23) months. Short sleep duration was defined as less than 7.5 hours (20th percentile). Multivariable Cox hazard models predicting CVD events were used to estimate the adjusted hazard ratio and 95% confidence interval (CI) for short sleep duration. A riser pattern was defined when mean nighttime systolic BP exceeded daytime systolic BP. The end point was a cardiovascular event: stroke, fatal or nonfatal myocardial infarction (MI), and sudden cardiac death. In multivariable analyses, short duration of sleep (<7.5 hours) was associated with incident CVD (hazard ratio [HR], 1.68; 95% CI, 1.06-2.66; P = .03). A synergistic interaction was observed between short sleep duration and the riser pattern (P = .09). When subjects were classified according to their sleep time and a riser vs nonriser pattern, the group with shorter sleep duration plus the riser pattern had a substantially and significantly higher incidence of CVD than the group with predominant normal sleep duration plus the nonriser pattern (HR, 4.43; 95% CI, 2.09-9.39; P < .001), independent of covariates. Short duration of sleep is associated with incident CVD risk and the combination of the riser pattern and short duration of sleep that is most strongly predictive of future CVD, independent of ambulatory BP levels. Physicians should inquire about sleep duration in the risk assessment of patients with hypertension.

Compound-specific amino acid δ15N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean

NASA Astrophysics Data System (ADS)

McCarthy, Matthew D.; Lehman, Jennifer; Kudela, Raphael

2013-02-01

Stable nitrogen isotopic analysis of individual amino acids (δ15N-AA) has unique potential to elucidate the complexities of food webs, track heterotrophic transformations, and understand diagenesis of organic nitrogen (ON). While δ15N-AA patterns of autotrophs have been shown to be generally similar, prior work has also suggested that differences may exist between cyanobacteria and eukaryotic algae. However, δ15N-AA patterns in differing oceanic algal groups have never been closely examined. The overarching goals of this study were first to establish a more quantitative understanding of algal δ15N-AA patterns, and second to examine whether δ15N-AA patterns have potential as a new tracer for distinguishing prokaryotic vs. eukaryotic N sources. We measured δ15N-AA from prokaryotic and eukaryotic phytoplankton cultures and used a complementary set of statistical approaches (simple normalization, regression-derived fractionation factors, and multivariate analyses) to test for variations. A generally similar δ15N-AA pattern was confirmed for all algae, however significant AA-specific variation was also consistently identified between the two groups. The relative δ15N fractionation of Glx (glutamine + glutamic acid combined) vs. total proteinaceous N appeared substantially different, which we hypothesize could be related to differing enzymatic forms. In addition, the several other AA (most notably glycine and leucine) appeared to have strong biomarker potential. Finally, we observed that overall patterns of δ15N values in algae correspond well with the Trophic vs. Source-AA division now commonly used to describe variable AA δ15N changes with trophic transfer, suggesting a common mechanistic basis. Overall, these results show that autotrophic δ15N-AA patterns can differ between major algal evolutionary groupings for many AA. The statistically significant multivariate results represent a first approach for testing ideas about relative eukaryotic vs. prokaryotic ON sources in the sea.

A segmentation approach for a delineation of terrestrial ecoregions

NASA Astrophysics Data System (ADS)

Nowosad, J.; Stepinski, T.

2017-12-01

Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for global ecological and conservation studies.

A proto-type design of a real-tissue phantom for the validation of deformation algorithms and 4D dose calculations

NASA Astrophysics Data System (ADS)

Szegedi, M.; Rassiah-Szegedi, P.; Fullerton, G.; Wang, B.; Salter, B.

2010-07-01

The purpose of this study is to design a real-tissue phantom for use in the validation of deformation algorithms. A phantom motion controller that runs sinusoidal and non-regular patient-based breathing pattern, via a piston, was applied to porcine liver tissue. It was regulated to simulate movement ranges similar to recorded implanted liver markers from patients. 4D CT was applied to analyze deformation. The suitability of various markers in the liver and the position reproducibility of markers and of reference points were studied. The similarity of marker motion pattern in the liver phantom and in real patients was evaluated. The viability of the phantom over time and its use with electro-magnetic tracking devices were also assessed. High contrast markers, such as carbon markers, implanted in the porcine liver produced less image artifacts on CT and were well visualized compared to metallic ones. The repositionability of markers was within a measurement accuracy of ±2 mm. Similar anatomical patient motions were reproducible up to elongations of 3 cm for a time period of at least 90 min. The phantom is compatible with electro-magnetic tracking devices and 4D CT. The phantom motion is reproducible and simulates realistic patient motion and deformation. The ability to carry out voxel-based tracking allows for the evaluation of deformation algorithms in a controlled environment with recorded patient traces. The phantom is compatible with all therapy devices clinically encountered in our department.

Repeatability and reproducibility of ribotyping and its computer interpretation.

PubMed

Lefresne, Gwénola; Latrille, Eric; Irlinger, Françoise; Grimont, Patrick A D

2004-04-01

Many molecular typing methods are difficult to interpret because their repeatability (within-laboratory variance) and reproducibility (between-laboratory variance) have not been thoroughly studied. In the present work, ribotyping of coryneform bacteria was the basis of a study involving within-gel and between-gel repeatability and between-laboratory reproducibility (two laboratories involved). The effect of different technical protocols, different algorithms, and different software for fragment size determination was studied. Analysis of variance (ANOVA) showed, within a laboratory, that there was no significant added variance between gels. However, between-laboratory variance was significantly higher than within-laboratory variance. This may be due to the use of different protocols. An experimental function was calculated to transform the data and make them compatible (i.e., erase the between-laboratory variance). The use of different interpolation algorithms (spline, Schaffer and Sederoff) was a significant source of variation in one laboratory only. The use of either Taxotron (Institut Pasteur) or GelCompar (Applied Maths) was not a significant source of added variation when the same algorithm (spline) was used. However, the use of Bio-Gene (Vilber Lourmat) dramatically increased the error (within laboratory, within gel) in one laboratory, while decreasing the error in the other laboratory; this might be due to automatic normalization attempts. These results were taken into account for building a database and performing automatic pattern identification using Taxotron. Conversion of the data considerably improved the identification of patterns irrespective of the laboratory in which the data were obtained.

Subtle but ubiquitous selection on body size in a natural population of collared flycatchers over 33 years.

PubMed

Björklund, M; Gustafsson, L

2017-07-01

Understanding the magnitude and long-term patterns of selection in natural populations is of importance, for example, when analysing the evolutionary impact of climate change. We estimated univariate and multivariate directional, quadratic and correlational selection on four morphological traits (adult wing, tarsus and tail length, body mass) over a time period of 33 years (≈ 19 000 observations) in a nest-box breeding population of collared flycatchers (Ficedula albicollis). In general, selection was weak in both males and females over the years regardless of fitness measure (fledged young, recruits and survival) with only few cases with statistically significant selection. When data were analysed in a multivariate context and as time series, a number of patterns emerged; there was a consistent, but weak, selection for longer wings in both sexes, selection was stronger on females when the number of fledged young was used as a fitness measure, there were no indications of sexually antagonistic selection, and we found a negative correlation between selection on tarsus and wing length in both sexes but using different fitness measures. Uni- and multivariate selection gradients were correlated only for wing length and mass. Multivariate selection gradient vectors were longer than corresponding vector of univariate gradients and had more constrained direction. Correlational selection had little importance. Overall, the fitness surface was more or less flat with few cases of significant curvature, indicating that the adaptive peak with regard to body size in this species is broader than the phenotypic distribution, which has resulted in weak estimates of selection. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

A multivariate variational objective analysis-assimilation method. Part 2: Case study results with and without satellite data

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.; Kidder, Stanley Q.; Scott, Robert W.

1988-01-01

The variational multivariate assimilation method described in a companion paper by Achtemeier and Ochs is applied to conventional and conventional plus satellite data. Ground-based and space-based meteorological data are weighted according to the respective measurement errors and blended into a data set that is a solution of numerical forms of the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation for a dry atmosphere. The analyses serve first, to evaluate the accuracy of the model, and second to contrast the analyses with and without satellite data. Evaluation criteria measure the extent to which: (1) the assimilated fields satisfy the dynamical constraints, (2) the assimilated fields depart from the observations, and (3) the assimilated fields are judged to be realistic through pattern analysis. The last criterion requires that the signs, magnitudes, and patterns of the hypersensitive vertical velocity and local tendencies of the horizontal velocity components be physically consistent with respect to the larger scale weather systems.

Central Body Fat Distribution Associates with Unfavorable Renal Hemodynamics Independent of Body Mass Index

PubMed Central

Zelle, Dorien M.; Bakker, Stephan J.L.; Navis, Gerjan

2013-01-01

Central distribution of body fat is associated with a higher risk of renal disease, but whether it is the distribution pattern or the overall excess weight that underlies this association is not well understood. Here, we studied the association between waist-to-hip ratio (WHR), which reflects central adiposity, and renal hemodynamics in 315 healthy persons with a mean body mass index (BMI) of 24.9 kg/m2 and a mean 125I-iothalamate GFR of 109 ml/min per 1.73 m2. In multivariate analyses, WHR was associated with lower GFR, lower effective renal plasma flow, and higher filtration fraction, even after adjustment for sex, age, mean arterial pressure, and BMI. Multivariate models produced similar results regardless of whether the hemodynamic measures were indexed to body surface area. Thus, these results suggest that central body fat distribution, independent of BMI, is associated with an unfavorable pattern of renal hemodynamic measures that could underlie the increased renal risk reported in observational studies. PMID:23578944

Multivariate neural biomarkers of emotional states are categorically distinct

PubMed Central

Kragel, Philip A.

2015-01-01

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories. PMID:25813790

Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

USGS Publications Warehouse

Swenson, Sean; Milly, P.C.D.

2006-01-01

Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

MToS: A Tree of Shapes for Multivariate Images.

PubMed

Carlinet, Edwin; Géraud, Thierry

2015-12-01

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

A Quadruped Robot Exhibiting Spontaneous Gait Transitions from Walking to Trotting to Galloping.

PubMed

Owaki, Dai; Ishiguro, Akio

2017-03-21

The manner in which quadrupeds change their locomotive patterns-walking, trotting, and galloping-with changing speed is poorly understood. In this paper, we provide evidence for interlimb coordination during gait transitions using a quadruped robot for which coordination between the legs can be self-organized through a simple "central pattern generator" (CPG) model. We demonstrate spontaneous gait transitions between energy-efficient patterns by changing only the parameter related to speed. Interlimb coordination was achieved with the use of local load sensing only without any preprogrammed patterns. Our model exploits physical communication through the body, suggesting that knowledge of physical communication is required to understand the leg coordination mechanism in legged animals and to establish design principles for legged robots that can reproduce flexible and efficient locomotion.

Development of a Pattern Recognition Methodology for Determining Operationally Optimal Heat Balance Instrumentation Calibration Schedules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurt Beran; John Christenson; Dragos Nica

2002-12-15

The goal of the project is to enable plant operators to detect with high sensitivity and reliability the onset of decalibration drifts in all of the instrumentation used as input to the reactor heat balance calculations. To achieve this objective, the collaborators developed and implemented at DBNPS an extension of the Multivariate State Estimation Technique (MSET) pattern recognition methodology pioneered by ANAL. The extension was implemented during the second phase of the project and fully achieved the project goal.

Characterization of Cryptosporidium parvum by Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry

PubMed Central

Magnuson, Matthew L.; Owens, James H.; Kelty, Catherine A.

2000-01-01

Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) was used to investigate whole and freeze-thawed Cryptosporidium parvum oocysts. Whole oocysts revealed some mass spectral features. Reproducible patterns of spectral markers and increased sensitivity were obtained after the oocysts were lysed with a freeze-thaw procedure. Spectral-marker patterns for C. parvum were distinguishable from those obtained for Cryptosporidium muris. One spectral marker appears specific for the genus, while others appear specific at the species level. Three different C. parvum lots were investigated, and similar spectral markers were observed in each. Disinfection of the oocysts reduced and/or eliminated the patterns of spectral markers. PMID:11055915

Critical aspects of substrate nanopatterning for the ordered growth of GaN nanocolumns

PubMed Central

2011-01-01

Precise and reproducible surface nanopatterning is the key for a successful ordered growth of GaN nanocolumns. In this work, we point out the main technological issues related to the patterning process, mainly surface roughness and cleaning, and mask adhesion to the substrate. We found that each of these factors, process-related, has a dramatic impact on the subsequent selective growth of the columns inside the patterned holes. We compare the performance of e-beam lithography, colloidal lithography, and focused ion beam in the fabrication of hole-patterned masks for ordered columnar growth. These results are applicable to the ordered growth of nanocolumns of different materials. PMID:22168918

Early Motherhood in an Intergenerational Perspective: The Experiences of a British Cohort.

ERIC Educational Resources Information Center

Manlove, Jennifer

1997-01-01

Uses nationally representative, longitudinal data from Great Britain to examine the fertility patterns of daughters (N=2,183) of teen mothers. Explores how early motherhood is reproduced across generations, including an earlier inherited age of menarche, poor family and educational environments, and an early ideal age of childbearing. (RJM)

An electronic nose for reliable measurement and correct classification of beverages.

PubMed

Mamat, Mazlina; Samad, Salina Abdul; Hannan, Mahammad A

2011-01-01

This paper reports the design of an electronic nose (E-nose) prototype for reliable measurement and correct classification of beverages. The prototype was developed and fabricated in the laboratory using commercially available metal oxide gas sensors and a temperature sensor. The repeatability, reproducibility and discriminative ability of the developed E-nose prototype were tested on odors emanating from different beverages such as blackcurrant juice, mango juice and orange juice, respectively. Repeated measurements of three beverages showed very high correlation (r > 0.97) between the same beverages to verify the repeatability. The prototype also produced highly correlated patterns (r > 0.97) in the measurement of beverages using different sensor batches to verify its reproducibility. The E-nose prototype also possessed good discriminative ability whereby it was able to produce different patterns for different beverages, different milk heat treatments (ultra high temperature, pasteurization) and fresh and spoiled milks. The discriminative ability of the E-nose was evaluated using Principal Component Analysis and a Multi Layer Perception Neural Network, with both methods showing good classification results.

A location-based multiple point statistics method: modelling the reservoir with non-stationary characteristics

NASA Astrophysics Data System (ADS)

Yin, Yanshu; Feng, Wenjie

2017-12-01

In this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central position distance function, which alleviates the requirement that the training image and the simulated grids have the same dimension. The weights in every direction of the distance function can be changed to characterize the reservoir heterogeneity in various directions. The local integral replacements of data events, structured random path, distance tolerance and multi-grid strategy are applied to reproduce the sedimentary patterns and obtain a more realistic result. This method is compared with the traditional Snesim method using a synthesized 3-D training image of Poyang Lake and a reservoir model of Shengli Oilfield in China. The results indicate that the new method can reproduce the non-stationary characteristics better than the traditional method and is more suitable for simulation of delta-front deposits. These results show that the new method is a powerful tool for modelling a reservoir with non-stationary characteristics.

Calculating point of origin of blood spatter using laser scanning technology.

PubMed

Hakim, Nashad; Liscio, Eugene

2015-03-01

The point of origin of an impact pattern is important in establishing the chain of events in a bloodletting incident. In this study, the accuracy and reproducibility of the point of origin estimation using the FARO Scene software with the FARO Focus(3D) laser scanner was determined. Five impact patterns were created for each of three combinations of distances from the floor (z) and the front wall (x). Fifteen spatters were created using a custom impact rig, scanned using the laser scanner, photographed using a DSLR camera, and processed using the Scene software. Overall results gave a SD = 3.49 cm (p < 0.0001) in the x-direction, SD = 1.14 cm (p = 0.9291) in the y-direction, and SD = 9.08 cm (p < 0.0115) in the z-direction. The technique performs within literature ranges of accepted accuracy and reproducibility and is comparable to results reported for other virtual stringing software. © 2015 American Academy of Forensic Sciences.

Pseudoracemic amino acid complexes: blind predictions for flexible two-component crystals.

PubMed

Görbitz, Carl Henrik; Dalhus, Bjørn; Day, Graeme M

2010-08-14

Ab initio prediction of the crystal packing in complexes between two flexible molecules is a particularly challenging computational chemistry problem. In this work we present results of single crystal structure determinations as well as theoretical predictions for three 1 ratio 1 complexes between hydrophobic l- and d-amino acids (pseudoracemates), known from previous crystallographic work to form structures with one of two alternative hydrogen bonding arrangements. These are accurately reproduced in the theoretical predictions together with a series of patterns that have never been observed experimentally. In this bewildering forest of potential polymorphs, hydrogen bonding arrangements and molecular conformations, the theoretical predictions succeeded, for all three complexes, in finding the correct hydrogen bonding pattern. For two of the complexes, the calculations also reproduce the exact space group and side chain orientations in the best ranked predicted structure. This includes one complex for which the observed crystal packing clearly contradicted previous experience based on experimental data for a substantial number of related amino acid complexes. The results highlight the significant recent advances that have been made in computational methods for crystal structure prediction.

An Electronic Nose for Reliable Measurement and Correct Classification of Beverages

PubMed Central

Mamat, Mazlina; Samad, Salina Abdul; Hannan, Mahammad A.

2011-01-01

This paper reports the design of an electronic nose (E-nose) prototype for reliable measurement and correct classification of beverages. The prototype was developed and fabricated in the laboratory using commercially available metal oxide gas sensors and a temperature sensor. The repeatability, reproducibility and discriminative ability of the developed E-nose prototype were tested on odors emanating from different beverages such as blackcurrant juice, mango juice and orange juice, respectively. Repeated measurements of three beverages showed very high correlation (r > 0.97) between the same beverages to verify the repeatability. The prototype also produced highly correlated patterns (r > 0.97) in the measurement of beverages using different sensor batches to verify its reproducibility. The E-nose prototype also possessed good discriminative ability whereby it was able to produce different patterns for different beverages, different milk heat treatments (ultra high temperature, pasteurization) and fresh and spoiled milks. The discriminative ability of the E-nose was evaluated using Principal Component Analysis and a Multi Layer Perception Neural Network, with both methods showing good classification results. PMID:22163964

Self-organized phenomena of pedestrian counterflow through a wide bottleneck in a channel

NASA Astrophysics Data System (ADS)

Dong, Li-Yun; Lan, Dong-Kai; Li, Xiang

2016-09-01

The pedestrian counterflow through a bottleneck in a channel shows a variety of flow patterns due to self-organization. In order to reveal the underlying mechanism, a cellular automaton model was proposed by incorporating the floor field and the view field which reflects the global information of the studied area and local interactions with others. The presented model can well reproduce typical collective behaviors, such as lane formation. Numerical simulations were performed in the case of a wide bottleneck and typical flow patterns at different density ranges were identified as rarefied flow, laminar flow, interrupted bidirectional flow, oscillatory flow, intermittent flow, and choked flow. The effects of several parameters, such as the size of view field and the width of opening, on the bottleneck flow are also analyzed in detail. The view field plays a vital role in reproducing self-organized phenomena of pedestrian. Numerical results showed that the presented model can capture key characteristics of bottleneck flows. Project supported by the National Basic Research Program of China (Grant No. 2012CB725404) and the National Natural Science Foundation of China (Grant Nos. 11172164 and 11572184).

Asymmetric Shock Wave Generation in a Microwave Rocket Using a Magnetic Field

NASA Astrophysics Data System (ADS)

Takahashi, Masayuki

2017-10-01

A plasma pattern is reproduced by coupling simulations between a particle-in- cell with Monte Carlo collisions model and a finite-difference time-domain simulation for an electromagnetic wave propagation when an external magnetic field is applied to the breakdown volume inside a microwave-rocket nozzle. The propagation speed and energy-absorption rate of the plasma are estimated based on the breakdown simulation, and these are utilized to reproduce shock wave propagation, which provides impulsive thrust for the microwave rocket. The shock wave propagation is numerically reproduced by solving the compressible Euler equation with an energy source of the microwave heating. The shock wave is asymmetrically generated inside the nozzle when the electron cyclotron resonance region has a lateral offset, which generates lateral and angular impulses for postural control of the vehicle. It is possible to develop an integrated device to maintain beaming ight of the microwave rocket, achieving both axial thrust improvement and postural control, by controlling the spatial distribution of the external magnetic field.

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Dietary Patterns and Risk of Death and Progression to ESRD in Individuals With CKD: A Cohort Study

PubMed Central

Gutiérrez, Orlando M.; Muntner, Paul; Rizk, Dana V.; McClellan, William M.; Warnock, David G.; Newby, P.K.; Judd, Suzanne E.

2014-01-01

Background Nutrition is strongly linked with health outcomes in chronic kidney disease (CKD). However, few studies have examined relationships between dietary patterns and health outcomes in persons with CKD. Study Design Observational cohort study. Setting & Participants 3,972 participants with CKD (defined as an estimated glomerular filtration rate < 60 ml/min/1.73 m2 or an albumin-creatinine ratio ≥30 mg/g at baseline) from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a prospective cohort study of 30,239 black and white adults at least 45 years of age. Predictors Five empirically derived dietary patterns identified via factor analysis: “Convenience” (Chinese and Mexican foods, pizza, other mixed dishes), “Plant-Based” (fruits, vegetables), “Sweets/Fats” (sugary foods), “Southern” (fried foods, organ meats, sweetened beverages), and “Alcohol/Salads” (alcohol, green-leafy vegetables, salad dressing). Outcomes All-cause mortality and end-stage renal disease (ESRD). Results A total of 816 deaths and 141 ESRD events were observed over approximately 6 years of follow-up. There were no statistically significant associations of Convenience, Sweets/Fats or Alcohol/Salads pattern scores with all-cause mortality after multivariable adjustment. In Cox regression models adjusted for sociodemographic factors, energy intake, co-morbidities, and baseline kidney function, higher Plant-Based pattern scores (indicating greater consistency with the pattern) were associated with lower risk of mortality (HR comparing fourth to first quartile, 0.77; 95%CI, 0.61–0.97) whereas higher Southern pattern scores were associated with greater risk of mortality (HR comparing fourth to first quartile, 1.51; 95%CI, 1.19–1.92). There were no associations of dietary patterns with incident ESRD in multivariable-adjusted models. Limitations Missing dietary pattern data, potential residual confounding from lifestyle factors. Conclusions A Southern dietary pattern rich in processed and fried foods was independently associated with mortality in persons with CKD. In contrast, a diet rich in fruits and vegetables appeared to be protective. PMID:24679894

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Weather patterns as a downscaling tool - evaluating their skill in stratifying local climate variables

NASA Astrophysics Data System (ADS)

Murawski, Aline; Bürger, Gerd; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

The use of a weather pattern based approach for downscaling of coarse, gridded atmospheric data, as usually obtained from the output of general circulation models (GCM), allows for investigating the impact of anthropogenic greenhouse gas emissions on fluxes and state variables of the hydrological cycle such as e.g. on runoff in large river catchments. Here we aim at attributing changes in high flows in the Rhine catchment to anthropogenic climate change. Therefore we run an objective classification scheme (simulated annealing and diversified randomisation - SANDRA, available from the cost733 classification software) on ERA20C reanalyses data and apply the established classification to GCMs from the CMIP5 project. After deriving weather pattern time series from GCM runs using forcing from all greenhouse gases (All-Hist) and using natural greenhouse gas forcing only (Nat-Hist), a weather generator will be employed to obtain climate data time series for the hydrological model. The parameters of the weather pattern classification (i.e. spatial extent, number of patterns, classification variables) need to be selected in a way that allows for good stratification of the meteorological variables that are of interest for the hydrological modelling. We evaluate the skill of the classification in stratifying meteorological data using a multi-variable approach. This allows for estimating the stratification skill for all meteorological variables together, not separately as usually done in existing similar work. The advantage of the multi-variable approach is to properly account for situations where e.g. two patterns are associated with similar mean daily temperature, but one pattern is dry while the other one is related to considerable amounts of precipitation. Thus, the separation of these two patterns would not be justified when considering temperature only, but is perfectly reasonable when accounting for precipitation as well. Besides that, the weather patterns derived from reanalyses data should be well represented in the All-Hist GCM runs in terms of e.g. frequency, seasonality, and persistence. In this contribution we show how to select the most appropriate weather pattern classification and how the classes derived from it are reflected in the GCMs.

Modeling digits. Digit patterning is controlled by a Bmp-Sox9-Wnt Turing network modulated by morphogen gradients.

PubMed

Raspopovic, J; Marcon, L; Russo, L; Sharpe, J

2014-08-01

During limb development, digits emerge from the undifferentiated mesenchymal tissue that constitutes the limb bud. It has been proposed that this process is controlled by a self-organizing Turing mechanism, whereby diffusible molecules interact to produce a periodic pattern of digital and interdigital fates. However, the identities of the molecules remain unknown. By combining experiments and modeling, we reveal evidence that a Turing network implemented by Bmp, Sox9, and Wnt drives digit specification. We develop a realistic two-dimensional simulation of digit patterning and show that this network, when modulated by morphogen gradients, recapitulates the expression patterns of Sox9 in the wild type and in perturbation experiments. Our systems biology approach reveals how a combination of growth, morphogen gradients, and a self-organizing Turing network can achieve robust and reproducible pattern formation. Copyright © 2014, American Association for the Advancement of Science.

A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins

NASA Astrophysics Data System (ADS)

Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R.

2011-09-01

Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells.

A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins

PubMed Central

Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R.

2011-01-01

Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells. PMID:21974603

A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins.

PubMed

Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R

2011-09-01

Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells. © 2011 American Institute of Physics

Mapping the Diversity among Runaways: A Descriptive Multivariate Analysis of Selected Social Psychological Background Conditions.

ERIC Educational Resources Information Center

Brennan, Tim

1980-01-01

A review of prior classification systems of runaways is followed by a descriptive taxonomy of runaways developed using cluster-analytic methods. The empirical types illustrate patterns of weakness in bonds between runaways and families, schools, or peer relationships. (Author)

Alcohol Use, Eating Patterns, and Weight Behaviors in a University Population

ERIC Educational Resources Information Center

Nelson, Melissa C.; Lust, Katherine; Story, Mary; Ehlinger, Ed

2009-01-01

Objective: To explore associations between alcohol, alcohol-related eating, and weight-related health indicators. Methods: Cross-sectional, multivariate regression of weight behaviors, binge drinking, and alcohol-related eating, using self-reported student survey data (n = 3206 undergraduates/graduates). Results: Binge drinking was associated with…

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

PubMed

Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B

2017-11-10

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

Modeling and Simulation of Upset-Inducing Disturbances for Digital Systems in an Electromagnetic Reverberation Chamber

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2014-01-01

This report describes a modeling and simulation approach for disturbance patterns representative of the environment experienced by a digital system in an electromagnetic reverberation chamber. The disturbance is modeled by a multi-variate statistical distribution based on empirical observations. Extended versions of the Rejection Samping and Inverse Transform Sampling techniques are developed to generate multi-variate random samples of the disturbance. The results show that Inverse Transform Sampling returns samples with higher fidelity relative to the empirical distribution. This work is part of an ongoing effort to develop a resilience assessment methodology for complex safety-critical distributed systems.

Cervical vertebrae maturation method morphologic criteria: poor reproducibility.

PubMed

Nestman, Trenton S; Marshall, Steven D; Qian, Fang; Holton, Nathan; Franciscus, Robert G; Southard, Thomas E

2011-08-01

The cervical vertebrae maturation (CVM) method has been advocated as a predictor of peak mandibular growth. A careful review of the literature showed potential methodologic errors that might influence the high reported reproducibility of the CVM method, and we recently established that the reproducibility of the CVM method was poor when these potential errors were eliminated. The purpose of this study was to further investigate the reproducibility of the individual vertebral patterns. In other words, the purpose was to determine which of the individual CVM vertebral patterns could be classified reliably and which could not. Ten practicing orthodontists, trained in the CVM method, evaluated the morphology of cervical vertebrae C2 through C4 from 30 cephalometric radiographs using questions based on the CVM method. The Fleiss kappa statistic was used to assess interobserver agreement when evaluating each cervical vertebrae morphology question for each subject. The Kendall coefficient of concordance was used to assess the level of interobserver agreement when determining a "derived CVM stage" for each subject. Interobserver agreement was high for assessment of the lower borders of C2, C3, and C4 that were either flat or curved in the CVM method, but interobserver agreement was low for assessment of the vertebral bodies of C3 and C4 when they were either trapezoidal, rectangular horizontal, square, or rectangular vertical; this led to the overall poor reproducibility of the CVM method. These findings were reflected in the Fleiss kappa statistic. Furthermore, nearly 30% of the time, individual morphologic criteria could not be combined to generate a final CVM stage because of incompatible responses to the 5 questions. Intraobserver agreement in this study was only 62%, on average, when the inconclusive stagings were excluded as disagreements. Intraobserver agreement was worse (44%) when the inconclusive stagings were included as disagreements. For the group of subjects that could be assigned a CVM stage, the level of interobserver agreement as measured by the Kendall coefficient of concordance was only 0.45, indicating moderate agreement. The weakness of the CVM method results, in part, from difficulty in classifying the vertebral bodies of C3 and C4 as trapezoidal, rectangular horizontal, square, or rectangular vertical. This led to the overall poor reproducibility of the CVM method and our inability to support its use as a strict clinical guideline for the timing of orthodontic treatment. Copyright © 2011 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

PubMed Central

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M.; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V.; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L.; Bilello, Michel; O'Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). Methods One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Results Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. Conclusions By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood–brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. PMID:26188015

Increasing plasma free fatty acids in healthy subjects induces aortic distensibility changes seen in obesity.

PubMed

Rider, Oliver J; Holloway, Cameron J; Emmanuel, Yaso; Bloch, Edward; Clarke, Kieran; Neubauer, Stefan

2012-05-01

Elevated free fatty acid (FFA) levels are known to impair aortic elastic function. In obesity, FFA levels are elevated and aortic distensibility (AD) reduced in a pattern that predominantly affects the distal aorta. Despite this, the role of FFAs in obesity-related aortic stiffness remains unclear. Using vascular MRI, we aimed to determine if (1) FFA level correlated with AD in obesity; and (2) whether elevating FFA acutely and subacutely in normal-weight subjects reproduced the distal pattern of AD change in obesity. To do this, regional AD was recorded in 35 normal-weight and 70 obese subjects and then correlated with FFA levels. When compared with normal weight, obesity was associated with reduced AD in a pattern predominantly affecting the distal aorta (ascending aorta by -22%, proximal descending aorta by -25%, and abdominal aorta by -35%; P<0.001). After controlling for age, blood pressure, and body mass index, FFA levels remained negatively correlated with abdominal AD (r=-0.43, P<0.01). In 2 further normal-weight groups, AD was recorded before and after elevation of FFA levels with intralipid infusion (by +535%, n=9) and a 5-day high-fat, low-carbohydrate diet (by +48%, n=14). Both intralipid infusion and a low-carbohydrate diet resulted in reduced abdominal AD (infusion -22%, diet -28%; both P<0.05), reproducing the distal pattern AD reduction seen in obesity. These findings suggest that elevated FFA impair AD in obesity and provide a potential therapeutic target to improve aortic elastic function in obesity.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

Complex patterns of multivariate selection on the ejaculate of a broadcast spawning marine invertebrate.

PubMed

Fitzpatrick, John L; Simmons, Leigh W; Evans, Jonathan P

2012-08-01

Assessing how selection operates on several, potentially interacting, components of the ejaculate is a challenging endeavor. Ejaculates can be subject to natural and/or sexual selection, which can impose both linear (directional) and nonlinear (stabilizing, disruptive, and correlational) selection on different ejaculate components. Most previous studies have examined linear selection of ejaculate components and, consequently, we know very little about patterns of nonlinear selection on the ejaculate. Even less is known about how selection acts on the ejaculate as a functionally integrated unit, despite evidence of covariance among ejaculate components. Here, we assess how selection acts on multiple ejaculate components simultaneously in the broadcast spawning sessile invertebrate Mytilus galloprovincialis using the statistical tools of multivariate selection analyses. Our analyses of relative fertilization rates revealed complex patterns of selection on sperm velocity, motility, and morphology. Interestingly, the most successful ejaculates were made up of slower swimming sperm with relatively low percentages of motile cells, and sperm with smaller head volumes that swam in highly pronounced curved swimming trajectories. These results are consistent with an emerging body of literature on fertilization kinetics in broadcast spawners, and shed light on the fundamental nature of selection acting on the ejaculate as a functionally integrated unit. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

Pattern of spread and prognosis in lower limb-onset ALS

PubMed Central

TURNER, MARTIN R.; BROCKINGTON, ALICE; SCABER, JAKUB; HOLLINGER, HANNAH; MARSDEN, RACHAEL; SHAW, PAMELA J.; TALBOT, KEVIN

2011-01-01

Our objective was to establish the pattern of spread in lower limb-onset ALS (contra- versus ipsi-lateral) and its contribution to prognosis within a multivariate model. Pattern of spread was established in 109 sporadic ALS patients with lower limb-onset, prospectively recorded in Oxford and Sheffield tertiary clinics from 2001 to 2008. Survival analysis was by univariate Kaplan-Meier log-rank and multivariate Cox proportional hazards. Variables studied were time to next limb progression, site of next progression, age at symptom onset, gender, diagnostic latency and use of riluzole. Initial progression was either to the contralateral leg (76%) or ipsilateral arm (24%). Factors independently affecting survival were time to next limb progression, age at symptom onset, and diagnostic latency. Time to progression as a prognostic factor was independent of initial direction of spread. In a regression analysis of the deceased, overall survival from symptom onset approximated to two years plus the time interval for initial spread. In conclusion, rate of progression in lower limb-onset ALS is not influenced by whether initial spread is to the contralateral limb or ipsilateral arm. The time interval to this initial spread is a powerful factor in predicting overall survival, and could be used to facilitate decision-making and effective care planning. PMID:20001488

Developing quantitative criteria to evaluate AOGCMs for application to regional climate assessments

NASA Astrophysics Data System (ADS)

Hayhoe, K.; Wake, C.; Bradbury, J.; Degaetano, A.; Hertel, A.

2006-12-01

Climate projections are the foundation for regional assessments of potential climate impacts. However, the soundness of regional assessments depends on the ability of global climate models to reproduce key processes responsible for regional climate trends. Here, we develop a systematic method to compare observed climate with historical atmosphere-ocean general circulation model (AOGCM) simulations, to assess the degree to which AOGCMs are able to reproduce regional circulation patterns. Applying this methodology to the U.S. Northeast (NE), we find that nearly all AOGCMs simulate a reasonable winter NAO pattern and seasonal positions of the Jet Stream and the East Coast Trough. However, not all models capture observed correlations between these circulation patterns and seasonal climate anomalies in the NE. Using only those AOGCMs that meet the criteria in each of these areas, we then develop projections of future climate change in the NE. The primary changes projected to occur over the next century - slightly greater temperature increases in summer than winter, and increases in winter precipitation - are consistent with projected trends in regional climate processes and are relatively independent of model or scale. These suggest confidence in the direction and potential range of the most notable regional climate trends, with the absolute magnitude of change depending on both the sensitivity of the climate system to human forcing as well as on human emissions over coming decades.

A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform

PubMed Central

Rizvi, Imran; Moon, Sangjun; Hasan, Tayyaba; Demirci, Utkan

2013-01-01

In vitro 3D cancer models that provide a more accurate representation of disease in vivo are urgently needed to improve our understanding of cancer pathology and to develop better cancer therapies. However, development of 3D models that are based on manual ejection of cells from micropipettes suffer from inherent limitations such as poor control over cell density, limited repeatability, low throughput, and, in the case of coculture models, lack of reproducible control over spatial distance between cell types (e.g., cancer and stromal cells). In this study, we build on a recently introduced 3D model in which human ovarian cancer (OVCAR-5) cells overlaid on Matrigel™ spontaneously form multicellular acini. We introduce a high-throughput automated cell printing system to bioprint a 3D coculture model using cancer cells and normal fibroblasts micropatterned on Matrigel™. Two cell types were patterned within a spatially controlled microenvironment (e.g., cell density, cell-cell distance) in a high-throughput and reproducible manner; both cell types remained viable during printing and continued to proliferate following patterning. This approach enables the miniaturization of an established macro-scale 3D culture model and would allow systematic investigation into the multiple unknown regulatory feedback mechanisms between tumor and stromal cells and provide a tool for high-throughput drug screening. PMID:21298805

Increased Prognostic Value of Query Amyloid Late Enhancement Score in Light-Chain Cardiac Amyloidosis.

PubMed

Wan, Ke; Sun, Jiayu; Han, Yuchi; Liu, Hong; Yang, Dan; Li, Weihao; Wang, Jie; Cheng, Wei; Zhang, Qing; Zeng, Zhi; Chen, Yucheng

2018-02-23

Late gadolinium enhancement (LGE) pattern is a powerful imaging biomarker for prognosis of cardiac amyloidosis. It is unknown if the query amyloid late enhancement (QALE) score in light-chain (AL) amyloidosis could provide increased prognostic value compared with LGE pattern.Methods and Results:Seventy-eight consecutive patients with AL amyloidosis underwent contrast-enhanced cardiovascular magnetic resonance imaging. Patients with cardiac involvement were grouped by LGE pattern and analyzed using QALE score. Receiver operating characteristic curve was used to identify the optimal cut-off for QALE score in predicting all-cause mortality. Survival of these patients was analyzed with the Kaplan-Meier method and multivariate Cox regression. During a median follow-up of 34 months, 53 of 78 patients died. The optimal cut-off for QALE score to predict mortality at 12-month follow-up was 9.0. On multivariate Cox analysis, QALE score ≥9 (HR, 5.997; 95% CI: 2.665-13.497; P<0.001) and log N-terminal pro-brain natriuretic peptide (HR, 1.525; 95% CI: 1.112-2.092; P=0.009) were the only 2 independent predictors of all-cause mortality. On Kaplan-Meier analysis, patients with subendocardial LGE can be further risk stratified using QALE score ≥9. The QALE scoring system provides powerful independent prognostic value in AL cardiac amyloidosis. QALE score ≥9 has added value to differentiate prognosis in AL amyloidosis patients with a subendocardial LGE pattern.

Diet and proinflammatory cytokine levels in head and neck squamous cell carcinoma

PubMed Central

Arthur, Anna E.; Peterson, Karen E.; Shen, Jincheng; Djuric, Zora; Taylor, Jeremy M.G.; Hebert, James R.; Duffy, Sonia A.; Peterson, Lisa A.; Bellile, Emily L.; Whitfield, Joel R.; Chepeha, Douglas B.; Schipper, Matthew J.; Wolf, Gregory T.; Rozek, Laura S.

2014-01-01

Background Proinflammatory cytokine levels may be associated with cancer stage, recurrence, and survival. A study was undertaken to determine if cytokine levels were associated with dietary patterns and fat-soluble micronutrients in previously untreated head and neck squamous cell carcinoma (HNSCC) patients. Methods This was a cross-sectional study of 160 newly diagnosed HNSCC patients who completed pretreatment food frequency questionnaires (FFQ) and health surveys. Dietary patterns were derived from FFQs using principal component analysis. Pretreatment serum levels of the proinflammatory cytokines IL-6, TNF-α, and IFN-γ were measured by ELISA and serum carotenoid and tocopherol levels by HPLC. Multivariable ordinal logistic regression models examined associations between cytokines and quartiles of reported and serum dietary variables. Results Three dietary patterns emerged: whole foods, Western, and convenience foods. In multivariable analyses, higher whole foods pattern scores were significantly associated with lower levels of IL-6, TNF-α, and IFN-γ (P = <0.001, P = 0.008, and P = 0.03, respectively). Significant inverse associations were reported between IL-6, TNF-α, and IFN-γ levels and quartiles of total reported carotenoid intake (P = 0.006, P = 0.04, and P = 0.04, respectively). There was an inverse association between IFN-γ levels and serum α-tocopherol levels (P = 0.03). Conclusions Consuming a pretreatment diet rich in vegetables, fruit, fish, poultry and whole grains may be associated with lower proinflammatory cytokine levels in patients with HNSCC. PMID:24830761

Interior renovation of a general practitioner office leads to a perceptual bias on patient experience for over one year.

PubMed

Gauthey, Jérôme; Tièche, Raphaël; Streit, Sven

2018-01-01

Measuring patient experience is key when assessing quality of care but can be biased: A perceptual bias occurs when renovations of the interior design of a general practitioner (GP) office improves how patients assessed quality of care. The aim was to assess the length of perceptual bias and if it could be reproduced after a second renovation. A GP office with 2 GPs in Switzerland was renovated twice within 3 years. We assessed patient experience at baseline, 2 months and 14 months after the first and 3 months after the second renovation. Each time, we invited a sample of 180 consecutive patients that anonymously graded patient experience in 4 domains: appearance of the office; qualities of medical assistants and GPs; and general satisfaction. We compared crude mean scores per domain from baseline until follow-up. In a multivariate model, we adjusted for patient's age, gender and for how long patients had been their GP. At baseline, patients aged 60.9 (17.7) years, 52% females. After the first renovation, we found a regression to the baseline level of patient experience after 14 months except for appearance of the office (p<0.001). After the second renovation, patient experience improved again in appearance of the office (p = 0.008), qualities of the GP (p = 0.008), and general satisfaction (p = 0.014). Qualities of the medical assistant showed a slight improvement (p = 0.068). Results were unchanged in the multivariate model. Interior renovation of a GP office probably causes a perceptual bias for >1 year that improves how patients rate quality of care. This bias could be reproduced after a second renovation strengthening a possible causal relationship. These findings imply to appropriately time measurement of patient experience to at least one year after interior renovation of GP practices to avoid environmental changes influences the estimates when measuring patient experience.

Interior renovation of a general practitioner office leads to a perceptual bias on patient experience for over one year

PubMed Central

2018-01-01

Introduction Measuring patient experience is key when assessing quality of care but can be biased: A perceptual bias occurs when renovations of the interior design of a general practitioner (GP) office improves how patients assessed quality of care. The aim was to assess the length of perceptual bias and if it could be reproduced after a second renovation. Methods A GP office with 2 GPs in Switzerland was renovated twice within 3 years. We assessed patient experience at baseline, 2 months and 14 months after the first and 3 months after the second renovation. Each time, we invited a sample of 180 consecutive patients that anonymously graded patient experience in 4 domains: appearance of the office; qualities of medical assistants and GPs; and general satisfaction. We compared crude mean scores per domain from baseline until follow-up. In a multivariate model, we adjusted for patient’s age, gender and for how long patients had been their GP. Results At baseline, patients aged 60.9 (17.7) years, 52% females. After the first renovation, we found a regression to the baseline level of patient experience after 14 months except for appearance of the office (p<0.001). After the second renovation, patient experience improved again in appearance of the office (p = 0.008), qualities of the GP (p = 0.008), and general satisfaction (p = 0.014). Qualities of the medical assistant showed a slight improvement (p = 0.068). Results were unchanged in the multivariate model. Conclusions Interior renovation of a GP office probably causes a perceptual bias for >1 year that improves how patients rate quality of care. This bias could be reproduced after a second renovation strengthening a possible causal relationship. These findings imply to appropriately time measurement of patient experience to at least one year after interior renovation of GP practices to avoid environmental changes influences the estimates when measuring patient experience. PMID:29462196

White matter hyperintensity patterns in cerebral amyloid angiopathy and hypertensive arteriopathy.

PubMed

Charidimou, Andreas; Boulouis, Gregoire; Haley, Kellen; Auriel, Eitan; van Etten, Ellis S; Fotiadis, Panagiotis; Reijmer, Yael; Ayres, Alison; Vashkevich, Anastasia; Dipucchio, Zora Y; Schwab, Kristin M; Martinez-Ramirez, Sergi; Rosand, Jonathan; Viswanathan, Anand; Greenberg, Steven M; Gurol, M Edip

2016-02-09

To identify different white matter hyperintensity (WMH) patterns between 2 hemorrhage-prone cerebral small vessel diseases (SVD): cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy (HA). Consecutive patients with SVD-related intracerebral hemorrhage (ICH) from a single-center prospective cohort were analyzed. Four predefined subcortical WMH patterns were compared between the CAA and HA groups. These WMH patterns were (1) multiple subcortical spots; (2) peri-basal ganglia (BG); (3) large posterior subcortical patches; and (4) anterior subcortical patches. Their associations with other imaging (cerebral microbleeds [CMBs], enlarged perivascular spaces [EPVS]) and clinical markers of SVD were investigated using multivariable logistic regression. The cohort included 319 patients with CAA and 137 patients with HA. Multiple subcortical spots prevalence was higher in the CAA compared to the HA group (29.8% vs 16.8%; p = 0.004). Peri-BG WMH pattern was more common in the HA- vs the CAA-ICH group (19% vs 7.8%; p = 0.001). In multivariable logistic regression, presence of multiple subcortical spots was associated with lobar CMBs (odds ratio [OR] 1.23; 95% confidence interval [CI] 1.01-1.50, p = 0.039) and high degree of centrum semiovale EPVS (OR 2.43; 95% CI 1.56-3.80, p < 0.0001). By contrast, age (OR 1.05; 95% CI 1.02-1.09, p = 0.002), deep CMBs (OR 2.46; 95% CI 1.44-4.20, p = 0.001), total WMH volume (OR 1.02; 95% CI 1.01-1.04, p = 0.002), and high BG EPVS degree (OR 8.81; 95% CI 3.37-23.02, p < 0.0001) were predictors of peri-BG WMH pattern. Different patterns of subcortical leukoaraiosis visually identified on MRI might provide insights into the dominant underlying microangiopathy type as well as mechanisms of tissue injury in patients with ICH. © 2016 American Academy of Neurology.

BLURRING OF BIOGEOGRAPHIC BOUNDARIES: A MULTIVARIATE ANALYSIS OF THE REGIONAL PATTERNS OF NATIVE AND NONINDIGENOUS SPECIES ASSEMBLAGES IN PACIFIC COAST ESTUARIES

EPA Science Inventory

Many, if not most, invaders have wide physiological tolerance limits and generalist habitat requirements. Consequently as a group nonindigenous species should have wider geographic distributions compared to native fauna. In turn, these broader distributions of nonindigenous speci...

Multivariate inference of pathway activity in host immunity and response to therapeutics

PubMed Central

Goel, Gautam; Conway, Kara L.; Jaeger, Martin; Netea, Mihai G.; Xavier, Ramnik J.

2014-01-01

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method. PMID:25147207

Multivariate factorial analysis to design a robust batch leaching test to assess the volcanic ash geochemical hazard.

PubMed

Ruggieri, Flavia; Gil, Raúl A; Fernandez-Turiel, Jose-Luis; Saavedra, Julio; Gimeno, Domingo; Lobo, Agustin; Martinez, Luis D; Rodriguez-Gonzalez, Alejandro

2012-04-30

A method to obtain robust information on short term leaching behaviour of volcanic ashes has been developed independently on the sample age. A mixed factorial design (MFD) was employed as a multivariate strategy for the evaluation of the effects of selected control factors and their interactions (amount of sample (A), contact time (B), and liquid to solid ratio or L/S (C)) on the leaching process of selected metals (Na, K, Mg, Ca, Si, Al, V, Mn, Fe, and Co) and anions (Cl(-) and SO(4)(2-)). Box plots of the data acquired were used to evaluate the reproducibility achieved at different experimental conditions. Both the amount of sample (A) and leaching time (B) had a significant effect on the element stripping whereas the L/S ratio influenced only few elements. The lowest dispersion values have been observed when 1.0 g was leached with an L/S ratio equal to 10, shaking during 4 h. The entire method is completed within few hours, and it is simple, feasible and reliable in laboratory conditions. Copyright © 2012 Elsevier B.V. All rights reserved.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

PubMed

Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

2011-11-15

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility

PubMed Central

Goñi, Joaquín; Sporns, Olaf; Cheng, Hu; Aznárez-Sanado, Maite; Wang, Yang; Josa, Santiago; Arrondo, Gonzalo; Mathews, Vincent P; Hummer, Tom A; Kronenberger, William G; Avena-Koenigsberger, Andrea; Saykin, Andrew J.; Pastor, María A.

2013-01-01

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the grey matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising, 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9–0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies. PMID:23831414

Multivariate Pattern Analysis Reveals Category-Related Organization of Semantic Representations in Anterior Temporal Cortex.

PubMed

Malone, Patrick S; Glezer, Laurie S; Kim, Judy; Jiang, Xiong; Riesenhuber, Maximilian

2016-09-28

The neural substrates of semantic representation have been the subject of much controversy. The study of semantic representations is complicated by difficulty in disentangling perceptual and semantic influences on neural activity, as well as in identifying stimulus-driven, "bottom-up" semantic selectivity unconfounded by top-down task-related modulations. To address these challenges, we trained human subjects to associate pseudowords (TPWs) with various animal and tool categories. To decode semantic representations of these TPWs, we used multivariate pattern classification of fMRI data acquired while subjects performed a semantic oddball detection task. Crucially, the classifier was trained and tested on disjoint sets of TPWs, so that the classifier had to use the semantic information from the training set to correctly classify the test set. Animal and tool TPWs were successfully decoded based on fMRI activity in spatially distinct subregions of the left medial anterior temporal lobe (LATL). In addition, tools (but not animals) were successfully decoded from activity in the left inferior parietal lobule. The tool-selective LATL subregion showed greater functional connectivity with left inferior parietal lobule and ventral premotor cortex, indicating that each LATL subregion exhibits distinct patterns of connectivity. Our findings demonstrate category-selective organization of semantic representations in LATL into spatially distinct subregions, continuing the lateral-medial segregation of activation in posterior temporal cortex previously observed in response to images of animals and tools, respectively. Together, our results provide evidence for segregation of processing hierarchies for different classes of objects and the existence of multiple, category-specific semantic networks in the brain. The location and specificity of semantic representations in the brain are still widely debated. We trained human participants to associate specific pseudowords with various animal and tool categories, and used multivariate pattern classification of fMRI data to decode the semantic representations of the trained pseudowords. We found that: (1) animal and tool information was organized in category-selective subregions of medial left anterior temporal lobe (LATL); (2) tools, but not animals, were encoded in left inferior parietal lobe; and (3) LATL subregions exhibited distinct patterns of functional connectivity with category-related regions across cortex. Our findings suggest that semantic knowledge in LATL is organized in category-related subregions, providing evidence for the existence of multiple, category-specific semantic representations in the brain. Copyright © 2016 the authors 0270-6474/16/3610089-08$15.00/0.

Brazilian dietary patterns and the dietary approaches to stop hypertension (DASH) diet-relationship with metabolic syndrome and newly diagnosed diabetes in the ELSA-Brasil study.

PubMed

Drehmer, Michele; Odegaard, Andrew O; Schmidt, Maria Inês; Duncan, Bruce B; Cardoso, Letícia de Oliveira; Matos, Sheila M Alvim; Molina, Maria Del Carmen B; Barreto, Sandhi M; Pereira, Mark A

2017-01-01

Studies evaluating dietary patterns, including the DASH diet, and their relationship with the metabolic syndrome and diabetes may help to understand the role of dairy products (low fat or full fat) in these conditions. Our aim is to identify dietary patterns in Brazilian adults and compare them with the (DASH) diet quality score in terms of their associations with metabolic syndrome and newly diagnosed diabetes in the Brazilian Longitudinal Study of Adult Health-the ELSA-Brasil study. The ELSA-Brasil is a multicenter cohort study comprising 15,105 civil servants, aged 35-74 years at baseline (2008-2010). Standardized interviews and exams were carried out, including an OGTT. We analyzed baseline data for 10,010 subjects. Dietary patterns were derived by principal component analysis. Multivariable logistic regression investigated associations of dietary patterns with metabolic syndrome and newly diagnosed diabetes and multivariable linear regression with components of metabolic syndrome. After controlling for potential confounders, we observed that greater adherence to the Common Brazilian meal pattern (white rice, beans, beer, processed and fresh meats), was associated with higher frequencies of newly diagnosed diabetes, metabolic syndrome and all of its components, except HDL-C. Participants with greater intake of a Common Brazilian fast foods/full fat dairy/milk based desserts pattern presented less newly diagnosed diabetes. An inverse association was also seen between the DASH Diet pattern and the metabolic syndrome, blood pressure and waist circumference. Diet, light foods and beverages/low fat dairy pattern was associated with more prevalence of both outcomes, and higher fasting glucose, HDL-C, waist circumference (among men) and lower blood pressure. Vegetables/fruit dietary pattern did not protect against metabolic syndrome and newly diagnosed diabetes but was associated with lower waist circumference. The inverse associations found for the dietary pattern characterizing Brazilian fast foods and desserts, typically containing dairy products, with newly diagnosed diabetes, and for the DASH diet with metabolic syndrome, support previously demonstrated beneficial effects of dairy products in metabolism. The positive association with metabolic syndrome and newly diagnosed diabetes found for the pattern characterizing a typical Brazilian meal deserves further investigation, particularly since it is frequently accompanied by processed meat. Trial registration NCT02320461. Registered 18 December 2014.

Transfer molding processes for nanoscale patterning of poly-L-lactic acid (PLLA) films

NASA Astrophysics Data System (ADS)

Dhakal, Rabin; Peer, Akshit; Biswas, Rana; Kim, Jaeyoun

2016-03-01

Nanoscale patterned structures composed of biomaterials exhibit great potential for the fabrication of functional biostructures. In this paper, we report cost-effective, rapid, and highly reproducible soft lithographic transfer-molding techniques for creating periodic micro- and nano-scale textures on poly (L-lactic acid) (PLLA) surface. These artificial textures can increase the overall surface area and change the release dynamics of the therapeutic agents coated on it. Specifically, we use the double replication technique in which the master pattern is first transferred to the PDMS mold and the pattern on PDMS is then transferred to the PLLA films through drop-casting as well as nano-imprinting. The ensuing comparison studies reveal that the drop-cast PLLA allows pattern transfer at higher levels of fidelity, enabling the realization of nano-hole and nano-cone arrays with pitch down to ~700 nm. The nano-patterned PLLA film was then coated with rapamycin to make it drug-eluting.

High-throughput manufacturing of size-tuned liposomes by a new microfluidics method using enhanced statistical tools for characterization.

PubMed

Kastner, Elisabeth; Kaur, Randip; Lowry, Deborah; Moghaddam, Behfar; Wilkinson, Alexander; Perrie, Yvonne

2014-12-30

Microfluidics has recently emerged as a new method of manufacturing liposomes, which allows for reproducible mixing in miliseconds on the nanoliter scale. Here we investigate microfluidics-based manufacturing of liposomes. The aim of these studies was to assess the parameters in a microfluidic process by varying the total flow rate (TFR) and the flow rate ratio (FRR) of the solvent and aqueous phases. Design of experiment and multivariate data analysis were used for increased process understanding and development of predictive and correlative models. High FRR lead to the bottom-up synthesis of liposomes, with a strong correlation with vesicle size, demonstrating the ability to in-process control liposomes size; the resulting liposome size correlated with the FRR in the microfluidics process, with liposomes of 50 nm being reproducibly manufactured. Furthermore, we demonstrate the potential of a high throughput manufacturing of liposomes using microfluidics with a four-fold increase in the volumetric flow rate, maintaining liposome characteristics. The efficacy of these liposomes was demonstrated in transfection studies and was modelled using predictive modeling. Mathematical modelling identified FRR as the key variable in the microfluidic process, with the highest impact on liposome size, polydispersity and transfection efficiency. This study demonstrates microfluidics as a robust and high-throughput method for the scalable and highly reproducible manufacture of size-controlled liposomes. Furthermore, the application of statistically based process control increases understanding and allows for the generation of a design-space for controlled particle characteristics. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

CHRONIC MEDICAL CONDITIONS AND REPRODUCIBILITY OF SELF-REPORTED AGE AT MENOPAUSE AMONG COMMUNITY DWELLING WOMEN

PubMed Central

de Vries, Heather F.; Northington, Gina M.; Kaye, Elise M.; Bogner, Hillary R.

2011-01-01

OBJECTIVE To examine the association between chronic medical conditions and reproducibility of self-reported age at menopause among community-dwelling women. METHOD Age at menopause was assessed in a population-based longitudinal survey of 240 women twice, in 1993 and 2004. Women who recalled age at menopause in 2004 within one year or less of the age at menopause recalled in 1993 (concordant) were compared with women who did not recall of age at menopause in 2004 within 1 year of age at menopause recalled in 1993 (discordant). Type of menopause (surgical or natural) and chronic medical conditions were assessed by self-report. RESULTS One hundred and forty three women (59.6%) reported surgical menopause and 97 (40.4%) reported natural menopause. In all, 130 (54.2%) of women recalled age at menopause in 2004 within one year or less of recalled age at menopause in 1994 while 110 (45.8%) women did not recall age at menopause in 2004 within one year or less of recalled age at menopause in 1994. Among women with surgical menopause, women with three or more medical conditions were less likely to have concordant recall of age at menopause than women with less than three chronic medical conditions (adjusted odds ratio (OR) = 0.36, 95% confidence interval (CI) [0.15, 0.91]) in multivariate models controlling for potentially influential characteristics including cognition and years from menopause. CONCLUSIONS Among women who underwent surgical menopause, the presence of three or more medical conditions is associated with decreased reproducibility of self-reported age at menopause. PMID:21971208

Chronic medical conditions and reproducibility of self-reported age at menopause among community-dwelling women.

PubMed

de Vries, Heather F; Northington, Gina M; Kaye, Elise M; Bogner, Hillary R

2011-12-01

The aim of this study was to examine the association between chronic medical conditions and reproducibility of self-reported age at menopause among community-dwelling women. Age at menopause was assessed in a population-based longitudinal survey of 240 women twice, in 1993 and 2004. Women who recalled age at menopause in 2004 within 1 year or less of age at menopause recalled in 1993 (concordant) were compared with women who did not recall age at menopause in 2004 within 1 year of age at menopause recalled in 1993 (discordant). Type of menopause (surgical or natural) and chronic medical conditions were assessed by self-report. One hundred forty-three women (59.6%) reported surgical menopause, and 97 (40.4%) reported natural menopause. In all, 130 (54.2%) women recalled age at menopause in 2004 within 1 year or less of recalled age at menopause in 1994, whereas 110 (45.8%) women did not recall age at menopause in 2004 within 1 year or less of recalled age at menopause in 1994. Among the women with surgical menopause, the women with three or more medical conditions were less likely to have concordant recall of age at menopause than the women with less than three chronic medical conditions (adjusted odds ratio, 0.36; 95% CI, 0.15-0.91) in multivariate models controlling for potentially influential characteristics including cognition and years since menopause. Among women who underwent surgical menopause, the presence of three or more medical conditions is associated with decreased reproducibility of self-reported age at menopause.

The Guy's stone score--grading the complexity of percutaneous nephrolithotomy procedures.

PubMed

Thomas, Kay; Smith, Naomi C; Hegarty, Nicholas; Glass, Jonathan M

2011-08-01

To report the development and validation of a scoring system, the Guy's stone score, to grade the complexity of percutaneous nephrolithotomy (PCNL). Currently, no standardized method is available to predict the stone-free rate after PCNL. The Guy's stone score was developed through a combination of expert opinion, published data review, and iterative testing. It comprises 4 grades: grade I, solitary stone in mid/lower pole or solitary stone in the pelvis with simple anatomy; grade II, solitary stone in upper pole or multiple stones in a patient with simple anatomy or a solitary stone in a patient with abnormal anatomy; grade III, multiple stones in a patient with abnormal anatomy or stones in a caliceal diverticulum or partial staghorn calculus; grade IV, staghorn calculus or any stone in a patient with spina bifida or spinal injury. It was assessed for reproducibility using the kappa coefficient and validated on a prospective database of 100 PCNL procedures performed in a tertiary stone center. The complications were graded using the modified Clavien score. The clinical outcomes were recorded prospectively and assessed with multivariate analysis. The Guy's stone score was the only factor that significantly and independently predicted the stone-free rate (P = .01). It was found to be reproducible, with good inter-rater agreement (P = .81). None of the other factors tested, including stone burden, operating surgeon, patient weight, age, and comorbidity, correlated with the stone-free rate. The Guy's stone score accurately predicted the stone-free rate after PCNL. It was easy to use and reproducible. Copyright © 2011 Elsevier Inc. All rights reserved.

Population-level analysis and validation of an individual-based cutthroat trout model

Treesearch

Steven F. Railsback; Bret C. Harvey; Roland H. Lamberson; Derek E. Lee; Claasen Nathan J.; Shuzo Yoshihara

2002-01-01

Abstract - An individual-based model of stream trout is analyzed by testing its ability to reproduce patterns of population-level behavior observed in real trout: (1) "self-thinning," a negative power relation between weight and abundance; (2) a "critical period" of density-dependent mortality in young-of-the-year; (3) high and age-speci...

Shallow Turbulence in Rivers and Estuaries

DTIC Science & Technology

2012-09-30

objectives are to: 1. Determine spatial patterns of shallow turbulence from in-situ and remote sensing data and investigate the effects and...production through a model parameter study, and determine the optimal model configuration that statistically reproduces the shallow turbulence...more probable cause. According to Nezu et al. (1993), longitudinal vorticity streets would cause alternating upwelling (boils) and down welling

Diurnal patterns in flight activity and effects of light on host finding behavior of the Asian citrus psyllid

USDA-ARS?s Scientific Manuscript database

The Asian citrus psyllid (ACP), Diaphorina citri (Hemiptera: Psyllidae), is an invasive pest of citrus in the United States. The psyllid feeds and reproduces primarily on new flush growth of citrus and other rutaceous plants. Because it vectors the bacterial causal agents of the deadly citrus green...

Hydrological hysteresis and its value for assessing process consistency in catchment conceptual models

Treesearch

O. Fovet; L. Ruiz; M. Hrachowitz; M. Faucheux; C. Gascuel-Odoux

2015-01-01

While most hydrological models reproduce the general flow dynamics, they frequently fail to adequately mimic system-internal processes. In particular, the relationship between storage and discharge, which often follows annual hysteretic patterns in shallow hard-rock aquifers, is rarely considered in modelling studies. One main reason is that catchment storage is...

Linking Social Structure and Interpersonal Behavior: A Theoretical Perspective on Cultural Schemas and Social Relations

ERIC Educational Resources Information Center

Ridgeway, Cecilia L.

2006-01-01

To explain how interpersonal behavior in relational contexts usually reproduces but sometimes modifies macro structural patterns, I outline a conceptual framework within which to understand existing theories and evidence and to develop new ones. Actors create and enact structure by means of several types of shared cultural schemas ("ordering…

Attitudes to School Science Held by Primary Children in Pakistan

ERIC Educational Resources Information Center

Iqbal, Hafiz Muhammad; Nageen, Tabassum; Pell, Anthony William

2008-01-01

Attitudes to science scales developed earlier in England have been used in and around a Pakistan city with children in Primary/Elementary Grades 4-8. The limitations of a "transferred scale" in a culturally different context are apparent in a failure to reproduce the English factor patterns, but items are identified to serve as a base…

The reactivity of the back revisited. Are there differences in reactivity in different parts of the back?

PubMed

Björk, Ann-Kristin; Bruze, Magnus; Engfeldt, Malin; Nielsen, Christel; Svedman, Cecilia

2017-01-01

In the contact dermatitis literature, it is regularly stated that the patch test reactivity on various areas of the back differs, which might have a large impact on the reproducibility of patch testing. To investigate the reproducibility of patch testing on the upper back with regard to the left as opposed to the right side, and the medial as opposed to the lateral part of the upper back. The reproducibility over time and with regard to the reactivity pattern was also investigated. Thirty-one subjects with contact allergy to the metals gold (n = 19) or nickel (n = 12) were patch tested with serial dilutions, in triplicate applications, on different locations on the upper back. The Friedman test was used for statistical calculations. No significant differences in the reactivity of the back were found. In all gold-allergic patients and 11 of 12 nickel-allergic patients, the allergy could be reproduced with regard to previous patch testing, but the degree of reactivity differed. When a high level of standardization of the patch test technique with the same test system was used, there were no differences in patch test reactions and sites of application on the upper back. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparison of Allogeneic and Syngeneic Rat Glioma Models by Using MRI and Histopathologic Evaluation

PubMed Central

Biasibetti, Elena; Valazza, Alberto; Capucchio, Maria T; Annovazzi, Laura; Battaglia, Luigi; Chirio, Daniela; Gallarate, Marina; Mellai, Marta; Muntoni, Elisabetta; Peira, Elena; Riganti, Chiara; Schiffer, Davide; Panciani, Pierpaolo; Lanotte, Michele

2017-01-01

Research in neurooncology traditionally requires appropriate in vivo animal models, on which therapeutic strategies are tested before human trials are designed and proceed. Several reproducible animal experimental models, in which human physiologic conditions can be mimicked, are available for studying glioblastoma multiforme. In an ideal rat model, the tumor is of glial origin, grows in predictable and reproducible patterns, closely resembles human gliomas histopathologically, and is weakly or nonimmunogenic. In the current study, we used MRI and histopathologic evaluation to compare the most widely used allogeneic rat glioma model, C6-Wistar, with the F98-Fischer syngeneic rat glioma model in terms of percentage tumor growth or regression and growth rate. In vivo MRI demonstrated considerable variation in tumor volume and frequency between the 2 rat models despite the same stereotactic implantation technique. Faster and more reproducible glioma growth occurred in the immunoresponsive environment of the F98-Fischer model, because the immune response is minimized toward syngeneic cells. The marked inability of the C6-Wistar allogeneic system to generate a reproducible model and the episodes of spontaneous tumor regression with this system may have been due to the increased humoral and cellular immune responses after tumor implantation. PMID:28381315

The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models

PubMed Central

Pires, Mathias M.; Cantor, Maurício; Guimarães, Paulo R.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Coltri, Patricia P.

2015-01-01

The network structure of biological systems provides information on the underlying processes shaping their organization and dynamics. Here we examined the structure of the network depicting protein interactions within the spliceosome, the macromolecular complex responsible for splicing in eukaryotic cells. We show the interactions of less connected spliceosome proteins are nested subsets of the connections of the highly connected proteins. At the same time, the network has a modular structure with groups of proteins sharing similar interaction patterns. We then investigated the role of affinity and specificity in shaping the spliceosome network by adapting a probabilistic model originally designed to reproduce food webs. This food-web model was as successful in reproducing the structure of protein interactions as it is in reproducing interactions among species. The good performance of the model suggests affinity and specificity, partially determined by protein size and the timing of association to the complex, may be determining network structure. Moreover, because network models allow building ensembles of realistic networks while encompassing uncertainty they can be useful to examine the dynamics and vulnerability of intracelullar processes. Unraveling the mechanisms organizing the spliceosome interactions is important to characterize the role of individual proteins on splicing catalysis and regulation. PMID:26443080

Three-dimensional Monte Carlo model of pulsed-laser treatment of cutaneous vascular lesions

NASA Astrophysics Data System (ADS)

Milanič, Matija; Majaron, Boris

2011-12-01

We present a three-dimensional Monte Carlo model of optical transport in skin with a novel approach to treatment of side boundaries of the volume of interest. This represents an effective way to overcome the inherent limitations of ``escape'' and ``mirror'' boundary conditions and enables high-resolution modeling of skin inclusions with complex geometries and arbitrary irradiation patterns. The optical model correctly reproduces measured values of diffuse reflectance for normal skin. When coupled with a sophisticated model of thermal transport and tissue coagulation kinetics, it also reproduces realistic values of radiant exposure thresholds for epidermal injury and for photocoagulation of port wine stain blood vessels in various skin phototypes, with or without application of cryogen spray cooling.

Towards a Multi-scale Montecarlo Climate Emulator for Coastal Flooding and Long-Term Coastal Change Modeling: The Beautiful Problem

NASA Astrophysics Data System (ADS)

Rueda, A.; Alvarez Antolinez, J. A.; Hegermiller, C.; Serafin, K.; Anderson, D. L.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Vitousek, S.; Camus, P.; Tomas, A.; Gonzalez, M.; Mendez, F. J.

2016-02-01

Long-term coastal evolution and coastal flooding hazards are the result of the non-linear interaction of multiple oceanographic, hydrological, geological and meteorological forcings (e.g., astronomical tide, monthly mean sea level, large-scale storm surge, dynamic wave set-up, shoreline evolution, backshore erosion). Additionally, interannual variability and trends in storminess and sea level rise are climate drivers that must be considered. Moreover, the chronology of the hydraulic boundary conditions plays an important role since a collection of consecutive minor storm events can have more impact than the 100-yr return level event. Therefore, proper modeling of shoreline erosion, beach recovery and coastal flooding should consider the sequence of storms, the multivariate nature of the hydrodynamic forcings, and the different time scales of interest (seasonality, interannual and decadal variability). To address this `beautiful problem', we propose a hybrid approach that combines: (a) numerical hydrodynamic and morphodynamic models (SWAN for wave transformation, a shoreline change model, X-Beach for modeling infragravity waves and erosion of the backshore during extreme events and RFSM-EDA (Jamieson et al, 2012) for high resolution flooding of the coastal hinterland); (b) long-term data bases (observational and hindcast) of sea state parameters, astronomical tides and non-tidal residuals; and (c) statistical downscaling techniques, non-linear data mining, and extreme value models. The statistical downscaling approaches for multivariate variables are based on circulation patterns (Espejo et al., 2014), the chronology of the circulation patterns (Guanche et al, 2013) and the event hydrographs of multivariate extremes, resulting in a time-dependent climate emulator of hydraulic boundary conditions for coupled simulations of the coastal change and flooding models. ReferencesEspejo et al (2014) Spectral ocean wave climate variability based on circulation patterns, J Phys Oc, doi: 10.1175/JPO-D-13-0276.1 Guanche et al (2013) Autoregressive logistic regression applied to atmospheric circulation patterns, Clim Dyn, doi: 10.1007/s00382-013-1690-3 Jamieson et al (2012) A highly efficient 2D flood model with sub-element topography, Proc. Of the Inst Civil Eng., 165(10), 581-595

Diversity pattern in Sesamum mutants selected for a semi-arid cropping system.

PubMed

Murty, B R; Oropeza, F

1989-02-01

Due to the complex requirements of moisture stress, substantial genetic diversity with a wide array of character combinations and effective simultaneous selection for several variables is necessary for improving the productivity and adaptation of a component crop in order for it to fit into a cropping system under semi-arid tropical conditions. Sesamum indicum L. is grown in Venezuela after rice/sorghum/or maize under such conditions. A mutation breeding program was undertaken using six locally adapted varieties to develop genotypes suitable for the above system. The diversity pattern for nine variables was assessed by multivariate analysis in 301 M4 progenies. Analysis of the characteristic roots and principal components in three methods of selection, i.e., M2 bulks (A), individual plant selection throughout (B), and selection in M3 for single variable (C), revealed differences in the pattern of variation between varieties, selection methods, and varieties x methods interactions. Method B was superior to the others and gave 17 of the 21 best M5 progenies. 'Piritu' and 'CF' varieties yielded the most productive progenies in M5 and M6. Diversity was large and selection was effective for such developmental traits as earliness and synchrony, combined with multiple disease resistance, which could be related to their importance by multivariate analyses. Considerable differences in the variety of character combinations among the high yielding. M5 progenies of 'CF' and 'Piritu' suggested possible further yield improvement. The superior response of 'Piritu' and 'CF' over other varieties in yield and adaptation was due to major changes in plant type and character associations. Multilocation testing of M5 generations revealed that the mutant progenies had a 40%-100% yield superiority over the parents; this was combined with earliness, synchrony, and multiple disease resistance, and was confirmed in the M6 generation grown on a commercial scale. This study showed that multivariate analysis is an effective tool for assessing diversity patterns, choice of appropriate variety, and selection methodology in order to make rapid progress in meeting the complex requirements of semi-arid cropping systems.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Radiation Channels Close to a Plasmonic Nanowire Visualized by Back Focal Plane Imaging

PubMed Central

Hartmann, Nicolai; Piatkowski, Dawid; Ciesielski, Richard; Mackowski, Sebastian; Hartschuh, Achim

2014-01-01

We investigated the angular radiation patterns, a key characteristic of an emitting system, from individual silver nanowires decorated with rare earth ion-doped nanocrystals. Back focal plane radiation patterns of the nanocrystal photoluminescence after local two-photon excitation can be described by two emission channels: Excitation of propagating surface plasmons in the nanowire followed by leakage radiation and direct dipolar emission observed also in the absence of the nanowire. Theoretical modeling reproduces the observed radiation patterns which strongly depend on the position of excitation along the nanowire. Our analysis allows to estimate the branching ratio into both emission channels and to determine the diameter dependent surface plasmon quasi-momentum, important parameters of emitter-plasmon structures. PMID:24131299

Programmable imprint lithography template

DOEpatents

Cardinale, Gregory F [Oakland, CA; Talin, Albert A [Livermore, CA

2006-10-31

A template for imprint lithography (IL) that reduces significantly template production costs by allowing the same template to be re-used for several technology generations. The template is composed of an array of spaced-apart moveable and individually addressable rods or plungers. Thus, the template can be configured to provide a desired pattern by programming the array of plungers such that certain of the plungers are in an "up" or actuated configuration. This arrangement of "up" and "down" plungers forms a pattern composed of protruding and recessed features which can then be impressed onto a polymer film coated substrate by applying a pressure to the template impressing the programmed configuration into the polymer film. The pattern impressed into the polymer film will be reproduced on the substrate by subsequent processing.

Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marre, O.; El Boustani, S.; Fregnac, Y.

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogatesmore » that reproduce the spatial and temporal correlations of a given data set.« less

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

USGS Publications Warehouse

Sork, Victoria L.; Davis, Frank W.; Westfall, Robert; Flint, Alan L.; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-01-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata N??e, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions. ?? 2010 Blackwell Publishing Ltd.

Identifying Neural Patterns of Functional Dyspepsia Using Multivariate Pattern Analysis: A Resting-State fMRI Study

PubMed Central

Liu, Peng; Qin, Wei; Wang, Jingjing; Zeng, Fang; Zhou, Guangyu; Wen, Haixia; von Deneen, Karen M.; Liang, Fanrong; Gong, Qiyong; Tian, Jie

2013-01-01

Background Previous imaging studies on functional dyspepsia (FD) have focused on abnormal brain functions during special tasks, while few studies concentrated on the resting-state abnormalities of FD patients, which might be potentially valuable to provide us with direct information about the neural basis of FD. The main purpose of the current study was thereby to characterize the distinct patterns of resting-state function between FD patients and healthy controls (HCs). Methodology/Principal Findings Thirty FD patients and thirty HCs were enrolled and experienced 5-mintue resting-state scanning. Based on the support vector machine (SVM), we applied multivariate pattern analysis (MVPA) to investigate the differences of resting-state function mapped by regional homogeneity (ReHo). A classifier was designed by using the principal component analysis and the linear SVM. Permutation test was then employed to identify the significant contribution to the final discrimination. The results displayed that the mean classifier accuracy was 86.67%, and highly discriminative brain regions mainly included the prefrontal cortex (PFC), orbitofrontal cortex (OFC), supplementary motor area (SMA), temporal pole (TP), insula, anterior/middle cingulate cortex (ACC/MCC), thalamus, hippocampus (HIPP)/parahippocamus (ParaHIPP) and cerebellum. Correlation analysis revealed significant correlations between ReHo values in certain regions of interest (ROI) and the FD symptom severity and/or duration, including the positive correlations between the dmPFC, pACC and the symptom severity; whereas, the positive correlations between the MCC, OFC, insula, TP and FD duration. Conclusions These findings indicated that significantly distinct patterns existed between FD patients and HCs during the resting-state, which could expand our understanding of the neural basis of FD. Meanwhile, our results possibly showed potential feasibility of functional magnetic resonance imaging diagnostic assay for FD. PMID:23874543

Detecting changes resulting from human pressure in a naturally quick-changing and heterogeneous environment: Spatial and temporal scales of variability in coastal lagoons

NASA Astrophysics Data System (ADS)

Pérez-Ruzafa, A.; Marcos, C.; Pérez-Ruzafa, I. M.; Barcala, E.; Hegazi, M. I.; Quispe, J.

2007-10-01

To detect changes in ecosystems due to human impact, experimental designs must include replicates at the appropriate scale to avoid pseudoreplication. Although coastal lagoons, with their highly variable environmental factors and biological assemblages, are relatively well-studied systems, very little is known about their natural scales of variation. In this study, we investigate the spatio-temporal scales of variability in the Mar Menor coastal lagoon (SE Spain) using structured hierarchical sampling designs, mixed and permutational multi-variate analyses of variance, and ordination multi-variate analyses applied to hydrographical parameters, nutrients, chlorophyll a and ichthyoplankton in the water column, and to macrophyte and fish benthic assemblages. Lagoon processes in the Mar Menor show heterogeneous patterns at different temporal and spatial scales. The water column characteristics (including nutrient concentration) showed small-scale spatio-temporal variability, from 10 0 to 10 1 km and from fortnightly to seasonally. Biological features (chlorophyll a concentration and ichthyoplankton assemblage descriptors) showed monthly changes and spatial patterns at the scale of 10 0 (chlorophyll a) - 10 1 km (ichthyoplankton). Benthic assemblages (macrophytes and fishes) showed significant differences between types of substrates in the same locality and between localities, according to horizontal gradients related with confinement in the lagoon, at the scale of 10 0-10 1 km. The vertical zonation of macrophyte assemblages (at scales of 10 1-10 2 cm) overlaps changes in substrata and horizontal gradients. Seasonal patterns in vegetation biomass were not significant, but the significant interaction between Locality and Season indicated that the seasons of maximum and minimum biomass depend on local environmental conditions. Benthic fish assemblages showed no significant patterns at the monthly scale but did show seasonal patterns.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change.

PubMed

Sork, Victoria L; Davis, Frank W; Westfall, Robert; Flint, Alan; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-09-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Actinic cheilitis: proposition and reproducibility of a clinical criterion.

PubMed

Poitevin, Nádia Antunes; Rodrigues, Mariana Sudati; Weigert, Karen Loureiro; Macedo, Carmen Lúcia Rodrigues; Dos Santos, Rubem Beraldo

2017-01-01

The actinic cheilitis (AC) is a precancerous lip lesion seen as a consequence of chronic sun exposure. Clinically, the border between the lip's skin and the semimucosa could be blurred; in the more aggressive cases, leucoplakia and ulcers also represent its clinical feature. It seems that no clinical criterion is universally accepted for this disease yet. Therefore, this study was carried out to make a proposition of a clinical score to actinic cheilitis (Grade I starting from dryness of vermilion to endured ulcers representing Grade IV) and to assess its reproducibility. Fifty subjects were assessed, most of whom were male, Caucasian farmers, with an average age of 46.12 (18-74) years. The obtained data were analysed by means of descriptive statistics and by Kappa test to assess the inter-examiners and the clinical Golden-Pattern concordance (95% CI). During calibration, 15 patients were examined three times a week by each examiner (4) until Kappa test observed k =0.8 or more. In the main experiment, the inter-examiner concordance was classified between good ( k =0.779; P <0.05) and very good ( k =0.925; P <0.05) from the 35 examined subjects. With the Golden-Pattern, it was considered very good ( k =0.812; P <0.05 to k =0.925; P <0.05). Four examiners with different experiences could strongly suggest that after adequate calibration, it could be well applied by examiners with as much experience as a dental student. The authors concluded that the proposed classification was easily applied and had a very good reproducibility.

A Comparison of Paleomagnetic Secular Variation during MIS 7-10 between the Bering Sea (IODP Ex. 323) and North Atlantic Ocean (ODP Leg 172): Implications for the space/time pattern of field and environmental variability (Invited)

NASA Astrophysics Data System (ADS)

Lund, S.; Okada, M.; Acton, G.; Clement, B. M.; Stoner, J. S.; Platzman, E. S.

2013-12-01

Detailed records of Brunhes paleomagnetic secular variation (PSV) during Marine Isotope Stages (MIS) 7-10 have been recovered from four IODP Ex. 323 sites in the Bering Sea (U1339, U1343-U1345) and four ODP Leg 172 sites from the subtropical North Atlantic Ocean (1060-1063). Reproducible records of PSV (both directions and paleointensity) have been recovered from three or more holes at each site and correlated among the four independent sites in each region. These PSV records provide an unprecedented database for considering patterns of long-term secular variation and evidence for excursional field behavior on a larger than individual regional scale. We will present reproducible evidence for sustained long-term secular variation in each region and assess the extent to which they may be interrelated. We have identified the times of magnetic field excursions 7α, 7β, 8α, 9α, and 9β in the Atlantic records and correlated those times to the Bering Sea records. There are no true excursions in the Bering Sea at those times, but several of these intervals mark the most anomalous field behavior in the Bering Sea during MIS 7-10. In both regions, the PSV also serves as a high-resolution chronostratigraphic tool for regional correlation of environmental variability. Both regions show clear, reproducible evidence among the sites for synchronous millennial-scale environmental variability that has not been diagnosed previously.

Numerical simulation on the southern flood and northern drought in summer 2014 over Eastern China

NASA Astrophysics Data System (ADS)

Xu, Lianlian; He, Shengping; Li, Fei; Ma, Jiehua; Wang, Huijun

2017-12-01

In summer 2014, Eastern China suffered a typical "southern flood and northern drought" anomalous climate. Observational analyses indicated that the anomalous vertical motion, East Asian subtropical westerly jet stream, and the East Asian summer monsoon (EASM) played important roles in the formation of such precipitation anomaly. Furthermore, using the climate model (IAP-AGCM-4.1) perturbed by simultaneous observed sea surface temperature anomalies (SSTAs) in global scale and four different regions (North Pacific, Indian Ocean, North Atlantic, and Equatorial Pacific), this study investigated the potential contribution of ocean to such "southern flood and northern drought" over Eastern China in summer 2014. The simulations forced by global-scale SSTAs or North Pacific SSTAs displayed the most similarity to the observed "southern flood and northern drought" over Eastern China. It was revealed that the global-scale and North Pacific SSTAs influenced the rainfall over Eastern China via modulating the EASM. The related simulations successfully reproduced the associated atmospheric circulation anomalies. The experiment driven by Indian Ocean SSTAs could also reproduce the similar precipitation anomaly pattern and suggested that the Indian Ocean exerted pronounced influence on the North Pacific Subtropical High. Additionally, the simulations forced by SSTAs in the North Atlantic and Equatorial Pacific successfully reproduced the northern drought but failed to capture the southern flood. The simulations suggested that precipitation anomaly over Eastern China in summer 2014 was a comprehensive effect of global SSTAs and the dominant contribution to the "southern flood and northern drought" pattern came from the North Pacific and Indian Ocean.

Mate-finding as an overlooked critical determinant of dispersal variation in sexually-reproducing animals.

PubMed

Gilroy, James J; Lockwood, Julie L

2012-01-01

Dispersal is a critically important process in ecology, but robust predictive models of animal dispersal remain elusive. We identify a potentially ubiquitous component of variation in animal dispersal that has been largely overlooked until now: the influence of mate encounters on settlement probability. We use an individual-based model to simulate dispersal in sexually-reproducing organisms that follow a simple set of movement rules based on conspecific encounters, within an environment lacking spatial habitat heterogeneity. We show that dispersal distances vary dramatically with fluctuations in population density in such a model, even in the absence of variation in dispersive traits between individuals. In a simple random-walk model with promiscuous mating, dispersal distributions become increasingly 'fat-tailed' at low population densities due to the increasing scarcity of mates. Similar variation arises in models incorporating territoriality. In a model with polygynous mating, we show that patterns of sex-biased dispersal can even be reversed across a gradient of population density, despite underlying dispersal mechanisms remaining unchanged. We show that some widespread dispersal patterns found in nature (e.g. fat tailed distributions) can arise as a result of demographic variability in the absence of heterogeneity in dispersive traits across the population. This implies that models in which individual dispersal distances are considered to be fixed traits might be unrealistic, as dispersal distances vary widely under a single dispersal mechanism when settlement is influenced by mate encounters. Mechanistic models offer a promising means of advancing our understanding of dispersal in sexually-reproducing organisms.

Neural substrates of time perception and impulsivity

PubMed Central

Wittmann, Marc; Simmons, Alan N.; Flagan, Taru; Lane, Scott D.; Wackermann, Jiří; Paulus, Martin P.

2011-01-01

Several studies provide empirical evidence for the association between impulsivity and time perception. However, little is known about the neural substrates underlying this function. This investigation examined the influence of impulsivity on neural activation patterns during the encoding and reproduction of intervals with durations of 3, 9 and 18 seconds using event-related functional magnetic resonance imaging (fMRI). Twenty-seven subjects participated in this study, including 15 high impulsive subjects that were classified based on their self-rating. FMRI activation during the duration reproduction task was correlated with measures of two self-report questionnaires related to the concept of impulsivity (Barratt Impulsiveness Scale, BIS; Zimbardo Time Perspective Inventory, ZTPI). Behaviorally, those individuals who under-reproduced temporal intervals also showed lower scores on the ZTPI future perspective subscale and higher scores on the BIS. FMRI activation revealed an accumulating pattern of neural activity peaking at the end of the 9- and 18-s interval within right posterior insula. Activations of brain regions during the reproduction phase of the timing task, such as those related to motor execution as well as to the ‘core control network’ – encompassing the inferior frontal and medial frontal cortex, the anterior insula as well as the inferior parietal cortex – were significantly correlated with reproduced duration, as well as with BIS and ZTPI subscales. In particular, the greater activation in these regions the shorter were the reproduced intervals, the more impulsive was an individual and the less pronounced the future perspective. Activation in the core control network, thus, may form a biological marker for cognitive time management and for impulsiveness. PMID:21763642

Pattern formation of frictional fingers in a gravitational potential

NASA Astrophysics Data System (ADS)

Eriksen, Jon Alm; Toussaint, Renaud; Mâløy, Knut Jørgen; Flekkøy, Eirik; Galland, Olivier; Sandnes, Bjørnar

2018-01-01

Aligned finger structures, with a characteristic width, emerge during the slow drainage of a liquid-granular mixture in a tilted Hele-Shaw cell. A transition from vertical to horizontal alignment of the finger structures is observed as the tilting angle and the granular density are varied. An analytical model is presented, demonstrating that the alignment properties are the result of the competition between fluctuating granular stresses and the hydrostatic pressure. The dynamics is reproduced in simulations. We also show how the system explains patterns observed in nature, created during the early stages of a dike formation.

Observation of an optical vortex beam from a helical undulator in the XUV region.

PubMed

Kaneyasu, Tatsuo; Hikosaka, Yasumasa; Fujimoto, Masaki; Iwayama, Hiroshi; Hosaka, Masahito; Shigemasa, Eiji; Katoh, Masahiro

2017-09-01

The observation of an optical vortex beam at 60 nm wavelength, produced as the second-harmonic radiation from a helical undulator, is reported. The helical wavefront of the optical vortex beam was verified by measuring the interference pattern between the vortex beam from a helical undulator and a normal beam from another undulator. Although the interference patterns were slightly blurred owing to the relatively large electron beam emittance, it was possible to observe the interference features thanks to the helical wavefront of the vortex beam. The experimental results were well reproduced by simulation.

Structural Covariance of the Default Network in Healthy and Pathological Aging

PubMed Central

Turner, Gary R.

2013-01-01

Significant progress has been made uncovering functional brain networks, yet little is known about the corresponding structural covariance networks. The default network's functional architecture has been shown to change over the course of healthy and pathological aging. We examined cross-sectional and longitudinal datasets to reveal the structural covariance of the human default network across the adult lifespan and through the progression of Alzheimer's disease (AD). We used a novel approach to identify the structural covariance of the default network and derive individual participant scores that reflect the covariance pattern in each brain image. A seed-based multivariate analysis was conducted on structural images in the cross-sectional OASIS (N = 414) and longitudinal Alzheimer's Disease Neuroimaging Initiative (N = 434) datasets. We reproduced the distributed topology of the default network, based on a posterior cingulate cortex seed, consistent with prior reports of this intrinsic connectivity network. Structural covariance of the default network scores declined in healthy and pathological aging. Decline was greatest in the AD cohort and in those who progressed from mild cognitive impairment to AD. Structural covariance of the default network scores were positively associated with general cognitive status, reduced in APOEε4 carriers versus noncarriers, and associated with CSF biomarkers of AD. These findings identify the structural covariance of the default network and characterize changes to the network's gray matter integrity across the lifespan and through the progression of AD. The findings provide evidence for the large-scale network model of neurodegenerative disease, in which neurodegeneration spreads through intrinsically connected brain networks in a disease specific manner. PMID:24048852

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Angular reconstitution-based 3D reconstructions of nanomolecular structures from superresolution light-microscopy images

PubMed Central

Salas, Desirée; Le Gall, Antoine; Fiche, Jean-Bernard; Valeri, Alessandro; Ke, Yonggang; Bron, Patrick; Bellot, Gaetan

2017-01-01

Superresolution light microscopy allows the imaging of labeled supramolecular assemblies at a resolution surpassing the classical diffraction limit. A serious limitation of the superresolution approach is sample heterogeneity and the stochastic character of the labeling procedure. To increase the reproducibility and the resolution of the superresolution results, we apply multivariate statistical analysis methods and 3D reconstruction approaches originally developed for cryogenic electron microscopy of single particles. These methods allow for the reference-free 3D reconstruction of nanomolecular structures from two-dimensional superresolution projection images. Since these 2D projection images all show the structure in high-resolution directions of the optical microscope, the resulting 3D reconstructions have the best possible isotropic resolution in all directions. PMID:28811371

Breakthrough in current-in-plane tunneling measurement precision by application of multi-variable fitting algorithm.

PubMed

Cagliani, Alberto; Østerberg, Frederik W; Hansen, Ole; Shiv, Lior; Nielsen, Peter F; Petersen, Dirch H

2017-09-01

We present a breakthrough in micro-four-point probe (M4PP) metrology to substantially improve precision of transmission line (transfer length) type measurements by application of advanced electrode position correction. In particular, we demonstrate this methodology for the M4PP current-in-plane tunneling (CIPT) technique. The CIPT method has been a crucial tool in the development of magnetic tunnel junction (MTJ) stacks suitable for magnetic random-access memories for more than a decade. On two MTJ stacks, the measurement precision of resistance-area product and tunneling magnetoresistance was improved by up to a factor of 3.5 and the measurement reproducibility by up to a factor of 17, thanks to our improved position correction technique.

Impact of novel histopathological factors on the outcomes of liver surgery for colorectal cancer metastases.

PubMed

Serrablo, A; Paliogiannis, P; Pulighe, F; Moro, S Saudi-Moro; Borrego-Estella, V; Attene, F; Scognamillo, F; Hörndler, C

2016-09-01

We evaluated the impacts of a series of novel histopathological factors on clinical-surgical outcomes and survival of patients who underwent surgery for colorectal cancer liver metastasis, with and without neoadjuvant chemotherapy. A prospective database including 150 consecutive patients who underwent 183 hepatic resections for metastatic colorectal cancer was evaluated. Among them, 74 (49.3%) received neoadjuvant chemotherapy before surgery. The histopathological factors studied were: a) microsatellitosis, b) type and pattern of tumour growth, c) nuclear grade and the number of mitoses/mm(2), d) perilesional pseudocapsule, e) intratumoural fibrosis, f) lesion cellularity, g) hypoxic-angiogenic perilesional growth pattern, and h) the tumour normal interface. Three or more metastatic lesions, R1 resection margins, and <50% tumour necrosis were prognostic factors for a worse OS, but only the former was confirmed to be an independent prognostic factor in the multivariate analysis. Furthermore, tumour fibrosis <40% and cellularity >10% were predictive of a worse neoadjuvant therapy response, but these findings were not confirmed in the multivariate analysis. Finally, tumour necrosis <50%, cellularity >10%, and TNI >0.5 mm were prognostic factors for a worse DFS and AS in the univariate but not in the multivariate analysis. Several factors seem to influence the outcomes of surgery for colorectal cancer liver metastasis, especially the number of the lesions, the margins of resection, the percentage of necrosis and fibrosis, as well as the cellularity and the TNI. Copyright © 2016 Elsevier Ltd. All rights reserved.

Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts

NASA Astrophysics Data System (ADS)

Goodwell, Allison E.; Kumar, Praveen

2017-07-01

In an ecohydrologic system, components of atmospheric, vegetation, and root-soil subsystems participate in forcing and feedback interactions at varying time scales and intensities. The structure of this network of complex interactions varies in terms of connectivity, strength, and time scale due to perturbations or changing conditions such as rainfall, drought, or land use. However, characterization of these interactions is difficult due to multivariate and weak dependencies in the presence of noise, nonlinearities, and limited data. We introduce a framework for Temporal Information Partitioning Networks (TIPNets), in which time-series variables are viewed as nodes, and lagged multivariate mutual information measures are links. These links are partitioned into synergistic, unique, and redundant information components, where synergy is information provided only jointly, unique information is only provided by a single source, and redundancy is overlapping information. We construct TIPNets from 1 min weather station data over several hour time windows. From a comparison of dry, wet, and rainy conditions, we find that information strengths increase when solar radiation and surface moisture are present, and surface moisture and wind variability are redundant and synergistic influences, respectively. Over a growing season, network trends reveal patterns that vary with vegetation and rainfall patterns. The framework presented here enables us to interpret process connectivity in a multivariate context, which can lead to better inference of behavioral shifts due to perturbations in ecohydrologic systems. This work contributes to more holistic characterizations of system behavior, and can benefit a wide variety of studies of complex systems.

Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

NASA Astrophysics Data System (ADS)

Aguilar-Hidalgo, D.; Lemos, M. C.; Córdoba, A.

2015-03-01

During organogenesis, developmental programs governed by Gene Regulatory Networks (GRN) define the functionality, size and shape of the different constituents of living organisms. Robustness, thus, is an essential characteristic that GRNs need to fulfill in order to maintain viability and reproducibility in a species. In the present work we analyze the robustness of the patterning for the ocellar complex formation in Drosophila melanogaster fly. We have systematically pruned the GRN that drives the development of this visual system to obtain the minimum pathway able to satisfy this pattern. We found that the mechanism underlying the patterning obeys to the dynamics of a 3-nodes network motif with a double negative feedback loop fed by a morphogenetic gradient that triggers the inhibition in a French flag problem fashion. A Boolean modeling of the GRN confirms robustness in the patterning mechanism showing the same result for different network complexity levels. Interestingly, the network provides a steady state solution in the interocellar part of the patterning and an oscillatory regime in the ocelli. This theoretical result predicts that the ocellar pattern may underlie oscillatory dynamics in its genetic regulation.

Why do lab-scale experiments ever resemble geological scale patterning?

NASA Astrophysics Data System (ADS)

Ferdowsi, Behrooz; Jones, Brandon C.; Stein, Jeremy L.; Shinbrot, Troy

2017-11-01

The Earth and other planets are abundant with curious and poorly understood surface patterns. Examples include sand dunes, periodic and aperiodic ridges and valleys, and networks of river and submarine channels. We make the minimalist proposition that the dominant mechanism governing these varied patterns is mass conservation: notwithstanding detailed particulars, the universal rule is mass conservation and there are only a finite number of surface patterns that can result from this process. To test this minimalist proposition, we perform experiments in a vertically vibrated bed of fine grains, and we show that every one of a wide variety of patterns seen in the laboratory is also seen in recorded geomorphologies. We explore a range of experimental driving frequencies and amplitudes, and we complement these experimental results with a non-local cellular automata model that reproduces the surface patterns seen using a minimalist approach that allows a free surface to deform subject to mass conservation and simple known forces such as gravity. These results suggest a common cause for the effectiveness of lab-scale models for geological scale patterning that otherwise ought to have no reasonable correspondence.

Multivariate approach to matrimonial mobility in Catalonia.

PubMed

Calafell, F; Hernández, M

1993-10-01

Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.

Sheet-like and plume-like thermal flow in a spherical convection experiment performed under microgravity

NASA Astrophysics Data System (ADS)

Breuer, D.; Futterer, B.; Plesa, A.; Krebs, A.; Zaussinger, F.; Egbers, C.

2013-12-01

In mantle dynamics research, experiments, usually performed in rectangular geometries in Earth-based laboratories, have the character of ';exploring new physics and testing theories' [1]. In this work, we introduce our spherical geometry experiments on electro-hydrodynamical driven Rayleigh-Benard convection that have been performed for both temperature-independent (`GeoFlow I'), and temperature-dependent fluid viscosity properties (`GeoFlow II') with a measured viscosity contrast up to 1.5. To set up a self-gravitating force field, we use a high voltage potential between the inner and outer boundaries and a dielectric insulating liquid and perform the experiment under microgravity conditions at the ISS [2, 3]. Further, numerical simulations in 3D spherical geometry have been used to reproduce the results obtained in the `GeoFlow' experiments. For flow visualisation, we use Wollaston prism shearing interferometry which is an optical method producing fringe pattern images. Flow pattern differ between our two experiments (Fig. 1). In `GeoFlow I', we see a sheet-like thermal flow. In this case convection patterns have been successfully reproduced by 3D numerical simulations using two different and independently developed codes. In contrast, in `GeoFlow II' we obtain plume-like structures. Interestingly, numerical simulations do not yield this type of solution for the low viscosity contrast realised in the experiment. However, using a viscosity contrast of two orders of magnitude or higher, we can reproduce the patterns obtained in the `GeoFlow II' experiment, from which we conclude that non-linear effects shift the effective viscosity ratio [4]. References [1] A. Davaille and A. Limare (2009). In: Schubert, G., Bercovici, D. (Eds.), Treatise on Geophysics - Mantle Dynamics. [2] B. Futterer, C. Egbers, N. Dahley, S. Koch, L. Jehring (2010). Acta Astronautica 66, 193-100. [3] B. Futterer, N. Dahley, S. Koch, N. Scurtu, C. Egbers (2012). Acta Astronautica 71, 11-19. [4] B. Futterer, A. Krebs, A.-C. Plesa, F. Zaussinger, D.Breuer, C. Egbers (2013). submitted to Journal of Fluid Mechanics. Fig. 1: a) Sheet-like thermal flow in the GeoFlow I spherical experiment with silicone oil of temperature-stable properties (RaE=1.17e6); b) Plume-like dominated flow in the GeoFlow II experiment using a fluid with temperature dependent viscosity and volume expansion (RaE=1.87e6).

An Evaluation of Subseasonal Intensity Variation of the South Asian High in the CMIP5 Models

NASA Astrophysics Data System (ADS)

Shang, W.; Ren, X.

2017-12-01

The South Asian High (SAH) is a vital member among the Asian summer monsoon circulations in the upper troposphere located over the Tibetan Plateau and its surrounding areas during boreal summer. This study presents an evaluation of the characteristics of SAH's subseasonal intensity variation simulated by 18 coupled models from the Coupled Model Intercomparison Project (CMIP5). The empirical orthogonal function (EOF) analysis is used on subseasonal anomalies in 100hPa geopotential height over 20°-40°N, 35°-110°E for June, July and August from 1979 to 2005. The first EOF mode from the ERA-Interim (denoted as observation) shows a monopole pattern capturing the strengthening/weakening of SAH. The power spectrum analysis of the corresponding principal component (PC1) time series shows a period about 10-30 days. In general, all the 18 coupled models can reproduce the monopole pattern and its subseasonal oscillation to a certain extent. The four well-simulated models are: ACCESS1-3, HadGEM2-CC, MRI-CGCM3 and BNU-ESM, which EOF1 show strong positive anomalies in the 100hPa geopotential height over the SAH's region and weak negative anomalies in their north side. Lead-lag regression shows that the evolution of the EOF1 from day -12 to +3 in the above four models is also reasonably simulated. In the observation, positive rainfall band moves northward from the equatorial Indian Ocean, the Bay of Bengals and the Western North Pacific to the north of Indian Peninsula, south of Tibetan Plateau and Southeast China, accompanied by the increasing intensity of SAH. In the above four models, the northward movement of anomalous rainfall band can be well reproduced. In the observation, the spatial pattern of anomalies in integrated apparent heat source and integrated apparent moisture sink resemble that of rainfall, thus corresponding to anomalous condensation heat release. The anomalous heating stimulates positive height anomalies with an anomalous anticyclonic circulation to its northwest in the upper troposphere, causing the strengthening of SAH intensity. For the four well-simulated models, the anomalies pattern of and are realistically simulated. That is why they are able to reproduce the key features of the subseasonal intensity variation of the SAH.

Facebook Usage Patterns and School Attitudes

ERIC Educational Resources Information Center

Koles, Bernadett; Nagy, Peter

2012-01-01

Purpose: The purpose of this paper is to explore teenagers' and young adults' use of social networking sites (SNS), in light of certain personal, social and educational outcomes and attitudes. Design/methodology/approach: Data were gathered on the basis of surveys, and were analyzed through a series of multivariate models. Findings: It was found…

A Probability Based Framework for Testing the Missing Data Mechanism

ERIC Educational Resources Information Center

Lin, Johnny Cheng-Han

2013-01-01

Many methods exist for imputing missing data but fewer methods have been proposed to test the missing data mechanism. Little (1988) introduced a multivariate chi-square test for the missing completely at random data mechanism (MCAR) that compares observed means for each pattern with expectation-maximization (EM) estimated means. As an alternative,…

Multivariate Genetic Analysis of Learning and Early Reading Development

ERIC Educational Resources Information Center

Byrne, Brian; Wadsworth, Sally; Boehme, Kristi; Talk, Andrew C.; Coventry, William L.; Olson, Richard K.; Samuelsson, Stefan; Corley, Robin

2013-01-01

The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 ("N"?=?2,084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the…