scholarly journals Analyzing the Time Course of Pupillometric Data

2019 ◽  
Vol 23 ◽  
pp. 233121651983248 ◽  
Author(s):  
Jacolien van Rij ◽  
Petra Hendriks ◽  
Hedderik van Rijn ◽  
R. Harald Baayen ◽  
Simon N. Wood
Keyword(s):  
Regression Analysis ◽  
Language Processing ◽  
Nonlinear Regression ◽  
Time Course ◽  
Time Series Data ◽  
Pupil Dilation ◽  
Series Data ◽  
Measured Signal ◽  
Full Time ◽  
Psycholinguistic Research

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals How to predict relapse in leukaemia using time series data: A comparative in silico study

2020 ◽  
Author(s):  
Helene Hoffmann ◽  
Christoph Baldow ◽  
Thomas Zerjatke ◽  
Andrea Gottschalk ◽  
Sebastian Wagner ◽  
...  
Keyword(s):  
Data Quality ◽  
Computational Methods ◽  
In Silico ◽  
Prediction Accuracy ◽  
Time Course ◽  
Time Series Data ◽  
Specific Treatment ◽  
Series Data ◽  
Patient Specific ◽  
Clinical Markers

SummaryRisk stratification and treatment decisions for leukaemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improving the predictions for patient-specific treatment response.We analyzed the potential of different computational methods to accurately predict relapse for chronic and acute myeloid leukaemia, particularly focusing on the influence of data quality and quantity. Technically, we used clinical reference data to generate in-silico patients with varying levels of data quality. Based hereon, we compared the performance of mechanistic models, generalized linear models, and neural networks with respect to their accuracy for relapse prediction. We found that data quality has a higher impact on prediction accuracy than the specific choice of the method. We further show that adapted treatment and measurement schemes can considerably improve prediction accuracy. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukaemia patients.

scholarly journals Peran Grup Afiliasi dalam Memoderasi Pengaruh Hutang terhadap Nilai Perusahaan pada Perusahaan Property & Real Estate yang terdaftar di Bursa Efek Indonesia

FORUM EKONOMI ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 58
Author(s):  
Fina Maulidyani ◽  
Set Asmapane ◽  
Ledy Setiawati
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Stock Exchange ◽  
Debt Financing ◽  
Series Data ◽  
Simple Linear Regression ◽  
Group Affiliation ◽  
A Company ◽  
Positive Effect

The objective of this research is to examine empirically the effect of Debt on Firm’s Value and the ability of Group Affiliation to moderate the effect of Debt toward Firm’s Value by using an approach simple linear regression and Moderated Regression Analysis (MRA). This research uses time series data in 2011 – 2015 for company's finance data that got from Indonesia Stock Exchange. Election Procedure sample uses purposive sampling and the result are existed 35 companies that fulfill criterion. The results show that Debt has positive effect on Firm’s Value, while Group Affiliation act as a moderating variable can reduce the effect of Debt toward Firm’s Value. The higher capability of group affiliation to take control of a company, the lower debt financing that company has.Keywords:     Debt Financing, Firm’s Value, Group Affiliation

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

scholarly journals The Origins and Effects of Corporate Diversity Programs

2013 ◽  
Author(s):  
Frank Dobbin ◽  
Alexandra Kalev
Keyword(s):  
Equal Opportunity ◽  
Time Series Data ◽  
Diversity Training ◽  
Series Data ◽  
Mentoring Programs ◽  
Full Time ◽  
Wide Range ◽  
Diversity Programs ◽  
Corporate Diversity ◽  
The 1960S

Corporations have implemented a wide range of equal opportunity and diversity programs since the 1960s. This chapter reviews studies of the origins of these programs, surveys that assess the popularity of different programs, and research on the effects of programs on the workforce. Human resources managers championed several waves of innovations: corporate equal opportunity policies and recruitment and training programs in the 1960s; bureaucratic hiring and promotion policies and grievance mechanisms in the 1970s; diversity training, networking, and mentoring programs in the 1980s; and work/family and sexual harassment programs in the 1990s and beyond. It was those managers who designed equal opportunity and diversity programs, not lawyers or judges or government bureaucrats, thus corporate take-up of the programs remains very uneven. Statistical analyses of time-series data on the effects of corporate diversity measures reveal several patterns. Initiatives designed to quash managerial bias, through diversity training, diversity performance evaluations, and bureaucratic rules, have been broadly ineffective. By contrast, innovations designed to engage managers in promoting workforce integration—mentoring programs, diversity taskforces, and full-time diversity staffers—have led to increases in diversity in the most difficult job to integrate, management. The research has clear implications for corporate and public policy.

P–368 Dynamic metabolomic profiling during early implantation period

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Y Zhang ◽  
Y W Zhao ◽  
C C Wang ◽  
T C Li
Keyword(s):  
Mass Spectrometry ◽  
Time Series ◽  
Embryo Transfer ◽  
Study Design ◽  
Empirical Bayes ◽  
Time Course ◽  
Time Series Data ◽  
Series Data ◽  
Metabolomic Profiling ◽  
Implantation Period

Abstract Study question To investigate the different metabolomic profiling in serum between pregnant and non-pregnant women during early implantation period. Summary answer Metabolomics of progesterone-related hormones enhances from ET day3 for pregnancy women compared with non-pregnancy women. What is known already Metabolomics is based on high-throughput analytical methods to identify and quantify metabolites. Compared to other omics study, metabolomics is the closest one to the phenotype, allowing the observation of dynamic changes in phenotype at specific timepoints. So far there is no published work about the metabolomics profile in human early implantation period. Study design, size, duration: Study design: comparative study. Size: 14 pregnancy women and 14 non-pregnancy women. duration: time-course. Participants/materials, setting, methods Participants: pregnancy women and unpregnancy women after embryo transfer (ET). Setting: university-based study. Methods: Peripheral blood were collected at ET day0, 3, 6 and 9. metabolomic profiling in serum by platforms of capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry (LC-MS). Main results and the role of chance There were no statistical difference of the age, BMI, basal FSH level, endometrium thickness on the day of embryo transfer, distribution of primary and secondary fertility, embryo transfer cycle as well as the infertile types between the two groups. After deleting those with over 50% missing data, we finally have 310 metabolites into statistical analysis. Among the 310 metabolite, lipid metabolites account the largest percentage, nearly half of all metabolites. The second biggest class of metabolites in our data was organic acids. Combined results in repeated measurement ANOVA (RM-ANOVA) and ANOVA-simultaneous component analysis (ASCA) as well as multivariate empirical Bayes time-series analysis (MEBA), we finally found that progesterone-related hormones were the most important metabolites for the whole time-series data. Those significant metabolites showed a significant down regulation from ET day0 to ET day3 and up regulation from ET day3 to ET day9. Limitations, reasons for caution we have limited sample size for this study and further validation is necessary for confirmation. Wider implications of the findings: The phenomenon of upregulation of progesterone-related hormones from day3 in pregnancy group might be related to the embryo-originated hcg. Because the embryo has entered into endometrium at day3 and produced cytokines, hcg and other interaction with endometrium. Trial registration number NA

Congo Basin's Shrinking Watersheds

2020 ◽  
pp. 1452-1468
Author(s):  
Bila-Isia Inogwabini
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Democratic Republic Of Congo ◽  
Congo Basin ◽  
Series Data ◽  
Water Volume ◽  
Rainfall Time Series ◽  
Aquatic Resources ◽  
Basin Water

Rainfall time series data from three sites (Kinshasa, Luki, and Mabali) in the western Democratic Republic of Congo were analyzed using regression analysis; rainfall intensities decreased in all three sites. The Congo Basin waters will follow the equation y = -20894x + 5483.16; R2 = 0.7945. The model suggests 18%-loss of the Congo Basin water volume and 7%-decrease for fish biomasses by 2025. Financial incomes generated by fishing will decrease by 11% by 2040 compared with 1998 levels. About 51% of women (N= 408,173) from the Lake Tumba Landscape fish; their revenues decreased by 11% between 2005 and 2010. If this trend continues, women's revenues will decrease by 59% by 2040. Decreased waters will severely impact women (e.g. increasing walking distances to clean waters). Increasing populations and decreasing waters will lead to immigrations to this region because water resources will remain available and highly likely ignite social conflicts over aquatic resources.

Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants

2011 ◽  
Vol 15 (3) ◽  
pp. 336-344 ◽  
Author(s):  
Daisaku Kimura ◽  
◽  
Manabu Nii ◽  
Takafumi Yamaguchi ◽  
Yutaka Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Regression ◽  
Time Series Data ◽  
Numerical Data ◽  
The State ◽  
Series Data ◽  
Target System ◽  
Chemical Plants ◽  
Domain Experts ◽  
Recorded Data

In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult to realize. In this paper, a technique of diagnosing target systems based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.

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