Multivariate probit linear mixed models for multivariate longitudinal binary data.

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint. The correlation matrix may additionally be heteroscedastic; however, the matrix structure is commonly considered to be homoscedastic and constrained, such as exchangeable or autoregressive with order one. These assumptions are overly strong, resulting in skewed estimates of the covariate effects on the responses. Hence, we propose probit linear mixed models for multivariate longitudinal binary data, where the correlation matrix is estimated using hypersphere decomposition instead of the strong assumptions noted above. Simulations and real examples are used to demonstrate the proposed methods. An open source R package, BayesMGLM, is made available on GitHub at https://github.com/kuojunglee/BayesMGLM/ with full documentation to produce the results.

Experimental method for measuring color appearance shifts in high-dynamic-range luminance conditions.

We present an experimental method to determine color appearance shifts under high-dynamic-range conditions. A couple of light booths with variable luminance provide high-dynamic-range luminance conditions, and a perceptual color shift between the two booths is determined using color appearance matching. For red, green, yellow, and blue groups of four surface color samples, color shifts were measured for nine subjects under a dual illumination at background luminance levels of $100\,\,{{\rm cd/m}^2}$100cd/m2 and $4700\,\,{{\rm cd/m}^2}$4700cd/m2. We observed significant perceptual hue shifts toward blue with magnitudes of 2.5 to 3.9 and 5.0 to 6.9 CIELAB units, for the red and green samples, respectively, and decreases in chroma for most samples when changed from low to high luminances.

Effects of age, working memory, and word order on passive-sentence comprehension: evidence from a verb-final language.

BACKGROUND: The purpose of the current study was to investigate the effects of working-memory (WM) capacity on age-related changes in abilities to comprehend passive sentences when the word order was systematically manipulated. METHODS: A total of 134 individuals participated in the study. The sentence-comprehension task consisted of the canonical and non-canonical word-order conditions. A composite measure of WM scores was used as an index of WM capacity. RESULTS: Participants exhibited worse performance on sentences with non-canonical word order than canonical word order. The two-way interaction between age and WM was significant, suggesting that WM effects were greater than age effects on the task. CONCLUSIONS: WM capacity effects on passive-sentence comprehension increased dramatically as people aged, suggesting that those who have larger WM capacity are less vulnerable to age-related changes in sentence-comprehension abilities. WM capacity may serve as a cognitive reserve associated with sentence-comprehension abilities for elderly adults.

Verifying Identities of Plant-Based Multivitamins Using Phytochemical Fingerprinting in Combination with Multiple Bioassays.

Sales of multivitamins have been growing rapidly and the concept of natural multivitamin, plant-based multivitamin, or both has been introduced in the market, leading consumers to anticipate additional health benefits from phytochemicals that accompany the vitamins. However, the lack of labeling requirements might lead to fraudulent claims. Therefore, the objective of this study was to develop a strategy to verify identity of plant-based multivitamins. Phytochemical fingerprinting was used to discriminate identities. In addition, multiple bioassays were performed to determine total antioxidant capacity. A statistical computation model was then used to measure contributions of phytochemicals and vitamins to antioxidant activities. Fifteen multivitamins were purchased from the local markets in Seoul, Korea and classified into three groups according to the number of plant ingredients. Pearson correlation analysis among antioxidant capacities, amount phenols, and number of plant ingredients revealed that ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picryhydrazyl (DPPH) assay results had the highest correlation with total phenol content. This suggests that FRAP and DPPH assays are useful for characterizing plant-derived multivitamins. Furthermore, net effect linear regression analysis confirmed that the contribution of phytochemicals to total antioxidant capacities was always relatively higher than that of vitamins. Taken together, the results suggest that phytochemical fingerprinting in combination with multiple bioassays could be used as a strategy to determine whether plant-derived multivitamins could provide additional health benefits beyond their nutritional value.

Predicting Driver's mental workload using physiological signals: A functional data analysis approach.

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Spatial uniformity inspection apparatus for solar cells using a projection display.

We demonstrate a measurement apparatus to inspect spatial uniformity of quantum efficiency of solar cells using a beam projector. Deviation of irradiance from the used beam projector over the area of 1.5×0.8 m on the cell plane was flattened within ±2.6% through gray scale adjustment, which was originally about 200%. Scanning a small square image with an area of 3×3 mm over a square-shaped photovoltaic cell with an area of 15.6×15.6 cm, we could identify the locations according to efficiency level and showed that the cell had quantum efficiency deviation of more than 10%. Utilizing the advantageous feature of a projection display, we also demonstrated that this apparatus can inspect the spatial uniformity of solar modules and panels consisting of multiple solar cells.

Machine learning approach for study on subway passenger flow.

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a 'smart city' which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

Model-free predictor tests in survival regression through sufficient dimension reduction.

In this article, we test the effects of predictors in survival regression through two well-known sufficient dimension reduction methods. Since the usual sufficient dimension reduction methods do not require pre-specified models, the predictor effect tests can be considered model-free. All of the test statistics have χ (2) distributions. Numerical studies of the proposed predictor effect tests in various simulations and real data application are presented.

Syntactic Complexity as a Linguistic Marker to Differentiate Mild Cognitive Impairment From Normal Aging.

Purpose In this study, we sought to identify critical linguistic markers that can differentiate sentence processing of individuals with mild cognitive impairment (MCI) from the sentence processing of normal-aging populations by manipulating sentences' linguistic complexity. We investigated whether passive sentences, as linguistically complex structures, can serve as linguistic markers that can contribute to diagnoses that distinguish MCI from normal aging. Method In total, 52 participants, including 26 adults with amnestic MCI and 26 cognitively unimpaired adults, participated in the study. All participants were native speakers of Korean. We administered the two subsets of active and passive conditions using a sentence-picture paradigm with semantically reversible sentences to both groups. Results A mixed-effects model using PROC NLMIXED demonstrated that the MCI group exhibited differentially greater difficulty in processing passive than active sentences compared to the normal-aging group. A logistic regression fitted with the PROC LOGISTIC model identified the sum of the passive sentences, with age and education effects as the best models to distinguish individuals with MCI from the normal-aging group. Conclusion Sentence comprehension deficits emerged in the MCI stage when the syntactic complexity was increased. Furthermore, a passive structure was the best predictor for efficiently distinguishing the MCI group from the normal-aging group. These results are clinically and theoretically important, given that linguistic complexity can serve as a critical behavioral marker in the detection of early symptoms associated with linguistic-cognitive decline.

Analysis of microarray right-censored data through fused sliced inverse regression.

Sufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits. Recently, a fused approach for SIR is proposed to relieve this weakness, which fuses the kernel matrices computed by the SIR application from various numbers of slices. In the paper, the fused SIR is applied to a large-p-small n regression of a high-dimensional microarray right-censored data to show its practical advantage over usual SIR application. Through model validation, it is confirmed that the fused SIR outperforms the SIR with any number of slices under consideration.

Modeling microRNA-mRNA interactions using PLS regression in human colon cancer.

BACKGROUND: Changes in microRNA (miRNA) expression patterns have been extensively characterized in several cancers, including human colon cancer. However, how these miRNAs and their putative mRNA targets contribute to the etiology of cancer is poorly understood. In this work, a bioinformatics computational approach with miRNA and mRNA expression data was used to identify the putative targets of miRNAs and to construct association networks between miRNAs and mRNAs to gain some insights into the underlined molecular mechanisms of human colon cancer. METHOD: The miRNA and mRNA microarray expression profiles from the same tissues including 7 human colon tumor tissues and 4 normal tissues, collected by the Broad Institute, were used to identify significant associations between miRNA and mRNA. We applied the partial least square (PLS) regression method and bootstrap based statistical tests to the joint expression profiles of differentially expressed miRNAs and mRNAs. From this analysis, we predicted putative miRNA targets and association networks between miRNAs and mRNAs. Pathway analysis was employed to identify biological processes related to these miRNAs and their associated predicted mRNA targets. RESULTS: Most significantly associated up-regulated mRNAs with a down-regulated miRNA identified by the proposed methodology were considered to be the miRNA targets. On average, approximately 16.5% and 11.0% of targets predicted by this approach were also predicted as targets by the common prediction algorithms TargetScan and miRanda, respectively. We demonstrated that our method detects more targets than a simple correlation based association. Integrative mRNA:miRNA predictive networks from our analysis were constructed with the aid of Cytoscape software. Pathway analysis validated the miRNAs through their predicted targets that may be involved in cancer-associated biological networks. CONCLUSION: We have identified an alternative bioinformatics approach for predicting miRNA targets in human colon cancer and for reverse engineering the miRNA:mRNA network using inversely related mRNA and miRNA joint expression profiles. We demonstrated the superiority of our predictive method compared to the correlation based target prediction algorithm through a simulation study. We anticipate that the unique miRNA targets predicted by the proposed method will advance the understanding of the molecular mechanism of colon cancer and will suggest novel therapeutic targets after further experimental validations.

Marginalized random effects models for multivariate longitudinal binary data.

Generalized linear models with random effects are often used to explain the serial dependence of longitudinal categorical data. Marginalized random effects models (MREMs) permit likelihood-based estimations of marginal mean parameters and also explain the serial dependence of longitudinal data. In this paper, we extend the MREM to accommodate multivariate longitudinal binary data using a new covariance matrix with a Kronecker decomposition, which easily explains both the serial dependence and time-specific response correlation. A maximum marginal likelihood estimation is proposed utilizing a quasi-Newton algorithm with quasi-Monte Carlo integration of the random effects. Our approach is applied to analyze metabolic syndrome data from the Korean Genomic Epidemiology Study for Korean adults.