Differential Item Functioning in Performance Assessments: A Comparison of Three Procedures.

As performance assessments grow in popularity, it becomes increasingly important to investigate the effect of such assessments on various population subgroups. The purpose of this study was to investigate the relative empirical power of three popular statistical procedures (an extension of the generalized Mantel-Haenszel procedure, Logistic Discriminant Function Analysis, and a combined t-test procedure) in identifying polytomously scored items that function differentially for two subgroups of examinees. In the Monte Carlo study computer simulations were conducted to study the behavior of these procedures for identifying items exhibiting varying degrees of differential-item functioning (DIF). Each statistic was converted to a probability value to examine the number of times that the method rejected an item at the.05 levels. The results, based on simulated twenty-four conditions, each replicated 50 times, indicate a preference for the logistic discriminant function analysis (LDFA) procedure for DIF identification in polytomously scored items. The effects of the number of DIF items on the matching variable seem significant for identifying DIF in performance assessment. The effect was stronger for detecting uniform DIF than for identifying nonuniform DIF. Based on the findings of the study, the following conclusions were drawn: (1) For DIF analysis in performance assessments, the LDFA can be recommended as the preferred method to test constructors or practitioners. (2) Through using the LDFA for identifying DIF in performance assessments, the appropriateness in test usage for different subgroups will be enlarged. (3) The effects of the number of DIF items on the matching variable seem significant for identifying DIF in performance assessment. Thus, in order to decrease the effects of the proportion of DIF items on the matching variable, it is recommended to emphasize the judgmental analysis to evaluate biased items in a test before entering DIF analysis. Finally, the statistics should be interpreted with caution. Although DIF analysis is essential for the appropriateness of test use that is related to subgroups influenced by testing, DIF analysis is only one component of the extensive research for the validity and fairness of performance assessment

Chromaticity of Gravitational Microlensing Events

In this paper, we investigate the color changes of gravitational microlensing events caused by the two different mechanisms of differential amplification for a limb-darkened extended source and blending. From this investigation, we find that the color changes of limb-darkened extended source events (color curves) have dramatically different characteristics depending on whether the lens transits the source star or not. We show that for a source transit event, the lens proper motion can be determined by simply measuring the turning time of the color curve instead of fitting the overall color or light curves. We also find that even for a very small fraction of blended light, the color changes induced by the blending effect is equivalent to those caused by the limb-darkening effect, causing serious distortion in the observed color curve. Therefore, to obtain useful information about the lens and source star from the color curve of a limb-darkened extended source event, it will be essential to eliminate or correct for the blending effect. We discuss about the methods for the efficient correction of the blending effect.Comment: total 18 pages, including 5 figures and no table, MNRAS, submitte

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

The conventional CNN, widely used for two-dimensional images, however, is not directly applicable to non-regular geometric surface, such as a cortical thickness. We propose Geometric CNN (gCNN) that deals with data representation over a spherical surface and renders pattern recognition in a multi-shell mesh structure. The classification accuracy for sex was significantly higher than that of SVM and image based CNN. It only uses MRI thickness data to classify gender but this method can expand to classify disease from other MRI or fMRI dataComment: 29 page

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications

PD-1 deficiency protects experimental colitis via alteration of gut microbiota

Programmed cell death-1 (PD-1) is a coinhibitory molecule and plays a pivotal role in immune regulation. Here, we demonstrate a role for PD-1 in pathogenesis of inflammatory bowel disease (IBD). Wild-type (WT) mice had severe wasting disease during experimentally induced colitis, while mice deficient for PD-1 (PD-1(-/-)) did not develop colon inflammation. Interestingly, PD-1(-/-) mice cohoused with WT mice became susceptible to colitis, suggesting that resistance of PD-1(-/-) mice to colitis is dependent on their gut microbiota. 16S rRNA gene-pyrosequencing analysis showed that PD-1(-/-) mice had altered composition of gut microbiota with significant reduction in Rikenellaceae family. These altered colon bacteria of PD-1(-/-) mice induced less amount of inflammatory mediators from colon epithelial cells, including interleukin (IL)-6, and inflammatory chemokines. Taken together, our study indicates that PD-1 expression is involved in the resistance to experimental colitis through altered bacterial communities of colon.112Ysciescopuskc

SoEasy: A Software Framework for Easy Hardware Control Programming for Diverse IoT Platforms

Many Internet of Things (IoT) applications are emerging and evolving rapidly thanks to widespread open-source hardware platforms. Most of the high-end open-source IoT platforms include built-in peripherals, such as the universal asynchronous receiver and transmitter (UART), pulse width modulation (PWM), general purpose input output (GPIO) ports and timers, and have enough computation power to run embedded operating systems such as Linux. However, each IoT platform has its own way of configuring peripherals, and it is difficult for programmers or users to configure the same peripheral on a different platform. Although diverse open-source IoT platforms are widespread, the difficulty in programming those platforms hinders the growth of IoT applications. Therefore, we propose an easy and convenient way to program and configure the operation of each peripheral using a user-friendly Web-based software framework. Through the implementation of the software framework and the real mobile robot application development along with it, we show the feasibility of the proposed software framework, named SoEasy

Incremental economic burden associated with exudative age-related macular degeneration: a population-based study

Background The exudative age-related macular degeneration (AMD) causes considerable healthcare costs for patients and healthcare system, which are expected to grow as the population ages. The objective of this study was to assess the incremental economic burden of exudative AMD by comparing total healthcare costs between the exudative AMD group and non-AMD group to understand economic burden related to exudative AMD. Methods This retrospective cohort study used the National Health Insurance Service database including the entire Korean population. Exudative AMD group included individuals with at least one claim for ranibizumab and one claim using the registration code for exudative AMD (V201). Non-AMD group was defined as individuals without any claims regarding the diagnostic code of H35.3 or ranibizumab. The exudative AMD group and non-AMD group were matched using a propensity-score model. Incremental healthcare resource utilization and healthcare costs were measured during a one-year follow-up by employing econometric models: ordinary least squares (OLS) with log transformation and heteroscedastic retransformation; and generalized linear model (GLM) with a log link function and gamma distribution. Results A total of 7119 exudative AMD patients were matched to 7119 non-AMD patients. The number of outpatient visits was higher in the exudative AMD group (P-value < 0.0001), while the length of hospitalization was shorter in exudative AMD group (P-value < 0.0001). Exudative AMD patients had total costs 2.13 times (95%CI, 2.08–2.17) greater than non-AMD group using OLS, and total costs 4.06 times (95%CI, 3.82–4.31) greater than non-AMD group using GLM. Annual incremental total costs were estimated as $5519 (OLS) and$3699 (GLM). Conclusions Exudative AMD was associated with significantly increased healthcare costs compared to the non-AMD group. Attention is needed to manage the socioeconomic burden of exudative AMD.This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (grant number: NRF-2016R1C1B1009198); and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0373). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Corrigendum: Behavioral and Neuroimaging Evidence for Facial Emotion Recognition in Elderly Korean Adults with Mild Cognitive Impairment, Alzheimer's Disease, and Frontotemporal Dementia

Background: Facial emotion recognition (FER) is impaired in individuals with frontotemporal dementia (FTD) and Alzheimer’s disease (AD) when compared to healthy older adults. Since deficits in emotion recognition are closely related to caregiver burden or social interactions, researchers have fundamental interest in FER performance in patients with dementia.Purpose: The purpose of this study was to identify the performance profiles of six facial emotions (i.e., fear, anger, disgust, sadness, surprise, and happiness) and neutral faces measured among Korean healthy control (HCs), and those with mild cognitive impairment (MCI), AD, and FTD. Additionally, the neuroanatomical correlates of facial emotions were investigated.Methods: A total of 110 (33 HC, 32 MCI, 32 AD, 13 FTD) older adult participants were recruited from two different medical centers in metropolitan areas of South Korea. These individuals underwent an FER test that was used to assess the recognition of emotions or absence of emotion (neutral) in 35 facial stimuli. Repeated measures two-way analyses of variance were used to examine the distinct profiles of emotional recognition among the four groups. We also performed brain imaging and voxel-based morphometry (VBM) on the participants to examine the associations between FER scores and gray matter volume.Results: The mean score of negative emotion recognition (i.e., fear, anger, disgust, and sadness) clearly discriminated FTD participants from individuals with MCI and AD and HC [F(3,106) = 10.829, p 2 = 0.235], whereas the mean score of positive emotion recognition (i.e., surprise and happiness) did not. A VBM analysis showed negative emotions were correlated with gray matter volume of anterior temporal regions, whereas positive emotions were related to gray matter volume of fronto-parietal regions.Conclusion: Impairment of negative FER in patients with FTD is cross-cultural. The discrete neural correlates of FER indicate that emotional recognition processing is a multi-modal system in the brain. Focusing on the negative emotion recognition is a more effective way to discriminate healthy aging, MCI, and AD from FTD in older Korean adults.</p