The Promises of Parallel Outcomes

Unobserved confounding presents a major threat to the validity of causal inference from observational studies. In this paper, we introduce a novel framework that leverages the information in multiple parallel outcomes for identification and estimation of causal effects. Under a conditional independence structure among multiple parallel outcomes, we achieve nonparametric identification with at least three parallel outcomes. We further show that under a set of linear structural equation models, causal inference is possible with two parallel outcomes. We develop accompanying estimating procedures and evaluate their finite sample performance through simulation studies and a data application studying the causal effect of the tau protein level on various types of behavioral deficits

A review of interactive narrative systems and technologies: a training perspective

As an emerging form of digital entertainment, interactive narrative has attracted great attention of researchers over the past decade. Recently, there is an emerging trend to apply interactive narrative for training and simulation. An interactive narrative system allows players to proactively interact with simulated entities in a virtual world and have the ability to alter the progression of a storyline. In simulation-based training, the use of an interactive narrative system enables the possibility to offer engaging, diverse and personalized narratives or scenarios for different training purposes. This paper provides a review of interactive narrative systems and technologies from a training perspective. Specifically, we first propose a set of key requirements in developing interactive narrative systems for simulation-based training. Then we review nine representative existing systems with respect to their system architectures, features and related mechanisms. To examine their applicability to training, we investigate and compare the reviewed systems based on the functionalities and modules that support the proposed requirements. Furthermore, we discuss some open research issues on future development of interactive narrative technologies for training applications

A New Regularized Matrix Discriminant Analysis (R-MDA) Enabled Human-Centered EEG Monitoring Systems

The wider use of wearable devices for electroencephalogram (EEG) data capturing providesa very useful way for the monitoring and self-management of human health. However, the large volumesof data with high dimensions cause computational complexity in EEG data processing and pose a greatchallenge to the use of wearable EEG devices in healthcare. This paper proposes a new approach to extract thestructural information of EEG data and tackle the curse of dimensionality of the EEG data. A set of methodsfor dimensionality reduction (DR)-like linear discriminant analysis (LDA) and their improved methodshave been developed for EEG processing in the literature. However, the existing LDA-related methodssuffer from the singularity problem or expensive computational cost, and none of existing methods takeinto consideration the structure of the projection matrix, which is crucial for the extraction of the structuralinformation of the EEG data. In this paper, a new method called a regularized matrix discriminant analysis(R-MDA) is proposed for EEG feature representation and DR. In the R-MDA, the EEG data are representedas a data matrix, and projection vectors are reshaped to be a set of projection matrices stacking together. Byreformulating the LDA as a least-square formulation and imposing specified constraint on each projectionmatrix, the new R-MDA has been constructed to effectively reduce EEG dimensions and capturing thestructural information of the EEG data. Experimental results demonstrate that this new R-MDA outperformsthe existing LDA-related methods, including achieving improved accuracy with significant DR of the EEGdata. This offers an effective way to enable wearable EEG devices be applicable in human-centered healthmonitorin

A retrospective comparative study on the diagnostic efficacy and the complications: between CassiII rotational core biopsy and core needle biopsy

Accurate pathologic diagnosis and molecular classification of breast mass biopsy tissue is important for determining individualized therapy for (neo)adjuvant systemic therapies for invasive breast cancer. The CassiII rotational core biopsy system is a novel biopsy technique with a guide needle and a “stick-freeze” technology. The comprehensive assessments including the concordance rates of diagnosis and biomarker status between CassiII and core needle biopsy were evaluated in this study. Estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed through immunohistochemistry. In total, 655 patients with breast cancer who underwent surgery after biopsy at Sir Run Run Shaw Hospital between January 2019 to December 2021 were evaluated. The concordance rates (CRs) of malignant surgical specimens with CassiII needle biopsy was significantly high compared with core needle biopsy. Moreover, CassiII needle biopsy had about 20% improvement in sensitivity and about 5% improvement in positive predictive value compared to Core needle biopsy. The characteristics including age and tumor size were identified the risk factors for pathological inconsistencies with core needle biopsies. However, CassiII needle biopsy was associated with tumor diameter only. The CRs of ER, PgR, HER2, and Ki67 using Cassi needle were 98.08% (kappa, 0.941; p<.001), 90.77% (kappa, 0.812; p<.001), 69.62% (kappa, 0.482; p<.001), and 86.92% (kappa, 0.552; p<.001), respectively. Post-biopsy complications with CassiII needle biopsy were also collected. The complications of CassiII needle biopsy including chest stuffiness, pain and subcutaneous ecchymosis are not rare. The underlying mechanism of subcutaneous congestion or hematoma after CassiII needle biopsy might be the larger needle diameter and the effect of temperature on coagulation function. In summary, CassiII needle biopsy is age-independent and has a better accuracy than CNB for distinguishing carcinoma in situ and invasive carcinoma