Explain Variance of Prediction in Variational Time Series Models for Clinical Deterioration Prediction

Missingness and measurement frequency are two sides of the same coin. How frequent should we measure clinical variables and conduct laboratory tests? It depends on many factors such as the stability of patient conditions, diagnostic process, treatment plan and measurement costs. The utility of measurements varies disease by disease, patient by patient. In this study we propose a novel view of clinical variable measurement frequency from a predictive modeling perspective, namely the measurements of clinical variables reduce uncertainty in model predictions. To achieve this goal, we propose variance SHAP with variational time series models, an application of Shapley Additive Expanation(SHAP) algorithm to attribute epistemic prediction uncertainty. The prediction variance is estimated by sampling the conditional hidden space in variational models and can be approximated deterministically by delta's method. This approach works with variational time series models such as variational recurrent neural networks and variational transformers. Since SHAP values are additive, the variance SHAP of binary data imputation masks can be directly interpreted as the contribution to prediction variance by measurements. We tested our ideas on a public ICU dataset with deterioration prediction task and study the relation between variance SHAP and measurement time intervals

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-driven Approach

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes integer interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G

Text-guided Eyeglasses Manipulation with Spatial Constraints

Virtual try-on of eyeglasses involves placing eyeglasses of different shapes and styles onto a face image without physically trying them on. While existing methods have shown impressive results, the variety of eyeglasses styles is limited and the interactions are not always intuitive or efficient. To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively. Specifically, we introduce a mask encoder to extract mask conditions and a modulation module that enables simultaneous injection of text and mask conditions. This design allows for fine-grained control of the eyeglasses' appearance based on both textual descriptions and spatial constraints. Our approach includes a disentangled mapper and a decoupling strategy that preserves irrelevant areas, resulting in better local editing. We employ a two-stage training scheme to handle the different convergence speeds of the various modality conditions, successfully controlling both the shape and style of eyeglasses. Extensive comparison experiments and ablation analyses demonstrate the effectiveness of our approach in achieving diverse eyeglasses styles while preserving irrelevant areas.Comment: Revised version: add some experiment

The Distance Between Exons and Alu Elements Influences RNA Circularization Efficiency

Circular RNA (circRNA) is a category of RNA that is created when the spliceosome back-splices an exon, thereby forming an RNA covalent circle. A few circRNAs have been shown to have regulatory functions, but the functions of most circRNAs are not known. Previous studies have demonstrated that repetitive elements flanking the exon(s), such as Alu elements, facilitate circularization, and have identified the minimal size of repetitive elements needed to drive circularization. We studied how the distance between exon splice donors/acceptors and Alu elements affects the efficiency of RNA circularization. To create the distance gradient, we inserted and/or deleted sequences between the splice donors/acceptor and Alu elements. We engineered the circular RNA so that it would express GFP after circularization. To measure the circularization efficiency, we conducted Western blots and Northern blots on the proteins and RNA harvested from cells. We showed that in HEK293 cells the distance of the repetitive element upstream of the exon has a large effect on circularization, while the distance downstream has little effect. Combining these observations, we created a minimal construct that can be circularized efficiently and expressed much more protein than our original construct. Overall, our study further contributed to the understanding of the cis elements that affect circular RNA formation in vivo, and design of vectors to efficiently express proteins from very stable RNAs.Bachelor of Scienc

A BIBLIOMETRIC ANALYSIS OF RESEARCH PUBLICATIONS THAT ADOPTED THE MEDICAL EXPENDITURE PANEL SURVEY (MEPS) DATA

BackgroundThe Medical Expenditure Panel Survey (MEPS) is a large publicly available information source provided by the Agency of Healthcare Research and Quality (AHRQ). Since 1996, AHRQ has been taking samples of households throughout the United States, and has provided an unparalleled degree of details about the use and payment of health care services. Although more than 1900 publications have used data from the MEPS dataset in the past two decades, it is still lack of an overview on the research effort that MEPS data have supported. The purpose of this study is to systematically survey the research publications that adopted MEPS as the sole or one of the datasets by a bibliometric approach. The findings meant to provide insights to policy makers, grant agencies, and researchers in terms of data usage patterns as well as organization, funding, publishing, and collaborations.Methods and Materials Two databases, PubMed and Web of Science (WoS) were used to search for peer-reviewed articles from 1996 to 2018 that adopted the MEPS data. The title and abstract fields of each retrieved publication were screened in Covidence1 based on a set of inclusion and exclusion criteria. The full citation records of the included publications were obtained by matching records in WoS using PMID, DOI, and title fields. WoS analytics, Microsoft Excel, and VoSviewer2 were utilized for quantitative and bibliometric network analysis, including productivity, research categories and topic clustering, journal sources, other datasets co-used with MEPS, funding sources, and co-authorship (i.e., organizational and country collaboration).Results A total of 1953 publications from 1996 to 2018 were included in this study. The publications that adopted MEPS data grew steadily with an average of 75 articles per year and reached a peak in 2017. The top research categories were healthcare services, public environmental occupational health, health policy, and internal medicine. In addition, 235 key terms were extracted from the title and abstracts of the included publications and formed three major topic clusters (i.e., health quality and cost, healthcare accessibility and disparity, child and family care). Additional 24 datasets were identified that were frequently co-used with MEPS by the included studies such as Nationwide Inpatient Sample and Truven MarketScan. National Institute of Health was the top grant agency for the included studies. More than 3200 authors from 25 countries and 1074 institutions or organizations contributed to the included publications. ConclusionsAlthough MEPS focuses on the representative samples of domestic households and researchers in the U.S. contributed the most to the included publications, the MEPS data have inspired cross-country collaborations. Our results illustrated the broad landscape of research effort that MEPS data have supported and substantiated the value of AHRQ’s effort of providing the MEPS data to the public. The findings will help stakeholders with informed decision making.References1.Better systematic review management Internet. Covidence. cited 2019Mar13. Available from: https://www.covidence.org/2.Eck NJV, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2009;84(2):523-38Bachelor of Scienc