ALE: Additive Latent Effect Models for Grade Prediction

The past decade has seen a growth in the development and deployment of educational technologies for assisting college-going students in choosing majors, selecting courses and acquiring feedback based on past academic performance. Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e.g., next term). Accurate and timely prediction of students' academic grades is important for developing effective degree planners and early warning systems, and ultimately improving educational outcomes. Existing grade prediction methods mostly focus on modeling the knowledge components associated with each course and student, and often overlook other factors such as the difficulty of each knowledge component, course instructors, student interest, capabilities and effort. In this paper, we propose additive latent effect models that incorporate these factors to predict the student next-term grades. Specifically, the proposed models take into account four factors: (i) student's academic level, (ii) course instructors, (iii) student global latent factor, and (iv) latent knowledge factors. We compared the new models with several state-of-the-art methods on students of various characteristics (e.g., whether a student transferred in or not). The experimental results demonstrate that the proposed methods significantly outperform the baselines on grade prediction problem. Moreover, we perform a thorough analysis on the importance of different factors and how these factors can practically assist students in course selection, and finally improve their academic performance

Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of test cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in

Responses of global terrestrial evapotranspiration to climate change and increasing atmospheric CO2 in the 21st century

Quantifying the spatial and temporal patterns of the water lost to the atmosphere through land surface evapotranspiration (ET) is essential for understanding the global hydrological cycle, but remains much uncertain. In this study, we use the Dynamic Land Ecosystem Model to estimate the global terrestrial ET during 2000-2009 and project its changes in response to climate change and increasing atmospheric CO2 under two IPCC SRES scenarios (A2 and B1) during 2010-2099. Modeled results show a mean annual global terrestrial ET of about 549 (545-552) mm yr(-1) during 2000-2009. Relative to the 2000s, global terrestrial ET for the 2090s would increase by 30.7 mm yr(-1) (5.6%) and 13.2 mm yr(-1) (2.4%) under the A2 and B1 scenarios, respectively. About 60% of global land area would experience increasing ET at rates of over 9.5 mm decade(-1) over the study period under the A2 scenario. The Arctic region would have the largest ET increase (16% compared with the 2000s level) due to larger increase in temperature than other regions. Decreased ET would mainly take place in regions like central and western Asia, northern Africa, Australia, eastern South America, and Greenland due to declines in soil moisture and changing rainfall patterns. Our results indicate that warming temperature and increasing precipitation would result in large increase in ET by the end of the 21st century, while increasing atmospheric CO2 would be responsible for decrease in ET, given the reduction of stomatal conductance under elevated CO2.PublishedYe

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Research Progress on Bioactivity and Mechanism of Tea Polyphenols

Tea polyphenols are a class of polyphenolic mixtures with phenolic hydroxyl structure in tea plants, which are the main functional component of tea, and the content is relatively high in green tea. Tea polyphenol bioactivity research gains popularity, in the functional food, drug development, preservation and preservation of preservatives and other areas with broad application prospects. Tea polyphenols have a variety of biological activities such as antioxidant, anticancer, hypolipidemic, blood sugar regulation, antibacterial, anti-radiation and so on. Their mechanism of action mainly includes regulating protein kinase B (AKT), nuclear factor-kappa B (NF-κB), epithelial growth factor receptor (EGFR), adenylate activated protein kinase (AMPK) and other signalling pathways and related proteins. By analyzing the relevant research literature in recent years, we review the material properties, biological activities, mechanisms and applications of tea polyphenols, with a view to providing reference for the development of tea polyphenol-containing functional foods and natural medicines

Association Between Viral Infections and Glioma Risk: A Two-Sample Bidirectional Mendelian Randomization Analysis

Background: Glioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma. Methods: We conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental variables to estimate the causal relationship between glioma and twelve types of viral infections from corresponding GWAS data. In addition, sensitivity analyses were performed. Results: After correcting for multiple tests and sensitivity analysis, we detected that genetically predicted herpes zoster (caused by Varicella zoster virus (VZV) infection) significantly decreased risk of low-grade glioma (LGG) development (OR = 0.85, 95% CI: 0.76-0.96, P = 0.01, FDR = 0.04). No causal effects of the other eleven viral infections on glioma and reverse causality were detected. Conclusions: This is one of the first and largest studies in this field. We show robust evidence supporting that genetically predicted herpes zoster caused by VZV infection reduces risk of LGG. The findings of our research advance understanding of the etiology of glioma