Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Epidemics, Public Sentiment, and Infectious Disease Equity Market Volatility.

Background: This article studies the relationship between the COVID-19 epidemic, public sentiment, and the volatility of infectious disease equities from the perspective of the United States. We use weekly data from January 3, 2020 to March 7, 2021. This provides a sufficient dataset for empirical analysis. Granger causality test results prove the two-way relationship between the fluctuation of infectious disease equities and confirmed cases. In addition, confirmed cases will cause the public to search for COVID-19 tests, and COVID-19 tests will also cause fluctuations in infectious disease equities, but there is no reverse correlation. The results of this research are useful to investors and policy makers. Investors can use the number of confirmed cases to predict the volatility of infectious disease equities. Similarly, policy makers can use the intervention of retrieved information to stabilize public sentiment and equity market fluctuations, and integrate a variety of information to make more scientific judgments on the trends of the epidemic.

The impact of economic complexity on energy demand in OECD countries.

This paper investigates the role of economic complexity on energy demand using the panel dataset of 25 Organization for Economic Co-operation and Development (OECD) countries from 1978 to 2016. Both real per capita income level and economy-wide real energy price index are critical determinants in energy demand modeling. The battery of the cross-sectional dependency test proposed by Pesaran (2004 and 2007) is used, signaling the presence of cross-sectional dependency in the dataset. Thus, the Westerlund (2007) cointegration test is also used, revealing the long-run relationship between the series. Moreover, the results from using the Augmented Mean Group (AMG) estimations illustrate that real per capita income level positively affects energy demand while real energy price and economic complexity negatively influence on it. From a policy perspective, we suggest increasing technological innovation (i.e., higher economic complexity) will reduce the energy demand. The reduction of massive energy usage may be beneficial for the natural environment's health in the OECD countries.