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Summary: Background: Medications prescribed for chronic diseases can lead to short-term neuropsychiatric symptoms, but their long-term effects on brain structures and psychiatric conditions remain unclear. Methods: We comprehensively analyzed the FDA Adverse Event Reporting System database and conducted drug target Mendelian Randomization (MR) studies on six categories of common drugs, 477 brain imaging-derived phenotypes (IDPs) and eight psychiatric disorders. Genetic instruments were extracted from expression quantitative trait loci (eQTLs) in blood, brain, and other target tissues, protein quantitative trait loci (pQTLs) in blood, and genome-wide association studies (GWAS) of hemoglobin and cholesterol. Summary statistics for brain IDPs, psychiatric disorders, and gut microbiome were obtained from the BIG40, Psychiatric Genomics Consortium, and MiBioGen. A two-step MR and mediation analysis were employed to screen possible mediators of drug-IDP effects from 119 gut microbiota genera and identify their mediation proportions. Findings: Among 19 drug classes, six drugs were found to be associated with higher risks of psychiatric adverse events, while 11 drugs were associated with higher risks of gastrointestinal adverse events in the FAERS analysis. We identified ten drug-psychiatric disorder associations, 202 drug-IDP associations, 16 drug-microbiota associations, and four drug-microbiota-IDP causal links. For example, PPARG activation mediated HbA1c reduction caused a higher risk of bipolar disorder (BD) II. Genetically proxied GLP-1R agonists were significantly associated with an increase in the volume of the CA3-head of the right hippocampus and the area of the left precuneus cortex, both of which have been shown to correlate with cognition in previous studies. Interpretation: Common drugs may affect brain structure and risk of psychiatric disorder. Oral medications in particular may exert some of these effects by influencing gut microbiota. This study calls for greater attention to be paid to the neuropsychiatric adverse effects of drugs and encourages drug repurposing. Funding: National Natural Science Foundation of China (grant No. 82330035, 82130043, 82172685, and 82001223), National Natural Science Foundation of Hunan Province (grant No. 2021SK1010), and the Science Foundation for Distinguished Young Scholars of Changsha (grant No. kq2209006).

State estimation is a critical functionality of energy management system (EMS) to provide power system states in real-time operations. However, problems such as failure to converge, prone to failure during contingencies, and biased estimates while system is under stressed condition occur so that state estimation results may not be reliable. The unreliable results further impact downstream network and market applications, such as contingency analysis, voltage stability analysis, transient stability analysis, system alarming, and unit commitment. Thus, operators may lose the awareness of system condition in EMS. This paper proposes a fully independent and one-of-a-kind system by integrating linear state estimator into situational awareness applications based on real-time synchrophasor data. With guaranteed and accurate state estimation solution and advanced real-time data analytic and monitoring functionalities, the system is capable of assisting operators to assess and diagnose current system conditions for proactive and necessary corrective actions. The architecture, building components, and implementation of the proposed system are explored in detail. Two case studies with simulated data from the subsystems of Electric Reliability Council of Texas (ERCOT) and Los Angeles Department of Water and Power (LADWP) are presented. The test results show the effectiveness and reliability of the system, and its value for realtime power system operations.