Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Report on industrial attachment with E-Cube Pte. Ltd

Direct-digital-control (DDC) systems are complex and extensive systems empowered to manage energy in building heating, ventilation and air-conditioning (HVAC) systems. It has resulted in a variety of applications, such as the detection and analysis of faults in HVAC systems, the monitoring of equipment, the verification of energy savings, and the documentation of indoor-air-quality condition

Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is common worldwide and potentially life-threatening; however, many affected individuals remain undiagnosed and untreated. This research aims to innovate on a simple, cost-saving, and reliable approach to diagnose OSA via the acquisition and analysis of snore signals, with an intention to mass screen for OSA. This thesis attempts to achieve the research aim through: (1) the implementation of a robust and user-friendly acquisition system for snore signals, along with recommendations for measurement standards; (2) the development of an advanced wavelet-driven preprocessing system that efficiently integrates both snore signal enhancement and snore activity detection; (3) the identification of effective snore-based OSA diagnostic markers, including formant frequencies (82.5–100% sensitivity, 82.0–95.0% specificity), wavelet bicoherence peaks (82.5–100% sensitivity, 83.3–100% specificity), and psychoacoustic metrics (72.0–78.0% sensitivity, 91.2–92.0% specificity), which accurately classify apneic and benign snores in same- and both-gender patient groups (p-value < 0.0001); (4) the formulation of regression models that are indicative of OSA severity; (5) the investigation of physiological-anatomical-acoustical relationships of snores via source-filter modeling; and (6) the successful generation of natural-sounding synthetic snores using a novel snore source flow model.DOCTOR OF PHILOSOPHY (EEE

Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea?

Objective: To study the feasibility of using acoustic signatures in snore signals for the diagnosis of obstructive sleep apnea (OSA). Methods: Snoring sounds of 30 apneic snorers (24 males; 6 females; apnea-hypopnea index, AHI = 46.9 ± 25.7 events/h) and 10 benign snorers (6 males; 4 females; AHI = 4.6 ± 3.4 events/h) were captured in a sleep laboratory. The recorded snore signals were preprocessed to remove noise, and subsequently, modeled using a linear predictive coding (LPC) technique. Formant frequencies (F1, F2, and F3) were extracted from the LPC spectrum for analysis. The accuracy of this approach was assessed using receiver operating characteristic curves and notched box plots. The relationship between AHI and F1 was further explored via regression analysis. Results: Quantitative differences in formant frequencies between apneic and benign snores are found in same- or both-gender snorers. Apneic snores exhibit higher formant frequencies than benign snores, especially F1, which can be related to the pathology of OSA. This study yields a sensitivity of 88%, a specificity of 82%, and a threshold value of F1 = 470 Hz that best differentiate apneic snorers from benign snorers (both gender combined). Conclusion: Acoustic signatures in snore signals carry information for OSA diagnosis, and snore-based analysis might potentially be a non-invasive and inexpensive diagnostic approach for mass screening of OSA

Blockchain applications in health care for COVID-19 and beyond: a systematic review

10.1016/s2589-7500(21)00210-7The Lancet Digital Health31

Investigation of obstructive sleep apnea using nonlinear mode interations in nonstationary snore signals

Acoustic studies on snoring sounds have recently drawn attention as a potential alternative to polysomnography in the diagnosis of obstructive sleep apnea (OSA). This paper investigates the feasibility of using nonlinear coupling between frequency modes in snore signals via wavelet bicoherence (WBC) analysis for screening of OSA. Two novel markers (PF1 and PSF), which are frequency modes with high nonlinear coupling strength in their respective WBC spectrum, are proposed to differentiate between apneic and benign snores in same- or both-gender snorers. Snoring sounds were recorded from 40 subjects (30 apneic and 10 benign) by a hanging microphone, and subsequently preprocessed within a wavelet transform domain. Forty inspiratory snores (30 as training and 10 as test data) from each subject were examined. Results demonstrate that nonlinear mode interactions in apneic snores are less self-coupled and usually occupy higher and wider frequency ranges than that of benign snores. PF1 and PSF are indicative of apneic and benign snores (p < 0.0001), with optimal thresholds of PF1 = 285 Hz and PSF = 492 Hz (for both genders combined), as well as sensitivity and specificity values between 85.0 and 90.7%, respectively, outperforming the conventional diagnostic indicator (spectral peak frequency, PF = 243-275 Hz, sensitivity = 77.7-79.7%, specificity = 72.0-78.0%, p < 0.0001). Relationships between apnea-hypopnea index and the proposed markers could likely take the functional form of exponential or power. Perspectives on nonlinear dynamics analysis of snore signals are promising for further research and development of a reliable and inexpensive diagnostic tool for OSA