National Geographic Society, Washington, District of Columbia 20036, USA.
Published: April 2014
AI Article Synopsis
Article Abstract
The search for a method that utilizes biological information to predict humans' place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000-130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS's accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing.
Background: Cognitive bias plays a crucial role in the onset and persistence of depression. Research has established a link between personality traits and cognitive bias; however, most of them focused on the general population, overlooking individuals with depression. We aim to explore the relationship between personality and cognitive bias in patients with major depressive disorder (MDD) and compare the results with healthy controls (HCs) to identify potential patterns.
Background: Foodborne illness is a significant public health concern, particularly in developing countries like Bangladesh. Young adults, aged 18-26 (including undergraduates and recent graduates), are especially vulnerable to the onset of unhealthy eating habits and nutritional imbalances as they begin living independently, often away from their families. This research aims to identify the risk factors associated with the knowledge, attitudes, and practices related to safe food handling among residential university students.
Background: The link between regional body fat distribution and overactive bladder (OAB) in prior epidemiological research has been uncertain. Our objective is to assess the relationship between increased regional body fat and the prevalence of OAB.
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Department of Clinical Research Laboratory, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, No.120 Gui Dan Road, Foshan, 528222, China.
Background: The role of inflammation and hypoproteinemia in influencing outcomes of critically ill patients has been widely recognized. However, there is a paucity of research on the prognostic value of the platelet-to-albumin ratio (PAR) in critically ill patients. Therefore, the aim of this study is to assess the prognostic significance of PAR in this population.
Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.
Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.
