No abstract

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately guage users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.

The human genome encodes approximately 20,000 proteins, many still uncharacterised. It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed a publicly available and customisable “Unknome database” that ranks proteins based on how little is known about them. We applied RNA interference (RNAi) in Drosophila to 260 unknown genes that are conserved between flies and humans. Knockdown of some genes resulted in loss of viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control, and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and 2 genes contributing to male fertility. Our work illustrates the importance of poorly understood genes, provides a resource to accelerate future research, and highlights a need to support database curation to ensure that misannotation does not erode our awareness of our own ignorance.

Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3) is the most common autosomal dominant ataxia. In view of the development of targeted therapies for SCA3, precise knowledge of stage-dependent fluid and MRI biomarker changes is needed. We analyzed cross-sectional data of 292 SCA3 mutation carriers including 57 pre-ataxic individuals, and 108 healthy controls from the European Spinocerebellar ataxia type 3/Machado-Joseph Disease Initiative (ESMI) cohort. Blood concentrations of mutant ATXN3 and neurofilament light (NfL) were determined, and volumes of pons, cerebellar white matter (CWM) and cerebellar grey matter (CGM) were measured on MRI. Mutant ATXN3 concentrations were high before and after ataxia onset, while NfL continuously increased and deviated from normal 11.9 years before onset. Pons and CWM volumes decreased, but the deviation from normal was only 2.0 years (pons) and 0.3 years (CWM) before ataxia onset. We propose a staging model of SCA3 that includes an initial asymptomatic carrier stage followed by the biomarker stage defined by absence of ataxia, but a significant rise of NfL. The biomarker stage leads into the ataxia stage, defined by manifest ataxia. The present analysis provides a robust framework for further studies aiming at elaboration and differentiation of the staging model of SCA3.