Light profoundly impacts many aspects of human physiology and behaviour, including the synchronization of the circadian clock, the production of melatonin, and cognition. These effects of light, termed the non-visual effects of light, have been primarily investigated in laboratory settings, where light intensity, spectrum and timing can be carefully controlled to draw associations with physiological outcomes of interest. Recently, the increasing availability of wearable light loggers has opened the possibility of studying personal light exposure in free-living conditions where people engage in activities of daily living, yielding findings associating aspects of light exposure and health outcomes, supporting the importance of adequate light exposure at appropriate times for human health. However, comprehensive protocols capturing environmental (e.g., geographical location, season, climate, photoperiod) and individual factors (e.g., culture, personal habits, behaviour, commute type, profession) contributing to the measured light exposure are currently lacking. Here, we present a protocol that combines smartphone-based experience sampling (experience sampling implementing Karolinska Sleepiness Scale, KSS ratings) and high-quality light exposure data collection at three body sites (near-corneal plane between the two eyes mounted on spectacle, neck-worn pendant/badge, and wrist-worn watch-like design) to capture daily factors related to individuals’ light exposure. We will implement the protocol in an international multi-centre study to investigate the environmental and socio-cultural factors influencing light exposure patterns in Germany, Ghana, Netherlands, Spain, Sweden, and Turkey (minimum n = 15, target n = 30 per site, minimum n = 90, target n = 180 across all sites). With the resulting dataset, lifestyle and context-specific factors that contribute to healthy light exposure will be identified. This information is essential in designing effective public health interventions.
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Introduction: Besides dyspnoea and cough, patients with idiopathic pulmonary fibrosis (IPF) or sarcoidosis may experience distressing non-respiratory symptoms, such as fatigue or muscle weakness. However, whether and to what extent symptom burden differs between patients with IPF or sarcoidosis and individuals without respiratory disease remains currently unknown. Objectives: To study the respiratory and non-respiratory burden of multiple symptoms in patients with IPF or sarcoidosis and to compare the symptom burden with individuals without impaired spirometric values, FVC and FEV1 (controls). Methods: Demographics and symptoms were assessed in 59 patients with IPF, 60 patients with sarcoidosis and 118 controls (age ≥18 years). Patients with either condition were matched to controls by sex and age. Severity of 14 symptoms was assessed using a Visual Analogue Scale. Results: 44 patients with IPF (77.3% male; age 70.6±5.5 years) and 44 matched controls, and 45 patients with sarcoidosis (48.9% male; age 58.1±8.6 year) and 45 matched controls were analyzed. Patients with IPF scored higher on 11 symptoms compared to controls (p<0.05), with the largest differences for dyspnoea, cough, fatigue, muscle weakness and insomnia. Patients with sarcoidosis scored higher on all 14 symptoms (p<0.05), with the largest differences for dyspnoea, fatigue, cough, muscle weakness, insomnia, pain, itch, thirst, micturition (night, day). Conclusions: Generally, respiratory and non-respiratory symptom burden is significantly higher in patients with IPF or sarcoidosis compared to controls. This emphasizes the importance of awareness for respiratory and non-respiratory symptom burden in IPF or sarcoidosis and the need for additional research to study the underlying mechanisms and subsequent interventions.
DOCUMENT
Humans use metaphors in thinking. Most metaphors are visual. In processing information stimuli the mind depends partly on visual codes. Information is processed and stored through two channels: one for non-verbal information and another for verbal information. The two different areas of information in the brain are interconnected. The information is stored in patterns that form an inner representation of how individuals perceive their reality and their self. The active processing of new information, remembering and the self-image are related phenomena, that influence each other, sometimes leading to biased interpretation or even reconstruction of contents in each of these areas. Imagination, expectations and anticipations of the future and memories are the more active manifestations of this process. In this process mimesis plays an important role. Mimesis is the imitation of reality in play, story-telling or creating images of how things should look like in the future. Through mimesis people can anticipate on roles in social life, or appropriate experiences from someone else and relate them to one’s own life story. When this happens the information is related to the self through processes of association and becomes ‘Erfahrung’.
DOCUMENT
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is currently used as an important tool for biomedical applications (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this technology and its applications was recently published in a special edition of the Journal of Aerosol Sciences [1]. Even though EHDA is already applied in many different industrial processes, there are not many controlling tools commercially available which can be used to remotely operate the system as well as identify some spray characteristics, e.g. droplet size, operational mode, droplet production ratio. The AECTion project proposes the development of an innovative controlling system based on the electrospray current, signal processing & control and artificial intelligence to build a non-visual tool to control and characterize EHDA processes.
