Identifying Urban Sources as Cause of Elevated Grass Pollen Concentrations using GIS and Remote Sensing

We examine here the hypothesis that during flowering, the grass pollen concentrations at a specific site reflect the distribution of grass pollen sources within a few kilometres of this site.We perform this analysis on data from a measurement campaign in the city of Aarhus (Denmark) using three pollen traps and by comparing these observations with a novel inventory of grass pollen sources. The source inventory is based on a new methodology developed for urbanscale grass pollen sources. The new methodology is believed to be generally applicable for the European area, as it relies on commonly available remote sensing data combined with management information for local grass areas. The inventory has identified a number of grass pollen source areas present within the city domain. The comparison of the measured pollen concentrations with the inventory shows that the atmospheric concentrations of grass pollen in the urban zone reflect the source areas identified in the inventory, and that the pollen sources that are found to affect the pollen levels are located near or within the city domain. The results also show that during days with peak levels of pollen concentrations there is no correlation between the three urban traps and an operational trap located just 60 km away. This finding suggests that during intense flowering, the grass pollen concentration mirrors the local source distribution and is thus a local-scale phenomenon. Model simulations aimed at assessing population exposure to pollen levels are therefore recommended to take into account both local sources and local atmospheric transport, and not to rely only on describing regional to long-range transport of pollen. The derived pollen source inventory can be entered into local-scale atmospheric transport models in combination with other components that simulate pollen release in order to calculate urban-scale variations in the grass pollen load. The gridded inventory with a resolution of 14m is therefore made available as supplementary material to this paper, and the verifying grass pollen observations are additionally available in tabular form

Digital support interventions for the self-management of low back pain: a systematic review

Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health. Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively. Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm. Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak

Definition of Fiducial Points in the Normal Seismocardiogram

Abstract The purpose of this work is to define fiducial points in the seismocardiogram (SCG) and to correlate them with physiological events identified in ultrasound images. For 45 healthy subjects the SCG and the electrocardiogram (ECG) were recorded simultaneously at rest. Immediately following the SCG and ECG recordings ultrasound images of the heart were also obtained at rest. For all subjects a mean SCG signal was calculated and all fiducial points (peaks and valleys) were identified and labeled in the same way across all signals. Eight physiologic events, including the valve openings and closings, were annotated from ultrasound as well and the fiducial points were correlated with those physiologic events. A total of 42 SCG signals were used in the data analysis. The smallest mean differences (±SD) between the eight events found in the ultrasound images and the fiducial points, together with their correlation coefficients (r) were: atrial systolic onset: −2 (±16) ms, r = 0.75 (p < 0.001); peak atrial inflow: 13 (±19) ms, r = 0.63 (p < 0.001); mitral valve closure: 4 (±11) ms, r = 0.71 (p < 0.01); aortic valve opening: −3 (±11) ms, r = 0.60 (p < 0.001); peak systolic inflow: 13 (±23) ms, r = 0.42 (p < 0.01); aortic valve closure: −5 (±12) ms, r = 0.94 (p < 0.001); mitral valve opening: −7 (±19) ms, r = 0.87 (p < 0.001) and peak early ventricular filling: −18 (±28 ms), r = 0.79 (p < 0.001). In conclusion eight physiologic events characterizeing the cardiac cycle, are associated with reproducible, well-defined fiducial points in the SCG

Architecture of the type IVa pilus machine

Many bacteria, including important pathogens, move by projecting grappling-hook–like extensions called type IV pili from their cell bodies. After these pili attach to other cells or objects in their environment, the bacteria retract the pili to pull themselves forward. Chang et al. used electron cryotomography of intact cells to image the protein machines that extend and retract the pili, revealing where each protein component resides. Putting the known structures of the individual proteins in place like pieces of a three-dimensional puzzle revealed insights into how the machine works, including evidence that ATP hydrolysis by cytoplasmic motors rotates a membrane-embedded adaptor that slips pilin subunits back and forth from the membrane onto the pilus