Whole-cell 3D STORM reveals interactions between cellular structures with nanometer-scale resolution.

The ability to directly visualize nanoscopic cellular structures and their spatial relationship in all three dimensions will greatly enhance our understanding of molecular processes in cells. Here we demonstrated multicolor three-dimensional (3D) stochastic optical reconstruction microscopy (STORM) as a tool to quantitatively probe cellular structures and their interactions. To facilitate STORM imaging, we generated photoswitchable probes in several distinct colors by covalently linking a photoswitchable cyanine reporter and an activator molecule to assist bioconjugation. We performed 3D localization in conjunction with focal plane scanning and correction for refractive index mismatch to obtain whole-cell images with a spatial resolution of 20-30 nm and 60-70 nm in the lateral and axial dimensions, respectively. Using this approach, we imaged the entire mitochondrial network in fixed monkey kidney BS-C-1 cells, and studied the spatial relationship between mitochondria and microtubules. The 3D STORM images resolved mitochondrial morphologies as well as mitochondria-microtubule contacts that were obscured in conventional fluorescence images

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Quasar and galaxy classification using Gaia EDR3 and CatWise2020

In this work, we assess the combined use of Gaia photometry and astrometry with infrared data from CatWISE in improving the identification of extragalactic sources compared to the classification obtained using Gaia data. We evaluate different input feature configurations and prior functions, with the aim of presenting a classification methodology integrating prior knowledge stemming from realistic class distributions in the universe. In our work, we compare different classifiers, namely Gaussian Mixture Models (GMMs), XGBoost and CatBoost, and classify sources into three classes - star, quasar, and galaxy, with the target quasar and galaxy class labels obtained from SDSS16 and the star label from Gaia EDR3. In our approach, we adjust the posterior probabilities to reflect the intrinsic distribution of extragalactic sources in the universe via a prior function. We introduce two priors, a global prior reflecting the overall rarity of quasars and galaxies, and a mixed prior that incorporates in addition the distribution of the these sources as a function of Galactic latitude and magnitude. Our best classification performances, in terms of completeness and purity of the galaxy and quasar classes, are achieved using the mixed prior for sources at high latitudes and in the magnitude range G = 18.5 to 19.5. We apply our identified best-performing classifier to three application datasets from Gaia DR3, and find that the global prior is more conservative in what it considers to be a quasar or a galaxy compared to the mixed prior. In particular, when applied to the pure quasar and galaxy candidates samples, we attain a purity of 97% for quasars and 99.9% for galaxies using the global prior, and purities of 96% and 99% respectively using the mixed prior. We conclude our work by discussing the importance of applying adjusted priors portraying realistic class distributions in the universe.Comment: 21 pages, 23 figures, Accepted for publication in A&

Clathrin and AP2 are required for PtdIns(4,5)P2-mediated formation of LRP6 signalosomes

Canonical Wnt signaling is initiated by the binding of Wnt proteins to their receptors, low-density lipoprotein-related protein 5 and 6 (LRP5/6) and frizzled proteins, leading to phosphatidylinositol (4,5)bisphosphate (PtdIns(4,5)P2) production, signalosome formation, and LRP phosphorylation. However, the mechanism by which PtdIns(4,5)P2 regulates the signalosome formation remains unclear. Here we show that clathrin and adaptor protein 2 (AP2) were part of the LRP6 signalosomes. The presence of clathrin and AP2 in the LRP6 signalosomes depended on PtdIns(4,5)P2, and both clathrin and AP2 were required for the formation of LRP6 signalosomes. In addition, WNT3A-induced LRP6 signalosomes were primarily localized at cell surfaces, and WNT3A did not induce marked LRP6 internalization. However, rapid PtdIns(4,5)P2 hydrolysis induced artificially after WNT3A stimulation could lead to marked LRP6 internalization. Moreover, we observed WNT3A-induced LRP6 and clathrin clustering at cell surfaces using super-resolution fluorescence microscopy. Therefore, we conclude that PtdIns(4,5)P2 promotes the assembly of LRP6 signalosomes via the recruitment of AP2 and clathrin and that LRP6 internalization may not be a prerequisite for Wnt signaling to β-catenin stabilization

Detection of the Milky Way spiral arms in dust from 3D mapping

Large stellar surveys are sensitive to interstellar dust through the effects of reddening. Using extinctions measured from photometry and spectroscopy, together with three-dimensional (3D) positions of individual stars, it is possible to construct a three-dimensional dust map. We present the first continuous map of the dust distribution in the Galactic disk out to 7 kpc within 100 pc of the Galactic midplane, using red clump and giant stars from SDSS APOGEE DR14. We use a non-parametric method based on Gaussian Processes to map the dust density, which is the local property of the ISM rather than an integrated quantity. This method models the dust correlation between points in 3D space and can capture arbitrary variations, unconstrained by a pre-specified functional form. This produces a continuous map without line-of-sight artefacts. Our resulting map traces some features of the local Galactic spiral arms, even though the model contains no prior suggestion of spiral arms, nor any underlying model for the Galactic structure. This is the first time that such evident arm structures have been captured by a dust density map in the Milky Way. Our resulting map also traces some of the known giant molecular clouds in the Galaxy and puts some constraints on their distances, some of which were hitherto relatively uncertain.Comment: Accepted for publication in A&A, 9 pages, 7 figure

Detailed 3D structure of OrionA in dust with Gaia DR2

The unprecedented astrometry from Gaia DR2 provides us with an opportunity to study in detail molecular clouds in the solar neighbourhood. Extracting the wealth of information in these data remains a challenge, however. We have further improved our Gaussian Processes-based, three-dimensional dust mapping technique to allow us to study molecular clouds in more detail. These improvements include a significantly better scaling of the computational cost with the number of stars, and taking into account distance uncertainties to individual stars. Using Gaia DR2 astrometry together with 2MASS and WISE photometry for 30 000 stars, we infer the distribution of dust out to 600 pc in the direction of the Orion A molecular cloud. We identify a bubble-like structure in front of Orion A, centred at a distance of about 350 pc from the Sun. The main Orion A structure is visible at slightly larger distances, and we clearly see a tail extending over 100 pc that is curved and slightly inclined to the line-of-sight. The location of our foreground structure coincides with 5-10 Myr old stellar populations, suggesting a star formation episode that predates that of the Orion Nebula Cluster itself. We identify also the main structure of the Orion B molecular cloud, and in addition discover a background component to this at a distance of about 460 pc from the Sun. Finally, we associate our dust components at different distances with the plane-of-the-sky magnetic field orientation as mapped by Planck. This provides valuable information for modelling the magnetic field in 3D around star forming regions.Comment: Accepted for publication in Astronomy and Astrophysics. 9 pages, 12 figure

SURF IA Conflict Detection and Resolution Algorithm Evaluation

The Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) algorithm was evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. SURF IA is designed to increase flight crew situation awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the SURF IA algorithm under various runway scenarios, multiple levels of conflict detection and resolution (CD&R) system equipage, and various levels of horizontal position accuracy. This paper gives an overview of the SURF IA concept, simulation study, and results. Runway incursions are a serious aviation safety hazard. As such, the FAA is committed to reducing the severity, number, and rate of runway incursions by implementing a combination of guidance, education, outreach, training, technology, infrastructure, and risk identification and mitigation initiatives [1]. Progress has been made in reducing the number of serious incursions - from a high of 67 in Fiscal Year (FY) 2000 to 6 in FY2010. However, the rate of all incursions has risen steadily over recent years - from a rate of 12.3 incursions per million operations in FY2005 to a rate of 18.9 incursions per million operations in FY2010 [1, 2]. The National Transportation Safety Board (NTSB) also considers runway incursions to be a serious aviation safety hazard, listing runway incursion prevention as one of their most wanted transportation safety improvements [3]. The NTSB recommends that immediate warning of probable collisions/incursions be given directly to flight crews in the cockpit [4]

Linezolid-Associated Thrombocytopenia in Children with Renal Impairment

Poster presented at ID Week, October 2013, San Francisco, California