Quantifying nuclear wide chromatin compaction by phasor analysis of histone Förster resonance energy transfer (FRET) in frequency domain fluorescence lifetime imaging microscopy (FLIM) data.

The nanometer spacing between nucleosomes throughout global chromatin organisation modulates local DNA template access, and through continuous dynamic rearrangements, regulates genome function [1]. However, given that nucleosome packaging occurs on a spatial scale well below the diffraction limit, real time observation of chromatin structure in live cells by optical microscopy has proved technically difficult, despite recent advances in live cell super resolution imaging [2]. One alternative solution to quantify chromatin structure in a living cell at the level of nucleosome proximity is to measure and spatially map Förster resonance energy transfer (FRET) between fluorescently labelled histones - the core protein of a nucleosome [3]. In recent work we established that the phasor approach to fluorescence lifetime imaging microscopy (FLIM) is a robust method for the detection of histone FRET which can quantify nuclear wide chromatin compaction in the presence of cellular autofluorescence [4]. Here we share FLIM data recording histone FRET in live cells co-expressing H2B-eGFP and H2B-mCherry. The data was acquired in the frequency domain [5] and processed by the phasor approach to lifetime analysis [6]. The data can be valuable to researchers interested in using the histone FRET assay since it highlights the impact of cellular autofluorescence and acceptor-donor ratio on quantifying chromatin compaction. The data is related to the research article "Phasor histone FLIM-FRET microscopy quantifies spatiotemporal rearrangement of chromatin architecture during the DNA damage response" [4]

Mapping solar array location, size, and capacity using deep learning and overhead imagery

The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions

Microlensing and the Stellar Mass Function

Traditional approaches to measuring the stellar mass function (MF) are fundamentally limited because objects are detected based on their luminosity, not their mass. These methods are thereby restricted to luminous and relatively nearby stellar populations. Gravitational microlensing promises to revolutionize our understanding of the MF. It is already technologically feasible to measure the MFs of the Galactic disk and Galactic bulge as functions of position, although the actual execution of this program requires aggressive ground-based observations including infrared interferometry, as well as the launching of a small satellite telescope. Rapid developments in microlensing, including the new technique of ``pixel lensing'' of unresolved stars, will allow one to probe the MF and luminosity function of nearby galaxies. Such observations of M31 are already underway, and pixel-lensing observations of M87 with the {\it Hubble Space Telescope} would permit detection of dark intra-cluster objects in Virgo. Microlensing techniques can also be applied to investigate the star-formation history of the universe and to search for planets with masses as small as the Earth's. Based on an invited talk at the January 1996 AAS meeting in San Antonio. PASP (June 1996) in press, (c) ASP, reproduced with permission.Comment: 31 pages with 7 embedded figures. PASP (June 1996) in press, (c) ASP, reproduced with permissio

A Neural System for Automated CCTV Surveillance

This paper overviews a new system, the “Owens Tracker,” for automated identification of suspicious pedestrian activity in a car-park. Centralized CCTV systems relay multiple video streams to a central point for monitoring by an operator. The operator receives a continuous stream of information, mostly related to normal activity, making it difficult to maintain concentration at a sufficiently high level. While it is difficult to place quantitative boundaries on the number of scenes and time period over which effective monitoring can be performed, Wallace and Diffley [1] give some guidance, based on empirical and anecdotal evidence, suggesting that the number of cameras monitored by an operator be no greater than 16, and that the period of effective monitoring may be as low as 30 minutes before recuperation is required. An intelligent video surveillance system should therefore act as a filter, censuring inactive scenes and scenes showing normal activity. By presenting the operator only with unusual activity his/her attention is effectively focussed, and the ratio of cameras to operators can be increased. The Owens Tracker learns to recognize environmentspecific normal behaviour, and refers sequences of unusual behaviour for operator attention. The system was developed using standard low-resolution CCTV cameras operating in the car-parks of Doxford Park Industrial Estate (Sunderland, Tyne and Wear), and targets unusual pedestrian behaviour. The modus operandi of the system is to highlight excursions from a learned model of normal behaviour in the monitored scene. The system tracks objects and extracts their centroids; behaviour is defined as the trajectory traced by an object centroid; normality as the trajectories typically encountered in the scene. The essential stages in the system are: segmentation of objects of interest; disambiguation and tracking of multiple contacts, including the handling of occlusion and noise, and successful tracking of objects that “merge” during motion; identification of unusual trajectories. These three stages are discussed in more detail in the following sections, and the system performance is then evaluated

A Star-Forming Shock Front in Radio Galaxy 4C+41.17 Resolved with Laser-Assisted Adaptive Optics Spectroscopy

Near-infrared integral-field spectroscopy of redshifted [O III], H-beta and optical continuum emission from z=3.8 radio galaxy 4C+41.17 is presented, obtained with the laser-guide-star adaptive optics facility on the Gemini North telescope. Employing a specialized dithering technique, a spatial resolution of 0.10 arcsec or 0.7 kpc is achieved in each spectral element, with velocity resolution of ~70 km/s. Spectra similar to local starbursts are found for bright knots coincident in archival Hubble Space Telescope (HST) restframe-ultraviolet images, which also allows a key line diagnostic to be mapped together with new kinematic information. There emerges a clearer picture of the nebular emission associated with the jet in 8.3 GHz and 15 GHz Very Large Array maps, closely tied to a Ly-alpha-bright shell-shaped structure seen with HST. This supports a previous interpretation of that arc tracing a bow shock, inducing 10^10-11 M_solar star-formation regions that comprise the clumpy broadband optical/ultraviolet morphology near the core.Comment: 10 pages, 6 figures, accepted for publication in A