Set of movies files to provide minimal dataset for Smaczynska-de Rooij 2019 PLoS One figure 6Methodological information
Fluorescence microscopy: For co-localisation and live-cell imaging, cells expressing tagged
proteins were visualized after growing to early log phase in synthetic medium
with appropriate supplements.
Epifluorescence microscopy was performed using Olympus IX-81
inverted microscope with a DeltaVision RT Restoration Microscopy System (using
a 100x/1.40 NA oil objective), Photometrics Coolsnap HQ camera with Imaging and
Image capture performed using SoftWoRx™ image analysis and model-building
application (Applied Precision Instruments, Seattle). Time-lapse live cell imaging of GFP-tagged Sla2 was
performed with 1 sec time-lapse. All image data sets were deconvolved, using
the SoftWoRx application.
Time-lapse live cell images of
Rvs167-GFP was acquired using OMX DeltaVision V4 and a 60xUSPLAPO (numerical
aperture, 1.42) objective with refractive index 0.1514 immersion oil
(Cargille). Samples were illuminated using Insight Solid State Illuminator
(10%), and images were taken simultaneously on separate scientific
complementary metal oxide semiconductor (sCMOS) cameras (70 ms exposure). Seven
250 nm sections were acquired every 500 ms (181 time points). The stacks were
then deconvolved and processed, using SoftWorx, to produce a movie composed of
maximum intensity projections at each time point. The Rvs167-GFP lifetime was
analyzed from those projections. Movies and kymographs
were assembled using Fiji software. Images were
exported as TIFF files and image size adjusted to 300 d.p.i. and assembled
using Adobe Photoshop CS2.
Supplementary material for multi-agent gathering on a grid
Supplementary materials (www.doi.org/10.7551/ecal_a_056)
Abstract We examine the problem solving capabilities of swarms of computation- and memory-free agents. Each agent has a single line-of-sight sensor providing two bits of information. The agent maps this information directly onto constant motor commands. In previous work, we showed that such simplistic agents can solve tasks requiring them to organize spatially (multi-robot aggregation and circle formation) and manipulate passive objects (clustering). In the present work, we address the shepherding problem, where the computation- and memory-free agents—the shepherds—are tasked to gather and move a group of dynamic agents—the sheep—towards a pre-defined goal. The shepherds and sheep are modelled as e-puck robots using computer simulations. Our findings show that the shepherding problem does not fundamentally require arithmetic computation or memory to be solved. The obtained controller solution is robust with respect to sensory noise, and copes well with changes in the number of sheep.
Supplementary materials for Spatial Coverage Without Computation (www.doi.org/10.1109/ICRA.2019.8793731)
AbstractWe study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage. The solution requires only a single bit of information per robot—whether or not another robot is present in its line of sight. Computer simulations show that our deterministic controller, which was obtained through off-line optimization, achieves around 71–76% coverage in a test scenario with no robot redundancy, which corresponds to a 26–39% reduction of the area that is not covered, when compared to an optimized random walk. A moderately lower level of performance was observed in 20 experimental trials with 25 physical e-puck robots. Moreover, we demonstrate that the same controller can be used in environments of different dimensions and even to navigate a maze. The controller provides a baseline against which one can quantify the performance improvements that more advanced and expensive techniques may offer. Moreover, due to its simplicity, it could potentially be implemented on swarms of sub-millimeter-sized robots. This would pave the way for new applications in micro-medicine.
Contributors:Barnes, Amy, Black, Michelle
There is a lot of political attention on early years across the UK; with all countries championing a prevention approach to early child development – but are we actually talking about the same issue? Are we all talking about a fairer start for all? We will discuss this question with reference to a study we recently completed looking at early years policy in the four countries of the UK. We will discuss similarities in policy and systems, but also highlight differences; which, we suggest, implies different policy understandings of what ‘the problem’ and ‘causes’ of difference are in early child development. While we recognise much policy happens in ‘local places’ (e.g. through commissioning PH services), we will discuss how our recent review work highlights the substantive role of policy action at central executive (parliamentary/assembly) level in all UK countries in addressing wider determinants of early child development. Influencing at this central level is an important opportunity for public health practitioners, yet securing central-level political action often requires (amongst other things) coalitions of stakeholders who share similar beliefs, collectively identify with one another, and collectively try to frame dialogue, use evidence and advocate in ways that resonate with policy makers and the public. Drawing on the conclusions, we will ask the audience whether they think this type of coalition or collective public health identity exists within or across the UK PH workforce? And how we can strengthen the power of PH voices in national policy to deliver a fairer start for all?
Presentation made at PHE conference 2019: Title: Understanding and influencing the national policy context to deliver a fairer start for all?
Authors: Michelle Black - University of Sheffield; Amy Barnes - University of SheffieldSession: Place-based working to improve outcomes for children and young people - 2, 11/09/2019, 09:00 - 10:00
Supplementary materials (www.doi.org/10.1109/LRA.2018.2795640)
AbstractA canonical problem for swarms of agents is to collectively choose one of multiple options in their environment. We present a novel control strategy for solving this problem— the first to be free of arithmetic computation. The agents do not communicate with each other nor do they store run-time information. They have a line-of-sight sensor that extracts one ternary digit of information from the environment. At every time step, they directly map this information onto constant-value motor commands. We evaluate the control strategy with both simulated and physical e-puck robots. By default, the robots are expected to choose, and move to, one of two options of equal value. The simulation studies show that the strategy is robust against sensory noise, scalable to large swarm sizes, and generalizes to the problems of choosing between more than two options or between unequal options. The experiments—50 trials conducted with a group of 20 e-puck robots—show that the group achieves consensus in 96% of the trials. Given the extremely low hardware requirements of the strategy, it opens up new possibilities for the design of swarms of robots that are small in size (≪ 10−3 m) and large in numbers (≫ 103).