An efficient GUI-based clustering software for simulation and Bayesian cluster analysis of single-molecule localization microscopy data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays

Does distributed leadership have a place in destination management organisations? A policy-makers perspective

Within an increasingly networked environment and recent transitions in the landscape of funding for destination management organisations (DMOs) and destinations, pooling knowledge and resources may well be seen as a prerequisite to ensuring the long-term sustainability of reshaped, yet financially constrained DMOs facing severe challenges to deliver value to destinations, visitors and member organisations. Distributed Leadership (DL) is a recent paradigm gaining momentum in destination research as a promising response to these challenges. Building on the scarce literature on DL in a DMO context, this paper provides a policy-makers’ perspective into the place of DL in reshaped DMOs and DMOs undergoing transformation and explores current challenges and opportunities to the enactment and practice of DL. The underpinned investigation used in-depth, semi-structured interviews with policy-makers from VisitEngland following an interview agenda based on the DMO Leadership Cycle. Policy-makers within VisitEngland saw a multitude of opportunities for DMOs with regards to DL, but equally, they emphasised challenges acting as barriers to realising the potential benefits of introducing a DL model to DMOs as a response to uncertainty in the funding landscape

Analysis of the impact of length of stay on the quality of service experience, satisfaction and loyalty

Although length of stay is a relevant variable in destination management, little research has been produced connecting it with tourists' post-consumption behaviour. This research compares the post-consumption behaviour of same-day visitors with overnight tourists in a sample of 398 domestic vacationers at two Mediterranean heritage-and-beach destinations. Although economic research on length of stay posits that there are destination benefits in longer stays, same-day visitors score higher in most of the post-consumption variables under study. Significant differences arise in hedonic aspects of the tourist experience and destination loyalty. Thus, we propose that length of stay can be used as a segmentation variable. Furthermore, destination management organisations need to consider length of stay when designing tourism policies. The tourist product and communication strategies might be adapted to different vacation durations

A Search for Photons with Energies Above 2 × 1017^{17} eV Using Hybrid Data from the Low-Energy Extensions of the Pierre Auger Observatory

Ultra-high-energy photons with energies exceeding 10$^{17}$ eV offer a wealth of connections to different aspects of cosmic-ray astrophysics as well as to gamma-ray and neutrino astronomy. The recent observations of photons with energies in the 10$^{15}$ eV range further motivate searches for even higher-energy photons. In this paper, we present a search for photons with energies exceeding 2 × 10$^{17}$ eV using about 5.5 yr of hybrid data from the low-energy extensions of the Pierre Auger Observatory. The upper limits on the integral photon flux derived here are the most stringent ones to date in the energy region between 10$^{17}$ and 10$^{18}$ eV

A Search for Photons with Energies above 2 × 1017eV Using Hybrid Data from the Low-Energy Extensions of the Pierre Auger Observatory

Ultra-high-energy photons with energies exceeding 1017 eV offer a wealth of connections to different aspects of cosmic-ray astrophysics as well as to gamma-ray and neutrino astronomy. The recent observations of photons with energies in the 1015 eV range further motivate searches for even higher-energy photons. In this paper, we present a search for photons with energies exceeding 2 × 1017 eV using about 5.5 yr of hybrid data from the low-energy extensions of the Pierre Auger Observatory. The upper limits on the integral photon flux derived here are the most stringent ones to date in the energy region between 1017 and 1018 eV

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

Design and implementation of the AMIGA embedded system for data acquisition

The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3 m underground next to the water-Cherenkov stations that form the 23.5 km$^2$ large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 10$^{17}$ eV. At the depth of 2.3 m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. This work describes the design and implementation of the AMIGA embedded system, which provides centralized control, data acquisition and environment monitoring to its detectors. The presented system was firstly tested in the engineering array phase ended in 2017, and lately selected as the final design to be installed in all new detectors of the production phase. The system was proven to be robust and reliable and has worked in a stable manner since its first deployment.Comment: Accepted for publication at JINST. Published version, 34 pages, 15 figures, 4 table

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from $10^{17}~$eV up to more than $10^{20}~$eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.Comment: 23 pages, 15 figures. Version accepted for publication in JINS

Cosmological implications of photon-flux upper limits at ultra-high energies in scenarios of Planckian-interacting massive particles for dark matter

We present a thorough search for signatures that would be suggestive of super-heavy $X$ particles decaying in the Galactic halo, in the data of the Pierre Auger Observatory. From the lack of signal, we derive upper limits for different energy thresholds above ${\gtrsim}10^8$\,GeV on the expected secondary by-product fluxes from $X$-particle decay. Assuming that the energy density of these super-heavy particles matches that of dark matter observed today, we translate the upper bounds on the particle fluxes into tight constraints on the couplings governing the decay process as a function of the particle mass. We show that instanton-induced decay processes allow us to derive a bound on the reduced coupling constant of gauge interactions in the dark sector: \alpha_X \alt 0.09, for 10^{9} \alt M_X/\text{GeV} < 10^{19}. This upper limit on $\alpha_X$ is complementary to the non-observation of tensor modes in the cosmic microwave background in the context of Planckian-interacting massive particles for dark matter produced during the reheating epoch. Viable regions for this scenario to explain dark matter are delineated in several planes of the multidimensional parameter space that involves, in addition to $M_X$ and $\alpha_X$, the Hubble rate at the end of inflation, the reheating efficiency, and the non-minimal coupling of the Higgs with curvature.Comment: 15 pages, 8 figures, Accompanying paper of arXiv:2203.0885