Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

Heme oxygenase-1 derived carbon monoxide suppresses Aβ1-42 toxicity in astrocytes

Neurodegeneration in Alzheimer’s disease (AD) is extensively studied, and the involvement of astrocytes and other cell types in this process has been described. However, the responses of astrocytes themselves to amyloid β peptides ((Aβ; the widely accepted major toxic factor in AD) is less well understood. Here, we show that Aβ(1-42) is toxic to primary cultures of astrocytes. Toxicity does not involve disruption of astrocyte Ca2+ homeostasis, but instead occurs via formation of the toxic reactive species, peroxynitrite. Thus, Aβ(1-42) raises peroxynitrite levels in astrocytes, and Aβ(1-42) toxicity can be inhibited by antioxidants, or by inhibition of nitric oxide (NO) formation (reactive oxygen species (ROS) and NO combine to form peroxynitrite), or by a scavenger of peroxynitrite. Increased ROS levels observed following Aβ(1-42) application were derived from NADPH oxidase. Induction of heme oxygenase-1 (HO-1) protected astrocytes from Aβ(1-42) toxicity, and this protective effect was mimicked by application of the carbon monoxide (CO) releasing molecule CORM-2, suggesting HO-1 protection was attributable to its formation of CO. CO suppressed the rise of NADPH oxidase-derived ROS caused by Aβ(1-42). Under hypoxic conditions (0.5% O2, 48h) HO-1 was induced in astrocytes and Aβ(1-42) toxicity was significantly reduced, an effect which was reversed by the specific HO-1 inhibitor, QC-15. Our data suggest that Aβ(1-42) is toxic to astrocytes, but that induction of HO-1 affords protection against this toxicity due to formation of CO. HO-1 induction, or CO donors, would appear to present attractive possible approaches to provide protection of both neuronal and non-neuronal cell types from the degenerative effects of AD in the central nervous system

Heme oxygenase-1 derived carbon monoxide suppresses Aβ1-42 toxicity in astrocytes

Neurodegeneration in Alzheimer’s disease (AD) is extensively studied, and the involvement of astrocytes and other cell types in this process has been described. However, the responses of astrocytes themselves to amyloid peptides ((A; the widely accepted major toxic factor in AD) is less well understood. Here, we show that A(1-42) is toxic to primary cultures of astrocytes. Toxicity does not involve disruption of astrocyte Ca2+ homeostasis, but instead occurs via formation of the toxic reactive species, peroxynitrite. Thus, A(1-42) raises peroxynitrite levels in astrocytes, and A(1-42) toxicity can be inhibited by antioxidants, or by inhibition of nitric oxide (NO) formation (reactive oxygen species (ROS) and NO combine to form peroxynitrite), or by a scavenger of peroxynitrite. Increased ROS levels observed following A(1-42) application were derived from NADPH oxidase. Induction of heme oxygenase-1 (HO-1) protected astrocytes from A(1-42) toxicity, and this protective effect was mimicked by application of the carbon monoxide (CO) releasing molecule CORM-2, suggesting HO-1 protection was attributable to its formation of CO. CO suppressed the rise of NADPH oxidase-derived ROS caused by A(1-42). Under hypoxic conditions (0.5% O2, 48h) HO-1 was induced in astrocytes and A(1-42) toxicity was significantly reduced, an effect which was reversed by the specific HO-1 inhibitor, QC-15. Our data suggest that A(1-42) is toxic to astrocytes, but that induction of HO-1 affords protection against this toxicity due to formation of CO. HO-1 induction, or CO donors, would appear to present attractive possible approaches to provide protection of both neuronal and non-neuronal cell types from the degenerative effects of AD in the central nervous system

Reductions in External Divalent Cations Evoke Novel Voltage-Gated Currents in Sensory Neurons

It has long been recognized that divalent cations modulate cell excitability. Sensory nerve excitability is of critical importance to peripheral diseases associated with pain, sensory dysfunction and evoked reflexes. Thus we have studied the role these cations play on dissociated sensory nerve activity. Withdrawal of both Mg2+ and Ca2+ from external solutions activates over 90% of dissociated mouse sensory neurons. Imaging studies demonstrate a Na+ influx that then causes depolarization-mediated activation of voltage-gated Ca2+ channels (CaV), which allows Ca2+ influx upon divalent re-introduction. Inhibition of CaV (ω-conotoxin, nifedipine) or NaV (tetrodotoxin, lidocaine) fails to reduce the Na+ influx. The Ca2+ influx is inhibited by CaV inhibitors but not by TRPM7 inhibition (spermine) or store-operated channel inhibition (SKF96365). Withdrawal of either Mg2+ or Ca2+ alone fails to evoke cation influxes in vagal sensory neurons. In electrophysiological studies of dissociated mouse vagal sensory neurons, withdrawal of both Mg2+ and Ca2+ from external solutions evokes a large slowly-inactivating voltage-gated current (IDF) that cannot be accounted for by an increased negative surface potential. Withdrawal of Ca2+ alone fails to evoke IDF. Evidence suggests IDF is a non-selective cation current. The IDF is not reduced by inhibition of NaV (lidocaine, riluzole), CaV (cilnidipine, nifedipine), KV (tetraethylammonium, 4-aminopyridine) or TRPM7 channels (spermine). In summary, sensory neurons express a novel voltage-gated cation channel that is inhibited by external Ca2+ (IC50∼0.5 µM) or Mg2+ (IC50∼3 µM). Activation of this putative channel evokes substantial cation fluxes in sensory neurons

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Search for gravitational waves associated with gamma-ray bursts detected by Fermi and Swift during the LIGO–Virgo run O3b

We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC–2020 March 27 17:00 UTC). We conduct two independent searches: a generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds no evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate