Quenching and morphological evolution due to circumgalactic gas expulsion in a simulated galaxy with a controlled assembly history

We examine the influence of dark matter halo assembly on the evolution of a simulated ∼L⋆ galaxy. Starting from a zoom-in simulation of a star-forming galaxy evolved with the EAGLE galaxy formation model, we use the genetic modification technique to create a pair of complementary assembly histories: one in which the halo assembles later than in the unmodified case, and one in which it assembles earlier. Delayed assembly leads to the galaxy exhibiting a greater present-day star formation rate than its unmodified counterpart, while in the accelerated case the galaxy quenches at z ≃ 1, and becomes spheroidal. We simulate each assembly history nine times, adopting different seeds for the random number generator used by EAGLE’s stochastic subgrid implementations of star formation and feedback. The systematic changes driven by differences in assembly history are significantly stronger than the random scatter induced by this stochasticity. The sensitivity of ∼L⋆ galaxy evolution to dark matter halo assembly follows from the close coupling of the growth histories of the central black hole (BH) and the halo, such that earlier assembly fosters the formation of a more massive BH, and more efficient expulsion of circumgalactic gas. In response to this expulsion, the circumgalactic medium reconfigures at a lower density, extending its cooling time and thus inhibiting the replenishment of the interstellar medium. Our results indicate that halo assembly history significantly influences the evolution of ∼L⋆ central galaxies, and that the expulsion of circumgalactic gas is a crucial step in quenching them

Neuroactive Steroids Reverse Tonic Inhibitory Deficits in Fragile X Syndrome Mouse Model

Fragile X syndrome (FXS) is the most common form of inherited intellectual disability. A reduction in neuronal inhibition mediated by γ-aminobutyric acid type A receptors (GABAARs) has been implicated in the pathophysiology of FXS. Neuroactive steroids (NASs) are known allosteric modulators of GABAAR channel function, but recent studies from our laboratory have revealed that NASs also exert persistent metabotropic effects on the efficacy of tonic inhibition by increasing the protein kinase C (PKC)-mediated phosphorylation of the α4 and β3 subunits which increase the membrane expression and boosts tonic inhibition. We have assessed the GABAergic signaling in the hippocampus of fragile X mental retardation protein (FMRP) knock-out (Fmr1 KO) mouse. The GABAergic tonic current in dentate gyrus granule cells (DGGCs) from 3- to 5-week-old (p21–35) Fmr1 KO mice was significantly reduced compared to WT mice. Additionally, spontaneous inhibitory post synaptic inhibitory current (sIPSC) amplitudes were increased in DGGCs from Fmr1 KO mice. While sIPSCs decay in both genotypes was prolonged by the prototypic benzodiazepine diazepam, those in Frm1-KO mice were selectively potentiated by RO15-4513. Consistent with this altered pharmacology, modifications in the expression levels and phosphorylation of receptor GABAAR subtypes that mediate tonic inhibition were seen in Fmr1 KO mice. Significantly, exposure to NASs induced a sustained elevation in tonic current in Fmr1 KO mice which was prevented with PKC inhibition. Likewise, exposure reduced elevated membrane excitability seen in the mutant mice. Collectively, our results suggest that NAS act to reverse the deficits of tonic inhibition seen in FXS, and thereby reduce aberrant neuronal hyperexcitability seen in this disorder

Quenching and morphological evolution due to circumgalactic gas expulsion in a simulated galaxy with a controlled assembly history

We examine the influence of dark matter halo assembly on the evolution of a simulated $\sim L^\star$ galaxy. Starting from a zoom-in simulation of a star-forming galaxy evolved with the EAGLE galaxy formation model, we use the genetic modification technique to create a pair of complementary assembly histories: one in which the halo assembles later than in the unmodified case, and one in which it assembles earlier. Delayed assembly leads to the galaxy exhibiting a greater present-day star formation rate than its unmodified counterpart, whilst in the accelerated case the galaxy quenches at $z\simeq 1$, and becomes spheroidal. We simulate each assembly history nine times, adopting different seeds for the random number generator used by EAGLE's stochastic subgrid implementations of star formation and feedback. The systematic changes driven by differences in assembly history are significantly stronger than the random scatter induced by this stochasticity. The sensitivity of $\sim L^\star$ galaxy evolution to dark matter halo assembly follows from the close coupling of the growth histories of the central black hole (BH) and the halo, such that earlier assembly fosters the formation of a more massive BH, and more efficient expulsion of circumgalactic gas. In response to this expulsion, the circumgalactic medium reconfigures at a lower density, extending its cooling time and thus inhibiting the replenishment of the interstellar medium. Our results indicate that halo assembly history significantly influences the evolution of $\sim L^\star$ central galaxies, and that the expulsion of circumgalactic gas is a crucial step in quenching them

Imaging with therapeutic acoustic wavelets–short pulses enable acoustic localization when time of arrival is combined with delay and sum

—Passive acoustic mapping (PAM) is an algorithm that reconstructs the location of acoustic sources using an array of receivers. This technique can monitor therapeutic ultrasound procedures to confirm the spatial distribution and amount of microbubble activity induced. Current PAM algorithms have an excellentlateral resolution but have a poor axial resolution, making it difficult to distinguish acoustic sources within the ultrasound beams. With recent studies demonstrating that short-length and low-pressure pulses—acoustic wavelets—have the therapeutic function, we hypothesizedthat the axial resolution could be improved with a quasi-pulse-echo approach and that the resolution improvement would depend on the wavelet’s pulse length. This article describes an algorithm that resolves acoustic sources axially using time of flight and laterally using delayand-sum beamforming, which we named axial temporal position PAM (ATP-PAM). The algorithm accommodates a rapid short pulse (RaSP) sequence that can safely deliver drugs across the blood–brain barrier. We developed our algorithm with simulations (k-wave) and in vitro experiments for one-, two-, and five-cycle pulses, comparing our resolution against that of two current PAM algorithms. We then tested ATP-PAM in vivo and evaluated whether the reconstructed acoustic sources mapped to drug deliver

Sequence learning in Associative Neuronal-Astrocytic Network

The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and its most brain-derived branch, neuromorphic computing. Overturning our fundamental assumptions of how the brain works, the recent exploration of astrocytes is revealing that these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental evidence, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show that astrocytes were sufficient to trigger transitions between learned memories in the neuronal component of the network. Further, we mathematically derived the timing of the transitions that was governed by the dynamics of the calcium-dependent slow-currents in the astrocytic processes. Overall, we provide a brain-morphic mechanism for sequence learning that is inspired by, and aligns with, recent experimental findings. To evaluate our model, we emulated astrocytic atrophy and showed that memory recall becomes significantly impaired after a critical point of affected astrocytes was reached. This brain-inspired and brain-validated approach supports our ongoing efforts to incorporate non-neuronal computing elements in neuromorphic information processing.Comment: 8 pages, 5 figure

Using the theory of planned behaviour as a process evaluation tool in randomised trials of knowledge translation strategies : A case study from UK primary care

Peer reviewedPublisher PD

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

<p>Abstract</p> <p>Background</p> <p>Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.</p> <p>Results</p> <p>In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.</p> <p>Conclusion</p> <p>Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.</p