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  1. Abstract Background

    Alzheimer’s disease (AD) is a progressive neurodegenerative disease that impacts nearly 400 million people worldwide. The accumulation of amyloid beta (Aβ) in the brain has historically been associated with AD, and recent evidence suggests that neuroinflammation plays a central role in its origin and progression. These observations have given rise to the theory that Aβ is the primary trigger of AD, and induces proinflammatory activation of immune brain cells (i.e., microglia), which culminates in neuronal damage and cognitive decline. To test this hypothesis, many in vitro systems have been established to study Aβ-mediated activation of innate immune cells. Nevertheless, the transcriptional resemblance of these models to the microglia in the AD brain has never been comprehensively studied on a genome-wide scale.

    Methods

    We used bulk RNA-seq to assess the transcriptional differences between in vitro cell types used to model neuroinflammation in AD, including several established, primary and iPSC-derived immune cell lines (macrophages, microglia and astrocytes) and their similarities to primary cells in the AD brain. We then analyzed the transcriptional response of these innate immune cells to synthetic Aβ or LPS and INFγ.

    Results

    We found that human induced pluripotent stem cell (hIPSC)-derived microglia (IMGL) are the in vitro cell model that best resembles primary microglia. Surprisingly, synthetic Aβ does not trigger a robust transcriptional response in any of the cellular models analyzed, despite testing a wide variety of Aβ formulations, concentrations, and treatment conditions. Finally, we found that bacterial LPS and INFγ activate microglia and induce transcriptional changes that resemble many, but not all, aspects of the transcriptomic profiles of disease associated microglia (DAM) present in the AD brain.

    Conclusions

    These results suggest that synthetic Aβ treatment of innate immune cell cultures does not recapitulate transcriptional profiles observed in microglia from AD brains. In contrast, treating IMGL with LPS and INFγ induces transcriptional changes similar to those observed in microglia detected in AD brains.

     
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  2. Abstract

    The next core-collapse supernova in the Milky Way or its satellites will represent a once-in-a-generation opportunity to obtain detailed information about the explosion of a star and provide significant scientific insight for a variety of fields because of the extreme conditions found within. Supernovae in our galaxy are not only rare on a human timescale but also happen at unscheduled times, so it is crucial to be ready and use all available instruments to capture all possible information from the event. The first indication of a potential stellar explosion will be the arrival of a bright burst of neutrinos. Its observation by multiple detectors worldwide can provide an early warning for the subsequent electromagnetic fireworks, as well as signal to other detectors with significant backgrounds so they can store their recent data. The supernova early warning system (SNEWS) has been operating as a simple coincidence between neutrino experiments in automated mode since 2005. In the current era of multi-messenger astronomy there are new opportunities for SNEWS to optimize sensitivity to science from the next galactic supernova beyond the simple early alert. This document is the product of a workshop in June 2019 towards design of SNEWS 2.0, an upgraded SNEWS with enhanced capabilities exploiting the unique advantages of prompt neutrino detection to maximize the science gained from such a valuable event.

     
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    A bstract A search for new phenomena with top quark pairs in final states with one isolated electron or muon, multiple jets, and large missing transverse momentum is performed. Signal regions are designed to search for two-, three-, and four-body decays of the directly pair-produced supersymmetric partner of the top quark (stop). Additional signal regions are designed specifically to search for spin-0 mediators that are produced in association with a pair of top quarks and decay into a pair of dark-matter particles. The search is performed using the Large Hadron Collider proton-proton collision dataset at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb − 1 . No significant excess above the Standard Model background is observed, and limits at 95% confidence level are set in the stop-neutralino mass plane and as a function of the mediator mass or the dark-matter particle mass. Stops are excluded up to 1200 GeV (710 GeV) in the two-body (three-body) decay scenario. In the four-body scenario stops up to 640 GeV are excluded for a stop-neutralino mass difference of 60 GeV. Scalar and pseudoscalar dark-matter mediators are excluded up to 200 GeV when the coupling strengths of the mediator to Standard Model and dark-matter particles are both equal to one and when the mass of the dark-matter particle is 1 GeV. 
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    Abstract Jet substructure has provided new opportunities for searches and measurements at the LHC, and has seen continuous development since the optimization of the large-radius jet definition used by ATLAS was performed during Run 1. A range of new inputs to jet reconstruction, pile-up mitigation techniques and jet grooming algorithms motivate an optimisation of large-radius jet reconstruction for ATLAS. In this paper, this optimisation procedure is presented, and the performance of a wide range of large-radius jet definitions is compared. The relative performance of these jet definitions is assessed using metrics such as their pileup stability, ability to identify hadronically decaying W bosons and top quarks with large transverse momenta. A new type of jet input object, called a ‘unified flow object’ is introduced which combines calorimeter- and inner-detector-based signals in order to achieve optimal performance across a wide kinematic range. Large-radius jet definitions are identified which significantly improve on the current ATLAS baseline definition, and their modelling is studied using pp collisions recorded by the ATLAS detector at $$\sqrt{s}=13~\text {TeV}$$ s = 13 TeV during 2017. 
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    A Correction to this paper has been published: 10.1140/epjc/s10052-020-08730-0 
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