RNA-programmed genome editing using CRISPR/Cas9 from Streptococcus pyogenes has enabled rapid and accessible alteration of specific genomic loci in many organisms. A flexible means to target RNA would allow alteration and imaging of endogenous RNA transcripts analogous to CRISPR/Cas-based genomic tools, but most RNA targeting methods rely on incorporation of exogenous tags. Here, we demonstrate that nuclease-inactive S. pyogenes CRISPR/Cas9 can bind RNA in a nucleic-acid-programmed manner and allow endogenous RNA tracking in living cells. We show that nuclear-localized RNA-targeting Cas9 (RCas9) is exported to the cytoplasm only in the presence of sgRNAs targeting mRNA and observe accumulation of ACTB, CCNA2, and TFRC mRNAs in RNA granules that correlate with fluorescence in situ hybridization. We also demonstrate time-resolved measurements of ACTB mRNA trafficking to stress granules. Our results establish RCas9 as a means to track RNA in living cells in a programmable manner without genetically encoded tags.

The ARIANNA hexagonal radio array (HRA) is an experiment in its pilot phase designed to detect cosmogenic neutrinos of energies above 1016 eV. The most neutrino-like background stems from the radio emission of air showers. This article reports on dedicated efforts of simulating and detecting the signals of cosmic rays. A description of the fully radio self-triggered data-set, the properties of the detected air shower signals in the frequency range of 100–500 MHz and the consequences for neutrino detection are given. 38 air shower signals are identified by their distinct waveform characteristics, are in good agreement with simulations and their signals provide evidence that neutrino-induced radio signals will be distinguishable with high efficiency in ARIANNA. The cosmic ray flux at a mean energy of 6.5−1.0+1.2×1017 eV is measured to be 1.1−0.7+1.0×10−16 eV−1 km−2 sr−1 yr−1 and one five-fold coincident event is used to illustrate the capabilities of the ARIANNA detector to reconstruct arrival direction and energy of air showers.

Ongoing experimental efforts in Antarctica seek to detect ultra-high energy neutrinos by measurement of radio-frequency (RF) Askaryan radiation generated by the collision of a neutrino with an ice molecule. An array of RF antennas, deployed either in-ice or in-air, is used to infer the properties of the neutrino. To evaluate their experimental sensitivity, such experiments require a refractive index model for ray tracing radio-wave trajectories from a putative in-ice neutrino interaction point to the receiving antennas; this gives the degree of signal absorption or ray bending from source to receiver. The gradient in the density profile over the upper 200 meters of Antarctic ice, coupled with Fermat's least-time principle, implies ray "bending" and the existence of "forbidden" zones for predominantly horizontal signal propagation at shallow depths. After re-deriving the formulas describing such shadowing, we report on experimental results that, somewhat unexpectedly, demonstrate the existence of electromagnetic wave transport modes from nominally shadowed regions. The fact that this shadow-signal propagation is observed both at South Pole and the Ross Ice Shelf in Antarctica suggests that the effect may be a generic property of polar ice, with potentially important implications for experiments seeking to detect neutrinos.

Microsatellite repeat expansions in DNA produce pathogenic RNA species that cause dominantly inherited diseases such as myotonic dystrophy type 1 and 2 (DM1/2), Huntington's disease, and C9orf72-linked amyotrophic lateral sclerosis (C9-ALS). Means to target these repetitive RNAs are required for diagnostic and therapeutic purposes. Here, we describe the development of a programmable CRISPR system capable of specifically visualizing and eliminating these toxic RNAs. We observe specific targeting and efficient elimination of microsatellite repeat expansion RNAs both when exogenously expressed and in patient cells. Importantly, RNA-targeting Cas9 (RCas9) reverses hallmark features of disease including elimination of RNA foci among all conditions studied (DM1, DM2, C9-ALS, polyglutamine diseases), reduction of polyglutamine protein products, relocalization of repeat-bound proteins to resemble healthy controls, and efficient reversal of DM1-associated splicing abnormalities in patient myotubes. Finally, we report a truncated RCas9 system compatible with adeno-associated viral packaging. This effort highlights the potential of RCas9 for human therapeutics.

NuRadioMC is a Monte Carlo framework designed to simulate ultra-high energy neutrino detectors that rely on the radio detection method. This method exploits the radio emission generated in the electromagnetic component of a particle shower following a neutrino interaction. NuRadioMC simulates everything from the neutrino interaction in a medium, the subsequent Askaryan radio emission, the propagation of the radio signal to the detector and finally the detector response. NuRadioMC is designed as a modern, modular Python-based framework, combining flexibility in detector design with user-friendliness. It includes a state-of-the-art event generator, an improved modelling of the radio emission, a revisited approach to signal propagation and increased flexibility and precision in the detector simulation. This paper focuses on the implemented physics processes and their implications for detector design. A variety of models and parameterizations for the radio emission of neutrino-induced showers are compared and reviewed. Comprehensive examples are used to discuss the capabilities of the code and different aspects of instrumental design decisions.

The measurement of ultra-high energy (UHE) neutrinos (E > 1016 eV) opens a new field of astronomy with the potential to reveal the sources of ultra-high energy cosmic rays especially if combined with observations in the electromagnetic spectrum and gravitational waves. The ARIANNA pilot detector explores the detection of UHE neutrinos with a surface array of independent radio detector stations in Antarctica which allows for a cost-effective instrumentation of large volumes. Twelve stations are currently operating successfully at the Moore's Bay site (Ross Ice Shelf) in Antarctica and at the South Pole. We will review the current state of ARIANNA and its main results. We report on a newly developed wind generator that successfully operates in the harsh Antarctic conditions and powers the station for a substantial time during the dark winter months. The robust ARIANNA surface architecture, combined with environmentally friendly solar and wind power generators, can be installed at any deep ice location on the planet and operated autonomously. We report on the detector capabilities to determine the neutrino direction by reconstructing the signal arrival direction of a 800 m deep calibration pulser, and the reconstruction of the signal polarization using the more abundant cosmic-ray air showers. Finally, we describe a large-scale design – ARIA – that capitalizes on the successful experience of the ARIANNA operation and is designed sensitive enough to discover the first UHE neutrino.

Myotonic dystrophy type I (DM1) is a multisystemic autosomal-dominant inherited human disorder that is caused by CTG microsatellite repeat expansions (MREs) in the 3' untranslated region of DMPK. Toxic RNAs expressed from such repetitive sequences can be eliminated using CRISPR-mediated RNA targeting, yet evidence of its in vivo efficacy and durability is lacking. Here, using adult and neonatal mouse models of DM1, we show that intramuscular or systemic injections of adeno-associated virus (AAV) vectors encoding nuclease-dead Cas9 and a single-guide RNA targeting CUG repeats results in the expression of the RNA-targeting Cas9 for up to three months, redistribution of the RNA-splicing protein muscleblind-like splicing regulator 1, elimination of foci of toxic RNA, reversal of splicing biomarkers and amelioration of myotonia. The sustained reversal of DM1 phenotypes provides further support that RNA-targeting Cas9 is a viable strategy for treating DM1 and other MRE-associated diseases.

Ultra high energy neutrinos (Eν > 1016.5eV) are efficiently measured via radio signals following a neutrino interaction in ice. An antenna placed1(15 m) below the ice surface will measure two signals for the vast majority of events (90% at Eν=1018 eV): a direct pulse and a second delayed pulse from a reflection off the ice surface. This allows for a unique identification of neutrinos against backgrounds arriving from above. Furthermore, the time delay between the direct and reflected signal (D'n'R) correlates with the distance to the neutrino interaction vertex, a crucial quantity to determine the neutrino energy. In a simulation study, we derive the relation between time delay and distance and study the corresponding experimental uncertainties in estimating neutrino energies. We find that the resulting contribution to the energy resolution is well below the natural limit set by the unknown inelasticity in the initial neutrino interaction. We present an in-situ measurement that proves the experimental feasibility of this technique. Continuous monitoring of the local snow accumulation in the vicinity of the transmit and receive antennas using this technique provide a precision of (1 mm) in surface elevation, which is much better than that needed to apply the D'n'R technique to neutrinos.

The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies (Eν > 1016 eV), the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95% of the neutrino signal at a thermal noise rejection factor of 105, compared to a template matching procedure which reaches only 102 for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal.