Investigating the contributions of circadian pathway and insomnia risk genes to autism and sleep disturbances

Sleep disturbance is prevalent in youth with Autism Spectrum Disorder (ASD). Researchers have posited that circadian dysfunction may contribute to sleep problems or exacerbate ASD symptomatology. However, there is limited genetic evidence of this. It is also unclear how insomnia risk genes identified through GWAS in general populations are related to ASD and common sleep problems like insomnia traits in ASD. We investigated the contribution of copy number variants (CNVs) encompassing circadian pathway genes and insomnia risk genes to ASD risk as well as sleep disturbances in children with ASD. We studied 5860 ASD probands and 2092 unaffected siblings from the Simons Simplex Collection (SSC) and MSSNG database, as well as 7509 individuals from two unselected populations (IMAGEN and Generation Scotland). Sleep duration and insomnia symptoms were parent reported for SSC probands. We identified 335 and 616 rare CNVs encompassing circadian and insomnia risk genes respectively. Deletions and duplications with circadian genes were overrepresented in ASD probands compared to siblings and unselected controls. For insomnia-risk genes, deletions (not duplications) were associated with ASD in both cohorts. Results remained significant after adjusting for cognitive ability. CNVs containing circadian pathway and insomnia risk genes showed a stronger association with ASD, compared to CNVs containing other genes. Circadian genes did not influence sleep duration or insomnia traits in ASD. Insomnia risk genes intolerant to haploinsufficiency increased risk for insomnia when duplicated. CNVs encompassing circadian and insomnia risk genes increase ASD liability with little to no observable impacts on sleep disturbances

How achievable are COVID-19 clinical trial recruitment targets? A UK observational cohort study and trials registry analysis

Objectives: To analyse enrolment to interventional trials during the first wave of the COVID-19 pandemic in England and describe the barriers to successful recruitment in the circumstance of a further wave or future pandemics. Design: We analysed registered interventional COVID-19 trial data and concurrently did a prospective observational study of hospitalised patients with COVID-19 who were being assessed for eligibility to one of the RECOVERY, C19-ACS or SIMPLE trials. Setting: Interventional COVID-19 trial data were analysed from the clinicaltrials.gov and International Standard Randomized Controlled Trial Number databases on 12 July 2020. The patient cohort was taken from five centres in a respiratory National Institute for Health Research network. Population and modelling data were taken from published reports from the UK government and Medical Research Council Biostatistics Unit. Participants: 2082 consecutive admitted patients with laboratory-confirmed SARS-CoV-2 infection from 27 March 2020 were included. Main outcome measures: Proportions enrolled, and reasons for exclusion from the aforementioned trials. Comparisons of trial recruitment targets with estimated feasible recruitment numbers. Results: Analysis of trial registration data for COVID-19 treatment studies enrolling in England showed that by 12 July 2020, 29 142 participants were needed. In the observational study, 430 (20.7%) proceeded to randomisation. 82 (3.9%) declined participation, 699 (33.6%) were excluded on clinical grounds, 363 (17.4%) were medically fit for discharge and 153 (7.3%) were receiving palliative care. With 111 037 people hospitalised with COVID-19 in England by 12 July 2020, we determine that 22 985 people were potentially suitable for trial enrolment. We estimate a UK hospitalisation rate of 2.38%, and that another 1.25 million infections would be required to meet recruitment targets of ongoing trials. Conclusions: Feasible recruitment rates, study design and proliferation of trials can limit the number, and size, that will successfully complete recruitment. We consider that fewer, more appropriately designed trials, prioritising cooperation between centres would maximise productivity in a further wave

Copy-number variants and polygenic risk for intelligence confer risk for autism spectrum disorder irrespective of their effects on cognitive ability

IntroductionRare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer susceptibility for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD susceptibility, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability.MethodsIn a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability.ResultsBottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that susceptibility for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD.DiscussionOur findings suggest that cognitive ability itself may not be the factor driving the underlying liability for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits

Mechanical design of the optical modules intended for IceCube-Gen2

IceCube-Gen2 is an expansion of the IceCube neutrino observatory at the South Pole that aims to increase the sensitivity to high-energy neutrinos by an order of magnitude. To this end, about 10,000 new optical modules will be installed, instrumenting a fiducial volume of about 8 km3. Two newly developed optical module types increase IceCube’s current sensitivity per module by a factor of three by integrating 16 and 18 newly developed four-inch PMTs in specially designed 12.5-inch diameter pressure vessels. Both designs use conical silicone gel pads to optically couple the PMTs to the pressure vessel to increase photon collection efficiency. The outside portion of gel pads are pre-cast onto each PMT prior to integration, while the interiors are filled and cast after the PMT assemblies are installed in the pressure vessel via a pushing mechanism. This paper presents both the mechanical design, as well as the performance of prototype modules at high pressure (70 MPa) and low temperature (−40∘C), characteristic of the environment inside the South Pole ice

The next generation neutrino telescope: IceCube-Gen2

The IceCube Neutrino Observatory, a cubic-kilometer-scale neutrino detector at the geographic South Pole, has reached a number of milestones in the field of neutrino astrophysics: the discovery of a high-energy astrophysical neutrino flux, the temporal and directional correlation of neutrinos with a flaring blazar, and a steady emission of neutrinos from the direction of an active galaxy of a Seyfert II type and the Milky Way. The next generation neutrino telescope, IceCube-Gen2, currently under development, will consist of three essential components: an array of about 10,000 optical sensors, embedded within approximately 8 cubic kilometers of ice, for detecting neutrinos with energies of TeV and above, with a sensitivity five times greater than that of IceCube; a surface array with scintillation panels and radio antennas targeting air showers; and buried radio antennas distributed over an area of more than 400 square kilometers to significantly enhance the sensitivity of detecting neutrino sources beyond EeV. This contribution describes the design and status of IceCube-Gen2 and discusses the expected sensitivity from the simulations of the optical, surface, and radio components

Sensitivity of IceCube-Gen2 to measure flavor composition of Astrophysical neutrinos

The observation of an astrophysical neutrino flux in IceCube and its detection capability to separate between the different neutrino flavors has led IceCube to constraint the flavor content of this flux. IceCube-Gen2 is the planned extension of the current IceCube detector, which will be about 8 times larger than the current instrumented volume. In this work, we study the sensitivity of IceCube-Gen2 to the astrophysical neutrino flavor composition and investigate its tau neutrino identification capabilities. We apply the IceCube analysis on a simulated IceCube-Gen2 dataset that mimics the High Energy Starting Event (HESE) classification. Reconstructions are performed using sensors that have 3 times higher quantum efficiency and isotropic angular acceptance compared to the current IceCube optical modules. We present the projected sensitivity for 10 years of data on constraining the flavor ratio of the astrophysical neutrino flux at Earth by IceCube-Gen2

Direction reconstruction performance for IceCube-Gen2 Radio

The IceCube-Gen2 facility will extend the energy range of IceCube to ultra-high energies. The key component to detect neutrinos with energies above 10 PeV is a large array of in-ice radio detectors. In previous work, direction reconstruction algorithms using the forward-folding technique have been developed for both shallow (≲20 m) and deep in-ice detectors, and have also been successfully used to reconstruct cosmic rays with ARIANNA. Here, we focus on the reconstruction algorithm for the deep in-ice detector, which was recently introduced in the context of the Radio Neutrino Observatory in Greenland (RNO-G)

Estimating the coincidence rate between the optical and radio array of IceCube-Gen2

The IceCube-Gen2 Neutrino Observatory is proposed to extend the all-flavour energy range of IceCube beyond PeV energies. It will comprise two key components: I) An enlarged 8km3 in-ice optical Cherenkov array to measure the continuation of the IceCube astrophysical neutrino flux and improve IceCube\u27s point source sensitivity above ∼100TeV; and II) A very large in-ice radio array with a surface area of about 500km2. Radio waves propagate through ice with a kilometer-long attenuation length, hence a sparse radio array allows us to instrument a huge volume of ice to achieve a sufficient sensitivity to detect neutrinos with energies above tens of PeV. The different signal topologies for neutrino-induced events measured by the optical and in-ice radio detector - the radio detector is mostly sensitive to the cascades produced in the neutrino interaction, while the optical detector can detect long-ranging muon and tau leptons with high accuracy - yield highly complementary information. When detected in coincidence, these signals will allow us to reconstruct the neutrino energy and arrival direction with high fidelity. Furthermore, if events are detected in coincidence with a sufficient rate, they resemble the unique opportunity to study systematic uncertainties and to cross-calibrate both detector components

Deep Learning Based Event Reconstruction for the IceCube-Gen2 Radio Detector

The planned in-ice radio array of IceCube-Gen2 at the South Pole will provide unprecedented sensitivity to ultra-high-energy (UHE) neutrinos in the EeV range. The ability of the detector to measure the neutrino’s energy and direction is of crucial importance. This contribution presents an end-to-end reconstruction of both of these quantities for both detector components of the hybrid radio array (\u27shallow\u27 and \u27deep\u27) using deep neural networks (DNNs). We are able to predict the neutrino\u27s direction and energy precisely for all event topologies, including the electron neutrino charged-current (νe-CC) interactions, which are more complex due to the LPM effect. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling a measurement of the neutrino energy and direction. We discuss how we can use normalizing flows to predict the PDF for each individual event which allows modeling the complex non-Gaussian uncertainty contours of the reconstructed neutrino direction. Finally, we discuss how this work can be used to further optimize the detector layout to improve its reconstruction performance