The Next Generation of Axion Helioscopes: The International Axion Observatory (IAXO)

The International Axion Observatory (IAXO) is a proposed 4th-generation axion helioscope with the primary physics research goal to search for solar axions via their Primakoff conversion into photons of 1 \u2013 10 keV energies in a strong magnetic field. IAXO will achieve a sensitivity to the axion-photon coupling ga\u3b3 down to a few 710 1212 GeV 121 for a wide range of axion masses up to 3c 0.25 eV. This is an improvement over the currently best (3rd generation) axion helioscope, the CERN Axion Solar Telescope (CAST), of about 5 orders of magnitude in signal strength, corresponding to a factor 3c 20 in the axion photon coupling. IAXO's sensitivity relies on the construction of a large superconducting 8-coil toroidal magnet of 20 m length optimized for axion research. Each of the eight 60 cm diameter magnet bores is equipped with x-ray optics focusing the signal photons into 3c 0.2 cm2 spots that are imaged by very low background x-ray detectors. The magnet will be built into a structure with elevation and azimuth drives that will allow solar tracking for 12 hours each day. This contribution is a summary of our papers [1], [2] and [3] and we refer to these for further details

The IAXO Helioscope

The IAXO (International Axion Experiment) is a fourth generation helioscope with a sensitivity, in terms of detectable signal counts, at least 104 better than CAST phase-I, resulting in sensitivity on ga¿ one order of magnitude better. To achieve this performance IAXO will count on a 8-coil toroidal magnet with 60 cm diameter bores and equipped with X-ray focusing optics into 0.20 cm2 spots coupled to ultra-low background Micromegas X-ray detectors. The magnet will be on a platform that will allow solar tracking for 12 hours per day. The next short term objectives are to prepare a Technical Design Report and to construct the first prototypes of the hardware main ingredients: demonstration coil, X-ray optics and low background detector while refining the physics case and studying the feasibility studies for Dark Matter axions

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

An update on the Axion Helioscopes front: current activities at CAST and the IAXO project

Although they have not yet been detected, axions and axion-like particles (ALPs) continue to maintain the interest (even increasingly so) of the rare-event searches community as viable candidates for the Dark Matter of the Universe but also as a solution for several other puzzles of astrophysics. Their property of coupling to photons has inspired different experimental methods for their detection, one of which is the helioscope technique. The CERN Axion Solar Telescope (CAST) is the most sensitive helioscope built up to date and has recently published part of the latest data taken with the magnet bores gradually filled with 3He, probing the mass range up to 1.17 eV. The International AXion Observatory (IAXO) is being proposed as a facility where different axion studies can be performed, with the primary goal to study axions coming from the Sun. Designed to maximize sensitivity, it will improve the levels reached by CAST by almost 5 orders of magnitude in signal detection, that is more than one order of magnitude in terms of gaγ. Here we will summarize the most important aspects of the helioscopes, and focus mainly on IAXO, based on the recent papers [1, 2]

Effects of eight neuropsychiatric copy number variants on human brain structure

peer reviewedMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions. © 2021, The Author(s)