A bird’s eye view: using circuit theory to study urban landscape connectivity for birds

Context Connectivity is fundamental to understanding how landscape form influences ecological function. However, uncertainties persist due to the difficulty and expense of gathering empirical data to drive or to validate connectivity models, especially in urban areas, where relationships are multifaceted and the habitat matrix cannot be considered to be binary. Objectives This research used circuit theory to model urban bird flows (i.e. ‘current’), and compared results to observed abundance. The aims were to explore the ability of this approach to predict wildlife flows and to test relationships between modelled connectivity and variation in abundance. Methods Circuitscape was used to model functional connectivity in Bedford, Luton/Dunstable, and Milton Keynes, UK, for great tits (Parus major) and blue tits (Cyanistes caeruleus), drawing parameters from published studies of woodland bird flows in urban environments. Model performance was then tested against observed abundance data. Results Modelled current showed a weak yet positive agreement with combined abundance for P. major and C. caeruleus. Weaker correlations were found for other woodland species, suggesting the approach may be expandable if re-parameterised. Conclusions Trees provide suitable habitat for urban woodland bird species, but their location in large, contiguous patches and corridors along barriers also facilitates connectivity networks throughout the urban matrix. Urban connectivity studies are well-served by the advantages of circuit theory approaches, and benefit from the empirical study of wildlife flows in these landscapes to parameterise this type of modelling more explicitly. Such results can prove informative and beneficial in designing urban green space and new developments

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Association Study of Common Mitochondrial Variants and Cognitive Ability

Abstract Mitochondria are central to optimal functioning of the nervous system and disruption of mitochondrial function is known to lead to cognitive impairment. However, there has been little focus on whether common mitochondrial DNA polymorphisms contribute to normal variation in cognitive phenotypes. In this study, we use methodology for carrying out whole mitochondrial association studies in family cohorts to test whether 69 common mitochondrial variants and 10 common European haplogroups are associated with a number of measures of cognition, including information processing, word recognition and general cognitive ability, in a sample of Australian adolescent twins and their singleton/non-twin siblings. With data from 1,385 individuals from 665 families, this is by far the largest mitochondrial association study of cognition undertaken to date. We find that there is no significant evidence that either common European mitochondrial SNPs or haplogroups are associated with variation in cognitive performance. In spite of the associations not reaching significance, several of the most highly associated SNPs are in mitochondrial genes that have previously been identified as potentially playing a role in cognitive performance in mice. These genes warrant further investigation in both functional and association studies with larger cohorts

No association between general cognitive ability and rare copy number variation

There is increasing evidence for the role of rare copy-number variation (CNV) in the development of neuropsychiatric disorders. It is likely that such variants also have an effect on the variation of cognition in what is considered the "normal" phenotypic range. The role of rare CNV (> 20 KB in length; frequency < 5 %) on general cognitive ability is investigated in a sample of 800 individuals (mean age = 16.5, SD = 1.2) using copy-number variants called from the Illumina 610K SNP genotyping array with the software QuantiSNP. We assessed three measures of CNV burden-total CNV length, number of CNV and average CNV length-for both deletions and duplications in combination and separately. No correlation was found between any of the measures of CNV burden and IQ, or when comparing the top and bottom 10 % of the sample for IQ, both on a genome-wide scale and at individual positions across the genome