SDSS J162520.29+120308.7 – a new SU Ursae Majoris star in the period gap

We report results of an extensive world-wide observing campaign devoted to the recently discovered dwarf nova SDSS J162520.29+120308.7 (SDSS J1625). The data were obtained during the July 2010 eruption of the star and in August and September 2010 when the object was in quiescence. During the July 2010 superoutburst, SDSS J1625 clearly displayed superhumps with a mean period of Psh = 0.095942(17) days (138.16 ± 0.02 min) and a maximum amplitude reaching almost 0.4 mag. The superhump period was not stable, decreasing very rapidly at a rate of ˙P = −1.63(14) × 10−3 at the beginning of the superoutburst and increasing at a rate of ˙P = 2.81(20) × 10−4 in the middle phase. At the end of the superoutburst, it stabilized around the value of Psh = 0.09531(5) day. During the first twelve hours of the superoutburst, a low-amplitude double wave modulation was observed whose properties are almost identical to early superhumps observed in WZ Sge stars. The period of early superhumps, the period of modulations observed temporarily in quiescence, and the period derived from radial velocity variations are the same within measurement errors, allowing us to estimate the most probable orbital period of the binary to be Porb = 0.09111(15) days (131.20 ± 0.22 min). This value clearly indicates that SDSS J1625 is another dwarf nova in the period gap. Knowledge of the orbital and superhump periods allows us to estimate the mass ratio of the system to be q ≈ 0.25. This high value poses serious problems for both the thermal and tidal instability (TTI) model describing the behaviour of dwarf novae and for some models explaining the origin of early superhumps

Engaging communities in addressing air quality: a scoping review

Abstract Background Exposure to air pollution has a detrimental effect on health and disproportionately affects people living in socio-economically disadvantaged areas. Engaging with communities to identify concerns and solutions could support organisations responsible for air quality control, improve environmental decision-making, and widen understanding of air quality issues associated with health. This scoping review aimed to provide an overview of approaches used to engage communities in addressing air quality and identify the outcomes that have been achieved. Methods Searches for studies that described community engagement in air quality activities were conducted across five databases (Academic Search Complete, CABI, GreenFILE, MEDLINE, Web of Science). Data on study characteristics, community engagement approach, and relevant outcomes were extracted. The review process was informed by a multi-stakeholder group with an interest in and experience of community engagement in air quality. Thirty-nine papers from thirty studies were included in the final synthesis. Conclusion A range of approaches have been used to engage communities in addressing air quality, most notably air quality monitoring. Positive outcomes included increased awareness, capacity building, and changes to organisational policy and practice. Longer-term projects and further exploration of the impact of community engagement on improving air quality and health are needed as reporting on these outcomes was limited. </jats:sec

Prey differences drive local genetic adaptation in Antarctic fur seals

Antarctic fur seal Arctocephalus gazella colonies are found on sub-Antarctic islands around the continent. These islands experience a range of conditions in terms of physical and biological habitat, creating a natural laboratory to investigate local genetic adaptation. One striking habitat difference is in the availability of Euphausia superba krill as prey, which has led to A. gazella exhibiting a range of diets. A. gazella in some colonies consume exclusively krill, while their conspecifics in other colonies feed mainly on fish and consume few to no krill. To investigate potential adaptations to these different prey fields, reduced representation genome sequencing was conducted on A. gazella from all 8 of the major colonies. Twenty-seven genomic regions exhibiting signatures of natural selection were identified. Two of these genomic regions were clearly associated with seals living in krill-dominated areas or those in fish-dominated areas. Twenty-two additional genomic regions under selection showed a pattern consistent with prey differences as the driver of selection, after historical migrations from krill-dominated habitats where lineages evolved to present krill-poor habitat areas were taken into account. Only 1 of the genomic regions identified appeared to be explained by any other environmental variable analysed (depth). Genomic regions under prey-driven selection included genes associated with regulation of gene expression, skeletal development, and lipid metabolism. Adaptation to local prey has implications for spatial management of this species and for the potential impacts of climate- or harvest-driven reductions in krill abundance on these seals.The Norwegian Antarctic Research Expeditions (NARE) programmehttp://www.int-res.com/journals/meps/meps-homehj2020Mammal Research Institut

Boron Nitride Nanotube: Synthesis and Applications

Scientists have predicted that carbon's immediate neighbors on the periodic chart, boron and nitrogen, may also form perfect nanotubes, since the advent of carbon nanotubes (CNTs) in 1991. First proposed then synthesized by researchers at UC Berkeley in the mid 1990's, the boron nitride nanotube (BNNT) has proven very difficult to make until now. Herein we provide an update on a catalyst-free method for synthesizing highly crystalline, small diameter BNNTs with a high aspect ratio using a high power laser under a high pressure and high temperature environment first discovered jointly by NASA/NIA JSA. Progress in purification methods, dispersion studies, BNNT mat and composite formation, and modeling and diagnostics will also be presented. The white BNNTs offer extraordinary properties including neutron radiation shielding, piezoelectricity, thermal oxidative stability (> 800 C in air), mechanical strength, and toughness. The characteristics of the novel BNNTs and BNNT polymer composites and their potential applications are discussed

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery