Cosmic Needles versus Cosmic Microwave Background Radiation

It has been suggested by a number of authors that the 2.7K cosmic microwave background (CMB) radiation might have arisen from the radiation from Population III objects thermalized by conducting cosmic graphite/iron needle-shaped dust. Due to lack of an accurate solution to the absorption properties of exceedingly elongated grains, in existing literature which studies the CMB thermalizing process they are generally modelled as (1) needle-like spheroids in terms of the Rayleigh approximation; (2) infinite cylinders; and (3) the antenna theory. We show here that the Rayleigh approximation is not valid since the Rayleigh criterion is not satisfied for highly conducting needles. We also show that the available intergalactic iron dust, if modelled as infinite cylinders, is not sufficient to supply the required opacity at long wavelengths to obtain the observed isotropy and Planckian nature of the CMB. If appealing to the antenna theory, conducting iron needles with exceedingly large elongations (10^4) appear able to provide sufficient opacity to thermalize the CMB within the iron density limit. But the applicability of the antenna theory to exceedingly thin needles of nanometer/micrometer in thickness needs to be justified.Comment: 13 pages, 4 figures; submitted to ApJ

Spiral valve parasites of blue and common thresher sharks as indicators of shark feeding behaviour and ecology

Open Access via the Jisc Wiley agreement Acknowledgements This work would not have been possible without the assistance and samples provided by the National Marine Fisheries Service (NMFS) Southwest Region Fishery Observer Program and the participating drift gillnet fishermen. A. Arevalo, E. Reed, H. Colley, J. Williams, J. Tamez and K. Tran assisted with spiral valve dissections and parasite sorting in the lab. D. Losey helped with library research. D. Sweetnam, A. Yau, A. Thompson, M. Craig, S. Stohs, G. DiNardo provided constructive critiques that helped improve the manuscript. This research was supported by the National Oceanographic Atmospheric Administration (NOAA).Peer reviewedPublisher PD

Learned vocal variation is associated with abrupt cryptic genetic change in a parrot species complex

<div><p>Contact zones between subspecies or closely related species offer valuable insights into speciation processes. A typical feature of such zones is the presence of clinal variation in multiple traits. The nature of these traits and the concordance among clines are expected to influence whether and how quickly speciation will proceed. Learned signals, such as vocalizations in species having vocal learning (e.g. humans, many birds, bats and cetaceans), can exhibit rapid change and may accelerate reproductive isolation between populations. Therefore, particularly strong concordance among clines in learned signals and population genetic structure may be expected, even among continuous populations in the early stages of speciation. However, empirical evidence for this pattern is often limited because differences in vocalisations between populations are driven by habitat differences or have evolved in allopatry. We tested for this pattern in a unique system where we may be able to separate effects of habitat and evolutionary history. We studied geographic variation in the vocalizations of the crimson rosella (<em>Platycercus elegans</em>) parrot species complex. Parrots are well known for their life-long vocal learning and cognitive abilities. We analysed contact calls across a <em>ca</em> 1300 km transect encompassing populations that differed in neutral genetic markers and plumage colour. We found steep clinal changes in two acoustic variables (fundamental frequency and peak frequency position). The positions of the two clines in vocal traits were concordant with a steep cline in microsatellite-based genetic variation, but were discordant with the steep clines in mtDNA, plumage and habitat. Our study provides new evidence that vocal variation, in a species with vocal learning, can coincide with areas of restricted gene flow across geographically continuous populations. Our results suggest that traits that evolve culturally can be strongly associated with reduced gene flow between populations, and therefore may promote speciation, even in the absence of other barriers.</p> </div

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing