Breeding for resistance to Varroa destructor in North America provides the long-term solution to the economic troubles the mite brings. This review reports the development of two breeding successes that have produced honey bees of commercial quality that do not require pesticide treatment to control Varroa, highlights other traits that could be combined to increase resistance and examines the potential uses of marker-assisted selection (MAS) for breeding for Varroa resistance. Breeding work continues with these stocks to enhance their commercial utility. This work requires knowledge of the mechanisms of resistance that can be further developed or improved in selected stocks and studied with molecular techniques as a prelude to MAS.Varroa resistance / breeding program / Russian honey bees / Varroa-sensitive hygiene / marker-assisted selection

Motivation Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA). Results We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data. We evaluate dtangle on 11 benchmark datasets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle’s estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. Availability and implementation dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). Supplementary information Supplementary data are available at Bioinformatics online.

Complex tissues are composed of a large number of different types of cells, each involved in a multitude of biological processes. Consequently, an important component to understanding such processes is understanding the cell-type composition of the tissues. Estimating cell type composition using high-throughput gene expression data is known as cell-type deconvolution. In this paper, we first summarize the extensive deconvolution literature by identifying a common regression-like approach to deconvolution. We call this approach the Unified Deconvolution-as-Regression (UDAR) framework. While methods that fall under this framework all use a similar model, they fit using data on different scales. Two popular scales for gene expression data are logarithmic and linear. Unfortunately, each of these scales has problems in the UDAR framework. Using log-scale gene expressions proposes a biologically implausible model and using linear-scale gene expressions will lead to statistically inefficient estimators. To overcome these problems, we propose a new approach for cell-type deconvolution that works on a hybrid of the two scales. This new approach is biologically plausible and improves statistical efficiency. We compare the hybrid approach to other methods on simulations as well as a collection of eleven real benchmark datasets. Here, we find the hybrid approach to be accurate and robust.deconvolution, gene expression, microarray, RNA-seq .

A study was conducted to identify quantitative trait loci (QTLs) that affect learning in honeybees. Two F1 supersister queens were produced from a cross between two established lines that had been selected for differences in the speed at which they reverse a learned discrimination between odors. Different families of haploid drones from two of these F1 queens were evaluated for two kinds of learning performance--reversal learning and latent inhibition--which previously showed correlated selection responses. Random amplified polymorphic DNA markers were scored from recombinant, haploid drone progeny that showed extreme manifestations of learning performance. Composite interval mapping procedures identified two QTLs for reversal learning (lrn2 and lrn3: LOD, 2.45 and 2.75, respectively) and one major QTL for latent inhibition (lrn1: LOD, 6.15). The QTL for latent inhibition did not map to either of the linkage groups that were associated with reversal learning. Identification of specific genes responsible for these kinds of QTL associations will open up new windows for better understanding of genes involved in learning and memory.

Measuring the properties of scattered light is central to many laser-based gas diagnostic techniques, such as filtered Rayleigh scattering (FRS). Alongside the measurements, a model of the scattered light’s spectral lineshape is often used to extract quantitative information about the flow field like pressure, temperature, and velocity. In particular, the Tenti S6 or S7 model are frequently used to model the lineshape of Rayleigh–Brillouin (RB) scattered light. While accurate, it is well attested in the literature that these models can be computationally expensive when evaluated many times, for example, as part of iterative estimation or optimization routines. To overcome this, approximations of these spectral lineshape models can be used instead. In this paper, we develop a method called support vector spectrum approximation (SVSA). This method uses support vector regression and singular value decomposition to create efficient, accurate, and well-conditioned approximations of any existing spectral lineshape model. The SVSA framework improves upon existing approximation methods by allowing quick calculation of spectral lineshapes for arbitrary flow regimes with any number of input parameters over a wide range of values. We demonstrate the efficacy of SVSA in approximating coherent and spontaneous RB scattering spectra. In application, we use SVSA to optimize the design of a filtered Rayleigh scattering experiment of a complex shock-dominated flow. SVSA allows us to comprehensively minimize expected measurement uncertainty of number density and temperature for this experiment. It does this by enabling a high-resolution design of experiments that is otherwise intractable.

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Near the end of its mission, NASA's Cassini spacecraft performed several low-altitude passes across Saturn's auroral region. We present ultraviolet auroral imagery and various coincident particle and field measurements of two such passes, providing important information about the structure and dynamics of Saturn's auroral acceleration region. In upward field-aligned current regions, upward proton beams are observed to reach energies of several tens of keV; the associated precipitating electron populations are found to have mean energies of about 10 keV. With no significant wave activity being apparent, these findings indicate strong parallel potentials responsible for auroral acceleration, about 100 times stronger than at Earth. This is further supported by observations of proton conics in downward field-aligned current regions above the acceleration region, which feature a lower energy cutoff above ∼50 keV-indicating energetic proton populations trapped by strong parallel potentials while being transversely energized until they can overcome the trapping potential, likely through wave-particle interactions. A spacecraft pass through a downward current region at an altitude near the acceleration region reveals plasma wave features, which may be driving the transverse proton acceleration generating the conics. Overall, the signatures observed resemble those related to the terrestrial and Jovian aurorae, the particle energies and potentials at Saturn appearing to be significantly higher than at Earth and comparable to those at Jupiter.Plain Language Summary NASA's Cassini spacecraft orbited closer to Saturn than ever before during the last stage of its mission, the "Grand Finale". This allowed the onboard instruments to measure charged particles and plasma waves directly above the auroral region while simultaneously providing high-resolution imagery of the ultraviolet aurorae. Based on observations of highly energetic ions streaming away from the planet in regions of low plasma wave activity, we infer the existence of strong electric fields which act to accelerate electrons down into the atmosphere, driving the bright auroral emissions. Our estimates of the average energy of the precipitating electrons support this finding. Charged ions sometimes seem to be energized by plasma waves above the aurorae before they can escape, but the exact process in which this happens is not fully understood. Most signatures presented here resemble those observed in relation to Earth's aurorae, suggesting that the mechanisms acting at both planets are quite similar although Saturn's acceleration mechanism is significantly stronger.

Motivation: Understanding cell type composition is important to understanding many biological processes. Furthermore, in gene expression studies cell type composition can confound differential expression analysis (DEA). To aid understanding cell type composition, methods of estimating (deconvolving) cell type proportions from gene expression data have been developed. Results: We propose dtangle, a new cell-type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell-type proportions using publicly available, often cross-platform, reference data. To comprehensively evaluate dtangle, we assemble ten benchmark data sets. Here, dtangle is competitive with published deconvolution methods, is robust to selection of tuning parameters and is quicker than other methods. As a case study, we investigate the human immune response to Lyme disease. dtangle's estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. Availability: dtangle is on CRAN