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.