Effects of spinosad on honey bees (Apis mellifera): Findings from over ten years of testing and commercial use

Background: Spinosad is widely used as an insecticide in crop protection against thysanopteran, lepidopteran and dipteran species. As such it is intrinsically toxic to insects and among them to the honey bee (Apis mellifera). An updated risk assessment is presented in the context of the regulatory evaluation of spinosad products and is in accordance with the latest recommendation of regulatory guidance documents. Results: The intrinsic toxicity to the honey bee as observed in laboratory conditions through oral and contact tests on adults does not appear to impair honey bee colonies when exposed to treated attractive crops in tunnel conditions. Reasons for this could include reduced availability of residues of the product on plant surface compared to laboratory conditions, together with a fast dissipation from treated plants and the absence of active degradation products. Conclusions: Spinosad products present a negligible impact on honey bees when used under the current label recommendations and conditions of agricultural use. This conclusion deduced from data available for the regulatory risk assessment has been confirmed by the feedback of surveys on incidents, which address the potential impact of spinosad products under realistic conditions of exposure, including other environmental and chemical factors that are common in cropped areas. Keywords: honey bee, pesticide, risk assessment, risk management, spinosa

Thermal structure of a gas-permeable lava dome and timescale separation in its response to perturbation

The thermal boundary layer at the surface of a volcanic lava dome is investigated through a continuum model of the thermodynamic advection diffusion processes resulting from magmatic gas flow through the dome matrix. The magmatic gas mass flux, porosity and permeability of the rock are identified as key parameters. New, theoretical, nonlinear steady-state thermal profiles are reported which give a realistic surface temperature of 210 degC for a region of lava dome surface through which a gas flux of 3.5 x 10-3 kg s-1 m-2 passes. This contrasts favourably with earlier purely diffusive thermal models, which cool too quickly. Results are presented for time-dependent perturbations of the steady states as a response to: changes in surface pressure, a sudden rockfall from the lava dome surface, and a change in the magmatic gas mass flux at depth. Together with a generalized analysis using the method of multiple scales, this identifies two characteristic time scales associated with the thermal evolution of a dome carapace: a short time scale of several minutes, over which the magmatic gas mass flux, density, and pressure change to a new quasi-steady-state, and a longer time scale of several days, over which the thermal profile changes to a new equilibrium distribution. Over the longer time scale the dynamic properties of the dome continue to evolve, but only in slavish response to the ongoing temperature evolution. In the light of this time scale separation, the use of surface temperature measurements to infer changes in the magmatic gas flux for use in volcanic hazard prediction is discussed

Contrasting responses of mean and extreme snowfall to climate change

Snowfall is an important element of the climate system, and one that is expected to change in a warming climate. Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts. Simulations with climate models indicate that annual mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures. The response of heavy snowfall events to a changing climate, however, is unclear. Here I show that in simulations with climate models under a scenario of high emissions of greenhouse gases, by the late twenty-first century there are smaller fractional changes in the intensities of daily snowfall extremes than in mean snowfall over many Northern Hemisphere land regions. For example, for monthly climatological temperatures just below freezing and surface elevations below 1,000 metres, the 99.99th percentile of daily snowfall decreases by 8% in the multimodel median, compared to a 65% reduction in mean snowfall. Both mean and extreme snowfall must decrease for a sufficiently large warming, but the climatological temperature above which snowfall extremes decrease with warming in the simulations is as high as −9 °C, compared to −14 °C for mean snowfall. These results are supported by a physically based theory that is consistent with the observed rain–snow transition. According to the theory, snowfall extremes occur near an optimal temperature that is insensitive to climate warming, and this results in smaller fractional changes for higher percentiles of daily snowfall. The simulated changes in snowfall that I find would influence surface snow and its hazards; these changes also suggest that it may be difficult to detect a regional climate-change signal in snowfall extremes.National Science Foundation (U.S.) (Grant AGS-1148594)United States. National Aeronautics and Space Administration (ROSES Grant 09-IDS09-0049

Malaria and obesity: obese mice are resistant to cerebral malaria

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Segment-Specific Neuronal Subtype Specification by the Integration of Anteroposterior and Temporal Cues

To address the question of how neuronal diversity is achieved throughout the CNS, this study provides evidence of modulation of neural progenitor cell “output” along the body axis by integration of local anteroposterior and temporal cues

Contribution of Distinct Homeodomain DNA Binding Specificities to Drosophila Embryonic Mesodermal Cell-Specific Gene Expression Programs

Homeodomain (HD) proteins are a large family of evolutionarily conserved transcription factors (TFs) having diverse developmental functions, often acting within the same cell types, yet many members of this family paradoxically recognize similar DNA sequences. Thus, with multiple family members having the potential to recognize the same DNA sequences in cis-regulatory elements, it is difficult to ascertain the role of an individual HD or a subclass of HDs in mediating a particular developmental function. To investigate this problem, we focused our studies on the Drosophila embryonic mesoderm where HD TFs are required to establish not only segmental identities (such as the Hox TFs), but also tissue and cell fate specification and differentiation (such as the NK-2 HDs, Six HDs and identity HDs (I-HDs)). Here we utilized the complete spectrum of DNA binding specificities determined by protein binding microarrays (PBMs) for a diverse collection of HDs to modify the nucleotide sequences of numerous mesodermal enhancers to be recognized by either no or a single subclass of HDs, and subsequently assayed the consequences of these changes on enhancer function in transgenic reporter assays. These studies show that individual mesodermal enhancers receive separate transcriptional input from both I–HD and Hox subclasses of HDs. In addition, we demonstrate that enhancers regulating upstream components of the mesodermal regulatory network are targeted by the Six class of HDs. Finally, we establish the necessity of NK-2 HD binding sequences to activate gene expression in multiple mesodermal tissues, supporting a potential role for the NK-2 HD TF Tinman (Tin) as a pioneer factor that cooperates with other factors to regulate cell-specific gene expression programs. Collectively, these results underscore the critical role played by HDs of multiple subclasses in inducing the unique genetic programs of individual mesodermal cells, and in coordinating the gene regulatory networks directing mesoderm development.National Institutes of Health (U.S.) (Grant R01 HG005287

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns