Ankyrin-G coordinates assembly of the spectrin-based membrane skeleton, voltage-gated sodium channels, and L1 CAMs at Purkinje neuron initial segments

The axon initial segment is an excitable membrane highly enriched in voltage-gated sodium channels that integrates neuronal inputs and initiates action potentials. This study identifies Nav1.6 as the voltage-gated sodium channel isoform at mature Purkinje neuron initial segments and reports an essential role for ankyrin-G in coordinating the physiological assembly of Nav1.6, βIV spectrin, and the L1 cell adhesion molecules (L1 CAMs) neurofascin and NrCAM at initial segments of cerebellar Purkinje neurons. Ankyrin-G and βIV spectrin appear at axon initial segments by postnatal day 2, whereas L1 CAMs and Nav1.6 are not fully assembled at continuous high density along axon initial segments until postnatal day 9. L1 CAMs and Nav1.6 therefore do not initiate protein assembly at initial segments. βIV spectrin, Nav1.6, and L1 CAMs are not clustered in adult Purkinje neuron initial segments of mice lacking cerebellar ankyrin-G. These results support the conclusion that ankyrin-G coordinates the physiological assembly of a protein complex containing transmembrane adhesion molecules, voltage-gated sodium channels, and the spectrin membrane skeleton at axon initial segments

Structure and stress of Re(11%21); chiral terraces at a racemic surface

The surface structure and morphology of the clean Re(11%21) surface has been investigated through combined low energy electron diffraction intensity analysis of data taken at multiple angles of incidence, scanning tunneling microscopy, and first-principles density functional calculations. The results show how this globally racemic surface terminates in two chirally distinct terraces, which show largescale out-of-plane atomic relaxations and in-plane lateral movement of the uppermost atoms. We further identify and discuss the initial stages of step bunching upon adsorption of oxygen that leads ultimately to the large-scale faceting of the surface. Finally, we present calculations of surface stress and the response to applied surface strain, which suggest routes to the exertion of control over the expression of chirality at the surface

Payments for Ecosystem Services: Past, Present and Future

While we don’t tend to think about it, healthy ecosystems provide a variety of critical benefits. Ecosystem goods, the physical items an ecosystem provides, are obvious. Forests provide timber; coastal marshes provide shellfish. While less visible and generally taken for granted, the services underpinning these goods are equally important. Created by the interactions of living organisms with their environment, ecosystem services provide the conditions and processes that sustain human life.1 If you doubt this, consider how to grow an apple without pollination, pest control, or soil fertility. Once one realizes the importance of ecosystem services, three points quickly emerge: (1) landscapes provide a stream of services ranging from water quality and flood control to climate stability—the economic value of which can be significant; (2) the vast majority of these services are public goods and not exchanged in markets, so landowners have little incentive to provide these positive externalities; and (3) we, therefore, need to think creatively about creating markets for these services so they are not under-provided. This is the basis of the policy approach known as Payments for Ecosystem Services (“PES”)

Sunyaev-Zeldovich Cluster Counts as a Probe of Intra-Supercluster Gas

X-ray background surveys indicate the likely presence of diffuse warm gas in the Local Super Cluster (LSC), in accord with expectations from hydrodynamical simulations. We assess several other manifestations of warm LSC gas; these include anisotropy in the spatial pattern of cluster Sunyaev-Zeldovich (S-Z) counts, its impact on the CMB temperature power spectrum at the lowest multipoles, and implications on measurements of the S-Z effect in and around the Virgo cluster.Comment: 14 pages, 6 figures, draft versio

Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) × CFRmcg [R2 = 0.44, residual SD (sy.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) × DFRmcg (R2 = 0.53, sy.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P \u3c 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P \u3c 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency

Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) × CFRmcg [R2 = 0.44, residual SD (sy.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) × DFRmcg (R2 = 0.53, sy.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P \u3c 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P \u3c 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency