Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine.

Biological soil thin-sections and a combination of image analysis and geostatistical tools were used to conduct a detailed investigation into the distribution of bacteria in soil and their relationship with pores. The presence of spatial patterns in the distribution of bacteria was demonstrated at the microscale, with ranges of spatial autocorrelation of 1 mm and below. Bacterial density gradients were found within bacterial patches in topsoil samples and also in one subsoil sample. Bacterial density patches displayed a mosaic of high and low values in the remaining subsoil samples. Anisotropy was detected in the spatial structure of pores, but was not detected in relation to the distribution of bacteria. No marked trend as a function of distance to the nearest pore was observed in bacterial density values in the topsoil, but in the subsoil bacterial density was greatest close to pores and decreased thereafter. Bacterial aggregation was greatest in the cropped topsoil, though no consistent trends were found in the degree of bacterial aggregation as a function of distance to the nearest pore. The implications of the results presented for modelling and predicting bacterial activity in soil are discussed.

Very little is known about the spatial organization of soil microbes across scales that are relevant both to microbial function and to field-based processes. The spatial distributions of microbes and microbially mediated activity have a high intrinsic variability. This can present problems when trying to quantify the effects of disturbance, management practices, or climate change on soil microbial systems and attendant function. A spatial sampling regime was implemented in an arable field. Cores of undisturbed soil were sampled from a 3 x 3 x 0.9 m volume of soil (topsoil and subsoil) and a biological thin section, in which the in situ distribution of bacteria could be quantified, prepared from each core. Geostatistical analysis was used to quantify the nature of spatial structure from micrometers to meters and spatial point pattern analysis to test for deviations from complete spatial randomness of mapped bacteria. Spatial structure in the topsoil was only found at the microscale (micrometers), whereas evidence for nested scales of spatial structure was found in the subsoil (at the microscale, and at the centimeter to meter scale). Geostatistical ranges of spatial structure at the micro scale were greater in the topsoil and tended to decrease with depth in the subsoil. Evidence for spatial aggregation in bacteria was stronger in the topsoil and also decreased with depth in the subsoil, though extremely high degrees of aggregation were found at very short distances in the deep subsoil. The data suggest that factors that regulate the distribution of bacteria in the subsoil operate at two scales, in contrast to one scale in the topsoil, and that bacterial patches are larger and more prevalent in the topsoil.

Abstract.The

No abstract

[1] Theoretical analysis of energetics of the Ekman layer by incorporating the CoriolisStokes forcing into the classical Ekman model shows that the wind energy input to the Ekman layer has two components: the work done by the wind stress on the surface Ekman current and that done by the Coriolis-Stokes forcing on the whole body of water in the mixed layer. Under the assumption of constant vertical diffusivity, analytical forms of the direct wind energy input and the Stokes drift-induced energy input are derived. Assessments of relative importance of surface waves are made by comparing the wind energy input into the Ekman layer with and without wave-induced Stokes drift effects included. Using the European Centre for Medium-Range Weather Forecasts 40-year reanalysis wind stress and surface wave data sets, the total rate of wind energy input into the Ekman layer within the Antarctic Circumpolar Current (ACC) is estimated to be 833 GW, in which the direct wind energy input is 650 GW (78%), and the Stokes driftinduced energy input is 183 GW (22%). The total mechanical energy input into the ACC due to wave effects is increased by approximately 4% (30 GW) compared to that into the classical Ekman layer. Long-term variability of direct wind and Stokes drift-induced energy inputs to the ACC is also examined.Citation: Wu, K., and B. Liu (2008), Stokes drift -induced and direct wind energy inputs into the Ekman layer within the Antarctic Circumpolar Current,

No abstract

[1] A three-dimensional wave-current coupled modeling system is used to examine the influence of waves on coastal currents and sea level. This coupled modeling system consists of the wave model-WAM (Cycle 4) and the Princeton Ocean Model (POM). The results from this study show that it is important to incorporate surface wave effects into coastal storm surge and circulation models. Specifically, we find that (1) storm surge models without coupled surface waves generally under estimate not only the peak surge but also the coastal water level drop which can also cause substantial impact on the coastal environment, (2) introducing wave-induced surface stress effect into storm surge models can significantly improve storm surge prediction, (3) incorporating wave-induced bottom stress into the coupled wave-current model further improves storm surge prediction, and (4) calibration of the wave module according to minimum error in significant wave height does not necessarily result in an optimum wave module in a wave-current coupled system for current and storm surge prediction.