On the evolution of the solar wind between 1 and 5 AU at the time of the Cassini Jupiter flyby: Multispacecraft observations of interplanetary coronal mass ejections including the formation of a merged interaction region

The Cassini flyby of Jupiter occurred at a time near solar maximum. Consequently, the pre-Jupiter data set reveals clear and numerous transient perturbations to the Parker Spiral solar wind structure. Limited plasma data are available at Cassini for this period due to pointing restrictions imposed on the instrument. This renders the identification of the nature of such structures ambiguous, as determinations based on the magnetic field data alone are unreliable. However, a fortuitous alignment of the planets during this encounter allowed us to trace these structures back to those observed previously by the Wind spacecraft near the Earth. Of the phenomena that we are satisfactorily able to trace back to their manifestation at 1 AU, two are identified as being due to interplanetary coronal mass ejections. One event at Cassini is shown to be a merged interaction region, which is formed from the compression of a magnetic cloud by two anomalously fast solar wind streams. The flux-rope structure associated with this magnetic cloud is not as apparent at Cassini and has most likely been compressed and deformed. Confirmation of the validity of the ballistic projections used here is provided by results obtained from a one-dimensional magnetohydrodynamic projection of solar wind parameters measured upstream near the Earth. It is found that when the Earth and Cassini are within a few tens of degrees in heliospheric longitude, the results of this one-dimensional model predict the actual conditions measured at 5 AU to an impressive degree. Finally, the validity of the use of such one-dimensional projections in obtaining quasi-solar wind parameters at the outer planets is discussed.

Vibrational intensities VII: ethane and ethane-d6

Absolute intensity measurements have been made on the fundamental vibrations of C2H6 and C2D6, using the extrapolation method of Wilson and Wells and using nitrogen at pressures up to 50 atmospheres to broaden the bands. The absorption coefficient was integrated against the logarithm of the frequency. Normal coordinates were calculated from the potential function of Hansen and Dennison, and were used to interpret the results in terms of quantities (∂p/∂Si) giving the change of dipole moment with respect to the symmetry coordinates Si. Consistency of data between the isotopes was used both to eliminate ambiguities in the interpretation, and as a criterion in separating overlapping pairs of absorption bands. The results have been interpreted in terms of bond effective moments.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Combined affinity labelling and mass spectrometry analysis of differential cell surface protein expression in normal and prostate cancer cells

Differences in the expression of cell surface proteins between a normal prostate epithelial (1542-NP2TX) and a prostate cancer cell line (1542-CP3TX) derived from the same patient were investigated. A combination of affinity chromatographic purification of biotin-tagged surface proteins with mass spectrometry analysis identified 26 integral membrane proteins and 14 peripheral surface proteins. The findings confirm earlier reports of altered expression in prostate cancer for several cell surface proteins, including ALCAM/CD166, the Ephrin type A receptor, EGFR and the prostaglandin F2 receptor regulatory protein. In addition, several novel findings of differential expression were made, including the voltage-dependent anion selective channel proteins Porin 1 and 2, ecto-5'-nucleotidase (CD73) and Scavenger receptor B1. Cell surface protein expression changed both qualitatively and quantitatively when the cells were grown in the presence of either or both interferon INFalpha and INFgamma. Costimulation with type I and II interferons had additive or synergistic effects on the membrane density of several, mainly peripherally attached surface proteins. Concerted upregulation of surface exposed antigens may be of benefit in immuno-adjuvant-based treatment of interferon-responsive prostate cancer. In conclusion, this study demonstrates that differences in the expression of membrane proteins between normal and prostate cancer cells are reproducibly detectable following vectorial labelling with biotin, and that detailed analysis of extracellular-induced surface changes can be achieved by combining surface-specific labelling with high-resolution two-dimensional gel electrophoresis and mass spectrometry.