Effect of ascorbic acid on serum cholesterol levels and on die-away curves of 14C-4-cholesterol in baboons

Fourteen young male baboons (Papio ursinus) were divided into two groups. All the animals received the same dietary regimen during a 2%-month adaptation period. During the next 3 months one group received 250 mg and the other 20 mg vitamin C daily. For the last 2% months of the experiment no vitamin C was given to the first group, and that of the second group was increased to 350 mg daily. Simultaneously with the switchover, 14C-4-cholesterol was administered. A classical twopool system for the kinetic behaviour of cholesterol in the body was confirmed. Vitamin C treatment did not alter the serum cholesterol levels significantly, but the production rate was repressed. It was also shown that vitamin C was depleted from the body in a typical two-pool fashion.S. Afr. Med. J., 48, 1182 (1974)

Seasonal variation in serum ascorbic acid and serum lipid composition of free-living baboons (Papio ursinus)

Two surveys were conducted in the Kruger National Park in which 205 baboons were captured. The first survey was done during late summer and the second during late winter. Serum ascorbic acid, serum cholesterol and serum phospholipids were determined. Baboons of both sexes and various ages were captured. This work was undertaken to establish serum ascorbic acid, serum cholesterol and serum phospholipid values for baboons under free-living conditions. A seasonal variation was found, and the serum ascorbic acid serum cholesterol and serum phospholipid values were significantly higher during winter than during summer.S. Afr. Med. J., 48, 1700 (1974

Extent and Causes of Chesapeake Bay Warming

Coastal environments such as the Chesapeake Bay have long been impacted by eutrophication stressors resulting from human activities, and these impacts are now being compounded by global warming trends. However, there are few studies documenting long-term estuarine temperature change and the relative contributions of rivers, the atmosphere, and the ocean. In this study, Chesapeake Bay warming, since 1985, is quantified using a combination of cruise observations and model outputs, and the relative contributions to that warming are estimated via numerical sensitivity experiments with a watershed–estuarine modeling system. Throughout the Bay’s main stem, similar warming rates are found at the surface and bottom between the late 1980s and late 2010s (0.02 +/- 0.02C/year, mean +/- 1 standard error), with elevated summer rates (0.04 +/- 0.01C/year) and lower rates of winter warming (0.01 +/- 0.01C/year). Most (~85%) of this estuarine warming is driven by atmospheric effects. The secondary influence of ocean warming increases with proximity to the Bay mouth, where it accounts for more than half of summer warming in bottom waters. Sea level rise has slightly reduced summer warming, and the influence of riverine warming has been limited to the heads of tidal tributaries. Future rates of warming in Chesapeake Bay will depend not only on global atmospheric trends, but also on regional circulation patterns in mid-Atlantic waters, which are currently warming faster than the atmosphere. Supporting model data available at: https://doi.org/10.25773/c774-a36

Towards an integrated set of surface meteorological observations for climate science and applications

Observations are the foundation for understanding the climate system. Yet, currently available land meteorological data are highly fractured into various global, regional and national holdings for different variables and timescales, from a variety of sources, and in a mixture of formats. Added to this, many data are still inaccessible for analysis and usage. To meet modern scientific and societal demands as well as emerging needs such as the provision of climate services, it is essential that we improve the management and curation of available land-based meteorological holdings. We need a comprehensive global set of data holdings, of known provenance, that is truly integrated both across Essential Climate Variables (ECVs) and across timescales to meet the broad range of stakeholder needs. These holdings must be easily discoverable, made available in accessible formats, and backed up by multi-tiered user support. The present paper provides a high level overview, based upon broad community input, of the steps that are required to bring about this integration. The significant challenge is to find a sustained means to realize this vision. This requires a long-term international program. The database that results will transform our collective ability to provide societally relevant research, analysis and predictions in many weather and climate related application areas across much of the globe

Neutrino oscillation studies with IceCube-DeepCore

AbstractIceCube, a gigaton-scale neutrino detector located at the South Pole, was primarily designed to search for astrophysical neutrinos with energies of PeV and higher. This goal has been achieved with the detection of the highest energy neutrinos to date. At the other end of the energy spectrum, the DeepCore extension lowers the energy threshold of the detector to approximately 10 GeV and opens the door for oscillation studies using atmospheric neutrinos. An analysis of the disappearance of these neutrinos has been completed, with the results produced being complementary with dedicated oscillation experiments. Following a review of the detector principle and performance, the method used to make these calculations, as well as the results, is detailed. Finally, the future prospects of IceCube-DeepCore and the next generation of neutrino experiments at the South Pole (IceCube-Gen2, specifically the PINGU sub-detector) are briefly discussed

Benchmarking the performance of pairwise homogenization of surface temperatures in the United States

Changes in the circumstances behind in situ temperature measurements often lead to biases in individual station records that, collectively, can also bias regional temperature trends. Since these biases are comparable in magnitude to climate change signals, homogeneity “corrections” are necessary to make the records suitable for climate analysis. To quantify the effectiveness of U.S. surface temperature homogenization, a randomized perturbed ensemble of the USHCN pairwise homogenization algorithm was run against a suite of benchmark analogs to real monthly temperature data. Results indicate that all randomized versions of the algorithm consistently produce homogenized data closer to the true climate signal in the presence of widespread systematic errors. When applied to the real-world observations, the randomized ensemble reinforces previous understanding that the two dominant sources of bias in the U.S. temperature records are caused by changes to time of observation (spurious cooling in minimum and maximum) and conversion to electronic resistance thermometers (spurious cooling in maximum and warming in minimum). Error bounds defined by the ensemble output indicate that maximum temperature trends are positive for the past 30, 50 and 100 years, and that these maximums contain pervasive negative biases that cause the unhomogenized (raw) trends to fall below the lower limits of uncertainty. Moreover, because residual bias in the homogenized analogs is one-tailed under biased errors, it is likely that maximum temperature trends have been underestimated in the USHCN. Trends for minimum temperature are also positive over the three periods, but the ensemble error bounds encompass trends from the unhomogenized data

An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses

Temperature trends over 1979–2008 in the U.S. Historical Climatology Network (HCN) are compared with those in six recent atmospheric reanalyses. For the conterminous United States, the trend in the adjusted HCN (0.327 °C dec−1) is generally comparable to the ensemble mean of the reanalyses (0.342 °C dec−1). It is also well within the range of the reanalysis trend estimates (0.280 to 0.437 °C dec−1). The bias adjustments play a critical role, as the raw HCN dataset displays substantially less warming than all of the reanalyses. HCN has slightly lower maximum and minimum temperature trends than those reanalyses with hourly temporal resolution, suggesting the HCN adjustments may not fully compensate for recent non-climatic artifacts at some stations. Spatially, both the adjusted HCN and all of the reanalyses indicate widespread warming across the nation during the study period. Overall, the adjusted HCN is in broad agreement with the suite of reanalyses