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Antigens (HLAs) play an important role in host immune responses to infectious
pathogens, and influence organ transplantation, cancer and autoimmune diseases.
In this study we conducted a high resolution, sequence-based genotyping of HLA
class I and class II genes of more than 2000 women from Kenya, eastern Tanzania
and southern Uganda around Lake Victoria and analyzed their allele, phenotype
and haplotype frequencies. A considerable genetic diversity was observed at
both class I and II loci. A total of 79 HLA-A, 113 HLA-B, 53 HLA-C, 25 HLA-DPA1,
60 HLA-DPB1, 15 HLA-DQA1, 44 HLA-DQB1 and 38 HLA-DRB1 alleles have been
identified. The most common class I alleles were A * 02:01:01 (10.90%), B *
58:02 (8.79%), and C * 06:02:01 (16.98%). The most common class II alleles were
DPA1*01:03:01 (40.60%), DPB1 * 01:01:01 (23.45%), DQA1 * 01:02:01 (31.03%),
DQB1 * 03:01:01 (21.79%), DRB1 * 11:01:02 (11.65%), DRB3 * 02:02:01 (31.65%),
DRB4 * 01:01:01 (10.50%), and DRB5 * 01:01:01 (10.50%). Higher than expected
homozygosity was observed at HLA-B (P = 0.022), DQA1 (P = 0.004), DQB1 (P = 0.023),
and DRB1 (P = 0.0006) loci. The allele frequency distribution of this
population is very similar to the ones observed in other sub-Saharan
populations with the exception of lower frequencies of A * 23 (5.55% versus
11.21%) and DQA1 * 03 (4.79% versus 11.72%), and higher frequencies of DPB1 *
30 (2.26% versus 0.37%) and DRB1 * 11 (21.51% versus 15.89%). The knowledge of
the diversity and allele/ phenotype frequencies of the HLA alleles of this east
African population, can contribute to the understanding of how host genetic
factors influence disease susceptibility and effective anti-retroviral
treatment of HIV infections and future vaccine trials.
Avoiding potentially catastrophic global climate change is a moral imperative, demanding significant reductions in greenhouse gas emissions from all important transport sectors, including aviation. However, because passenger flights and freight traffic are increasing much faster than efficiency improvements, the aviation sector will not be able to reduce emissions, or even stabilize them at current levels, without direct, forceful action to reduce demand. This paper reviews the ethical principles and empirical realities supporting the case for reducing worldwide aviation traffic. It argues that most passenger air travel and air freight shipping represents unnecessary luxury consumption, which responsible moral agents should willingly reduce in order to mitigate global climate change. It considers several mechanisms for doing so, and contends that they may succeed, but only if combined with an explicit recognition and binding commitment that for the foreseeable future, aviation must be a slow-growth or no-growth sector of the world economy.
In a typical composite interval mapping experiment,
the probability of obtaining false QTL is likely to be at least an order of
magnitude greater than the nominal experiment-wise Type I error rate, as set by
permutation test. F2 mapping crosses were simulated with three
different genetic maps. Each map contained ten QTL on either three, six or
twelve linkage groups. QTL effects were additive only, and heritability was
50%. Each linkage group had 11 evenly-spaced (10 cM) markers. Selective genotyping was used.
Simulated data were analyzed by composite interval mapping with the Zmapqtl program
of QTL Cartographer. False positives were minimized by using the largest
feasible number of markers to control genetic background effects. Bootstrapping
was then used to recover mapping power lost to the large number of conditioning
markers. Bootstrapping is shown to be a useful tool for QTL discovery, although
it can also produce false positives. Quantitative bootstrap support—the
proportion of bootstrap replicates in which a significant likelihood maximum occurred
in a given marker interval—was positively correlated with the probability that
the likelihood maxima revealed a true QTL. X-linked QTL were detected with much
lower power than autosomal QTL. It is suggested that QTL mapping experiments
should be supported by accompanying simulations that replicate the marker map,
crossing design, sample size, and method of analysis used for the actual experiment.
It will be shown how the retailer can use economic theory to exploit the sparse information available to him to set the price of each item he is selling close to its profit-maximizing level. The variability of the maximum price acceptable to each customer is modeled using a probability density for demand, which provides an alternative to the conventional demand curve often employed. This alternative way of interpreting retail demand data provides insights into the optimal price as a central measure of a demand distribution. Modeling individuals’ variability in their maximum acceptable price using a near-exhaustive set of “demand densities”, it will be established that the optimal price will be close both to the mean of the underlying demand density and to the mean of the Rectangular distribution fitted to the underlying distribution. An algorithm will then be derived that produces a near-optimal price, whatever the market conditions prevailing, monopoly, oligopoly, monopolistic competition or, in the limiting case, perfect competition, based on the minimum of market testing. The algorithm given for optimizing the retail price, even when demand data are sparse, is shown in worked examples to be accurate and thus of practical use to retail businesses.
The paper introduces generalized demand densities as a new and effective way of conceptualizing and analyzing retail demand. The demand density is demonstrated to contain the same information as the demand curve conventionally used in economic studies of consumer demand, but the fact that it is a probability density sets bounds on its possible behavior, a feature that may be exploited to allow near-exhaustive testing of possible demand scenarios using candidate demand densities. Four such demand densities are examined in detail. The Household Income demand density is based on the assumption that a person’s maximum acceptable price (MAP) for an item is proportional to his household after-tax income. The Double Power demand density allows the mode to be located anywhere in the range between zero and the highest MAP possessed by anyone in the target population. The two-parameter, Rectangular demand density, the simplest model that a retailer may employ, has the useful feature that it may be matched relatively easily to any unimodal demand density and hence may act as its approximate proxy. The Kinked demand density is derived from the kinked demand curve sometimes used as a relatively uncomplicated way of conceptualizing the effects of oligopoly. The central measures of each of these demand densities are derived: mean price, mode, median, optimal and, when appropriate, the mean of the matched Rectangular demand density. In a further result arising from the use of demand densities, it is shown that stable trading at the kink price will not occur if the demand curve is kinked and convex.
promotions are a common feature of retail food markets, but why are they so widespread?
The theory of Relative Utility Pricing (RUP) developed in this paper provides
an explanation not only for supermarket promotional offers but also for more
general pricing of packs of different sizes in supermarkets and on the internet.
A clear and simple explanation is given for the two most widely used quantity
promotions: BOGOF and 3-for-the-price-of-2. The RUP model may be linked to the
theory of iso-elastic utility functions, and this allows the relationships
amongst risk-aversion, pack-size ratio and demand elasticity to be explored. “Cautious
consumers”, as defined in the paper, are found to be the only sensible target
for quantity promotions. It is argued that the needs of cautious consumers of
retail commodities will be best addressed if the vendor sets the ratio of
successive pack sizes as the square of the Golden Ratio, namely 2.62, and the
price-ratio at the Golden Ratio, 1.62. Thus the Golden Ratio may be regarded as
a marketing guide for vendors considering both their best interests and those
of their customers. This proposition is supported by an analysis showing that
higher profits are more likely to come from Golden Ratio sizing than from either BOGOF or 3-for-2
when variable costs lie in most of the upper half of the range that is required
for any of these multibuy offers to generate profit. The paper’s theoretical
predictions for both pack sizes and prices are supported by examples from the
retail sector: grocery, paperback books and electronics.