Ciba Foundation symposium最新文献

Throughout the 20th century many different patterns of asthma mortality have been observed. Following relatively stable asthma mortality rates during the first half of this century, there has been a gradual increase in asthma mortality in many countries over the last 50 years. Although a number of possible explanations have been proposed to explain this trend-including increases in asthma prevalence, increases in exposure to factors that trigger asthma attacks and changes in asthma management-their relative contribution in different countries is uncertain. Another pattern is that of sudden marked increases in asthma mortality occurring in at least seven countries in the 1960s and in New Zealand in the 1970s. Available evidence indicates that the cause of these 'epidemics' was the use of high dose preparations of two specific beta-agonist drugs, namely isoprenaline forte and fenoterol. The most recent trend observed in a number of western countries during the last decade has been a gradual reduction in asthma mortality; this may relate to improvements in the management of asthma.

This chapter reviews previous work on an evolutionary model describing the effect of time delays on human preferences. The model explains why the long-term real interest rate is usually near 3% and why rates of crime and driving accidents are highest among young adults. It does not succeed in explaining the phenomenon of preference reversal. The chapter reports new results on uncertainty and on a more comprehensive model allowing consumption to have simultaneous effects on mortality and fertility.

Precision agriculture aims at adjusting and fine-tuning land and crop management to the needs of plants within heterogeneous fields. Production aspects have to be balanced against environmental threshold values and modern information technology has made it possible to devise operational field systems. A reactive approach is described, using yield maps and sensors. A proactive approach uses simulation modelling of plant growth and solute fluxes to predict optimal timing of management practices. Precision agriculture, combining both approaches, is seen as making a major contribution towards the development of sustainable agricultural production systems.

In this paper, I address three topics relevant to the ability of precision agriculture to improve the environmental performance of agricultural production systems. First, I describe the fundamental ecological factors that influence the environmental performance of these systems and address how precision agriculture practices can or cannot interact with these factors. Second, I review the magnitude of the ecological processes that we hope to manage with precision agriculture relative to agricultural inputs to determine whether managing these processes can significantly affect system environmental performance. Finally, I address scale incongruencies between ecological processes and precision agriculture techniques that could limit the ability of these techniques to manage variability in these processes. The analysis suggests that there are significant ecological constraints on the ability of precision agriculture techniques to improve the environmental performance of agricultural production systems. The primary constraint is that these techniques do not address many of the key factors that cause poor environmental performance in these systems. Further, the magnitude of the ecological processes that we hope to manage with precision agriculture are quite small relative to agricultural inputs and, finally, these processes vary on scales that are incongruent with precision management techniques.

The mature form of the zymogen, human plasminogen (HPlg), contains 791 amino acids present in a single polypeptide chain. The fibrinolytic enzyme, human plasmin (HPlm), is formed from HPlg as a result of activator-catalysed cleavage of the Arg561-Val562 peptide bond in HPlg. The resulting HPlm contains a heavy chain of 561 amino acid residues, originating from the N-terminus of HPlg, doubly disulfide-linked to a light chain of 230 amino acid residues. This latter region, containing the C-terminus of HPlg, is homologous to serine proteases such as trypsin and elastase. The heavy chain of HPlm consists of five repeating triple-disulfide-linked peptide regions, c. 80 amino acid residues in length, termed kringles (K), that are responsible for interactions of HPlg and HPlm with substrates, inhibitors and regulators of HPlg activation. Important among the ligands of the kringles are positive activation effectors, typified by lysine and its analogues, and negative activation effectors, such as Cl-. The kringle domains of HPlg that participate in these binding interactions are K1, K4 and K5, and perhaps K2. These modules appear to function as independent domains. The amino acid residues important in these kringle/ligand binding interactions have been proposed by structural determinations, and their relative importance quantified by site-directed mutagenesis experimentation.

This paper summarizes statistical procedures which are useful for precision farming at different scales. Three topics are addressed: spatial comparison of scenarios for land use, analysis of data in the space-time domain, and sampling in space and time. The first study compares six scenarios for nitrate leaching to ground water. Disjunctive cokriging reduces the computing time by 80% without loss of accuracy. The second study analyses wind erosion during four storms in a field in Niger measured with 21 devices. We investigated the use of temporal replicates to overcome the lack of spatial data. The third study analyses the effects of sampling in space and time for soil nutrient data in a Southwest African field. We concluded that statistical procedures are indispensable for decision support to smart farming.

This paper demonstrates how geostatistics and fuzzy k-means classification can be used together to improve our practical understanding of crop yield-site response. Two aspects of soil are important for precision farming: (a) sensible classes for a given crop, and (b) their spatial variation. Local site classifications are more sensitive than general taxonomies and can be provided by the method of fuzzy k-means to transform a multivariate data set with i attributes measured at n sites into k overlapping classes; each site has a membership value mk for each class in the range 0-1. Soil variation is of interest when conditions vary over patches manageable by agricultural machinery. The spatial variation of each of the k classes can be analysed by computing the variograms of mk over the n sites. Memberships for each of the k classes can be mapped by ordinary kriging. Areas of class dominance and the transition zones between them can be identified by an inter-class confusion index; reducing the zones to boundaries gives crisp maps of dominant soil groups that can be used to guide precision farming equipment. Automation of the procedure is straightforward given sufficient data. Time variations in soil properties can be automatically incorporated in the computation of membership values. The procedures are illustrated with multi-year crop yield data collected from a 5 ha demonstration field at the Royal Agricultural College in Cirencester, UK.

Interleukin 4 (IL-4) is a pleiotropic type I cytokine that controls both growth and differentiation among haemopoietic and non-haemopoietic cells. Its receptor is a heterodimer. One chain, the IL-4R alpha chain, binds IL-4 with high affinity and determines the nature of the biochemical signals that are induced. The second chain, gamma c, is required for the induction of such signals. IL-4-mediated growth depends upon activation events that involve phosphorylation of Y497 of IL-4R alpha, leading to the binding and phosphorylation of 4PS/IRS-2 in haemopoietic cells and of IRS-1 in non-haemopoietic cells. By contrast, IL-4-mediated differentiation events depend upon more distal regions of the IL-4R alpha chain that include a series of STAT-6 binding sites. The distinctive roles of these receptor domains was verified by receptor-reconstruction experiments. The 'growth' and 'differentiation' domains of the IL-4R alpha chain, independently expressed as chimeric structures with a truncated version of the IL-2R beta chain, were shown to convey their functions to the hybrid receptor. The critical role of STAT-6 in IL-4-mediated gene activation and differentiation was made clear by the finding that lymphocytes from STAT-6 knockout mice are strikingly deficient in these functions but have retained the capacity to grow, at least partially, in response to IL-4. IL-4 plays a central role in determining the phenotype of naive CD4+ T cells. In the presence of IL-4, newly primed naive T cells develop into IL-4 producers while in its absence they preferentially become gamma-interferon (IFN-gamma) producers. Recently, a specialized subpopulation of T cells, CD4+/NK1.1+ cells, has been shown to produce large amounts of IL-4 upon stimulation. Two examples of mice with deficiencies in these cells are described--beta 2-microglobulin knockout mice and SJL mice. Both show defects in the development of IL-4-producing cells and in the increase in serum IgE in response to stimulation with the polyclonal stimulant anti-IgD. Both sets of mice have major diminutions in the number of CD4+/ NK1.1+ T cells, strongly indicating an important role of these cells in some but not all IgE responses to physiologic stimuli.

Amelogenesis imperfecta is a disfiguring inherited condition affecting tooth enamel. X-Linked and autosomal dominant and recessive inheritance patterns occur. X-Linked amelogenesis imperfecta has been studied extensively at the molecular level. Linkage analysis has shown that there is genetic hetetogeneity in X-linked amelogenesis imperfecta with two identified loci: AIH1 and AIH3. The AIH1 locus corresponds to the location of the amelogenin gene on the distal short arm of the X chromosome; various mutations in the amelogenin gene have been found in families with X-linked amelogenesis imperfecta. The AIH3 locus maps to the Xq24-q27.1 region on the long arm of the X chromosome. Linkage to the long arm of chromosome 4 has been established in three families with autosomal dominant amelogenesis imperfecta. There is as yet no published evidence for genetic heterogeneity in autosomal dominant amelogenesis imperfecta as in X-linked amelogenesis imperfecta. Candidate genes for autosomal dominant amelogenesis imperfecta include tuftelin (1q), albumin (4q) and ameloblastin (4q) but the involvement of these genes in the disease has yet to be demonstrated. In view of the variable clinical appearances within families with autosomal dominant amelogenesis imperfecta and X-linked amelogenesis imperfecta, together with the finding that different X-linked amelogenesis imperfecta phenotypes result from mutations within the same gene, an alternative classification based on the molecular defect and mode of inheritance rather than phenotype has been proposed.