The far-ultraviolet signature of the 'missing' baryons in the Local Group of galaxies

The number of baryons detected in the low-redshift (z < 1) Universe is far smaller than the number detected in corresponding volumes at higher redshifts. Simulations of the formation of structure in the Universe show that up to two-thirds of the 'missing' baryons may have escaped detection because of their high temperature and low density. One of the few ways to detect this matter directly is to look for its signature in the form of ultraviolet absorption lines in the spectra of background sources such as quasars. Here we show that the amplitude of the average velocity vector of 'high velocity' O vi (O5+) absorption clouds detected in a survey of ultraviolet emission from active galactic nuclei decreases significantly when the vector is transformed to the frames of the Galactic Standard of Rest and the Local Group of galaxies. At least 82 per cent of these absorbers are not associated with any 'high velocity' atomic hydrogen complex in our Galaxy, and are therefore likely to result from a primordial warm-hot intergalactic medium pervading an extended corona around the Milky Way or the Local Group. The total mass of baryons in this medium is estimated to be up to approximately 10(12) solar masses, which is of the order of the mass required to dynamically stabilize the Local Group.     

Many-body and correlation effects in semiconductors

Solids consist of 1022-1023 particles per cubic centimetre, interacting through infinite-range Coulomb interactions. The linear response of a solid to a weak external perturbation is well described by the concept of non-interacting 'quasiparticles' first introduced by Landau. But interactions between quasiparticles can be substantial in dense systems. For example, studies over the past decade have shown that Coulomb correlations between quasiparticles dominate the nonlinear optical response of semiconductors, in marked contrast to the behaviour of atomic systems. These Coulomb correlations and other many-body interactions are important not only for semiconductors, but also for all condensed-matter systems.     

Comparison of DMSO and glycerol as cryoprotectants for ascites tumor cells

Summary Mouse Ehrlich ascites and rat D23 ascites tumors were stored in liquid nitrogen under identical conditions for up to 3 years. Cell viability (trypan blue exclusion) and transplantability of both tumors in animals remained virtually unaffected if preserved in 10% DMSO containing medium, whereas, cells preserved in 10% glycerol failed to produce lethal tumors in rodents.This work was performed at the Cancer Research Unit, Royal Victoria Infirmary, Newcastle Upon Tyne, England. I thank John Geggie for expert technical assistance; to the North of England Council of the Cancer Research Campaign and the National Science Foundation (ENG-78-25432) for financial support; and to Dr John A. Dickson for advice and encouragement. Also, I am indebted to Professor Rakesh K. Jain for his support.     

水流量标准装置微机化的研究

本文研究将微型计算机引入容器稳压法的水流量标准装置。从性能、价格等因素出发,采用两台低价的单板机构成双机主-从并行系统,满足了水流量标准装置实时控制和数据处理并行工作的要求,其结果降低了水流量标准装置对流量稳定性的苛刻要求。     

Antigenic differences between a primary hamster lymphosarcoma and its liver metastases

An antiserum was raised in rabbits against a primary metastasizing lymphosarcoma (ML) of the hamster. This was made tumor-specific by absorption with normal hamster tissue extracts. Immunoglobulin-G was prepared and tested for its cytotoxicity towards cells derived from the primary tumor and its liver metastases. The ML-specific IgG was found to be 2--5 times more cytotoxic for cells derived from the primary tumor compared to cells obtained from liver metastases.     

Mortality forecasting using neural networks and an application to cause‐specific data for insurance purposes

Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee–Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee–Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee–Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.     

Dynamics of chronic myeloid leukaemia

The clinical success of the ABL tyrosine kinase inhibitor imatinib in chronic myeloid leukaemia (CML) serves as a model for molecularly targeted therapy of cancer, but at least two critical questions remain. Can imatinib eradicate leukaemic stem cells? What are the dynamics of relapse due to imatinib resistance, which is caused by mutations in the ABL kinase domain? The precise understanding of how imatinib exerts its therapeutic effect in CML and the ability to measure disease burden by quantitative polymerase chain reaction provide an opportunity to develop a mathematical approach. We find that a four-compartment model, based on the known biology of haematopoietic differentiation, can explain the kinetics of the molecular response to imatinib in a 169-patient data set. Successful therapy leads to a biphasic exponential decline of leukaemic cells. The first slope of 0.05 per day represents the turnover rate of differentiated leukaemic cells, while the second slope of 0.008 per day represents the turnover rate of leukaemic progenitors. The model suggests that imatinib is a potent inhibitor of the production of differentiated leukaemic cells, but does not deplete leukaemic stem cells. We calculate the probability of developing imatinib resistance mutations and estimate the time until detection of resistance. Our model provides the first quantitative insights into the in vivo kinetics of a human cancer.