Objective test for food sensitivity in asthmatic children: increased bronchial reactivity after cola drinks.

Ten asthmatic children with a history of cough and wheeze after drinking a cola drink performed histamine inhalation tests before and 30 minutes after a drink of Pepsi-Cola, soda water, and water on three separate study days. There was no significant change in baseline peak expiratory flow after any of the three drinks. Sensitivity to histamine was increased after the cola drink (p less than 0.005) but was not significantly different after soda water or water. The detection of change in sensitivity to histamine appears to be a simple and effective method of testing for food sensitivity in asthma.     

Molecular heterogeneity of D-end products detected by anti-H-2.28 sera

Immunoprecipitation of NP-40 lysates of 125I-labeled lymph-node cells with different anti-H-2 sera and with anti-Qa-2 serum has shown that the BALB/cByA strain (H-2d, Qa-2-negative) expresses, besides H-2Ld, another molecule that is not detectable in the BALB/c-H-2dm2 strain. Electrophoresis in SDS polyacrylamide gels indicated that this molecule, provisionally designated Lq, has an apparent molecular weight of 41000 daltons, in contrast to approximately 49000 daltons for H-2Kd and H-2Ld, and 47000 daltons for H-2Dd molecules. The anti-Qa-2 serum precipitated from the Qa-2-positive strains BALB/cHeA but not from the Qa-2-negative strains BALB/cByA and BALB/c-H-2dm2 a protein that gave a very strong band corresponding to the molecular weight 41000 daltons in the gel electrophoresis. The biochemical characteristics of the Lq molecule are thus more similar to those of Qa-2 than of H-2 antigens.     

Interactions Among Fuel Management,Species Composition,Bark Beetles,and Climate Change and the Potential Effects on Forests of the Lake Tahoe Basin

Climate-driven increases in wildfires, drought conditions, and insect outbreaks are critical threats to forest carbon stores. In particular, bark beetles are important disturbance agents although their long-term interactions with future climate change are poorly understood. Droughts and the associated moisture deficit contribute to the onset of bark beetle outbreaks although outbreak extent and severity is dependent upon the density of host trees, wildfire, and forest management. Our objective was to estimate the effects of climate change and bark beetle outbreaks on ecosystem carbon dynamics over the next century in a western US forest. Specifically, we hypothesized that (a) bark beetle outbreaks under climate change would reduce net ecosystem carbon balance (NECB) and increase uncertainty and (b) these effects could be ameliorated by fuels management. We also examined the specific tree species dynamics—competition and release—that determined NECB response to bark beetle outbreaks. Our study area was the Lake Tahoe Basin (LTB), CA and NV, USA, an area of diverse forest types encompassing steep elevation and climatic gradients and representative of mixed-conifer forests throughout the western United States. We simulated climate change, bark beetles, wildfire, and fuels management using a landscape-scale stochastic model of disturbance and succession. We simulated the period 2010–2100 using downscaled climate projections. Recurring droughts generated conditions conducive to large-scale outbreaks; the resulting large and sustained outbreaks significantly increased the probability of LTB forests becoming C sources over decadal time scales, with slower-than-anticipated landscape-scale recovery. Tree species composition was substantially altered with a reduction in functional redundancy and productivity. Results indicate heightened uncertainty due to the synergistic influences of climate change and interacting disturbances. Our results further indicate that current fuel management practices will not be effective at reducing landscape-scale outbreak mortality. Our results provide critical insights into the interaction of drivers (bark beetles, wildfire, fuel management) that increase the risk of C loss and shifting community composition if bark beetle outbreaks become more frequent.     

Does maternal condition or predation risk influence small mammal population dynamics?

There is strong debate over whether the intrinsic traits of individuals or the extrinsic environment exert the greater influence on small mammal population dynamics. We test the roles of maternal effects (an intrinsic factor) and predation risk (an extrinsic factor) in the population dynamics of wild strain house mice using a 2-factor enclosure experiment. Pre-release supplemental feeding with a high-fat diet created female treatment founders that were 6–10% heavier than controls, a condition that we predicted would be passed on as a maternal effect. Predation risk was enhanced using regular application of predator (red fox Vulpes vulpes ) scats. Founder populations of six females and six males released into eight, 15×15 m enclosures showed near exponential population growth over 17 weeks (maximum 3 generations). But there were no responses to either treatment in terms of survival, inherited body weights, fecundity or population size. We suggest that elevated maternal condition may have only minor and transient intergenerational effects with little long-term consequence. We also suggest that the general significance of predator scats as a cue to predation risk to alter prey behaviour may have been overestimated. Hence our results question the role of either factor in causing long-term responses that influence condition to affect population processes.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.