Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

Does Mother Nature really prefer rare species or are log‐left‐skewed SADs a sampling artefact?

Intensively sampled species abundance distributions (SADs) show left‐skew on a log scale. That is, there are too many rare species to fit a lognormal distribution. I propose that this log‐left‐skew might be a sampling artefact. Monte Carlo simulations show that taking progressively larger samples from a log‐unskewed distribution (such as the lognormal) causes log‐skew to decrease asymptotically (move towards ?∞) until it reaches the level of the underlying distribution (zero in this case). In contrast, accumulating certain types of repeated small samples results in a log‐skew that becomes progressively more log‐left‐skewed to a level well beyond the underlying distribution. These repeated samples correspond to samples from the same site over many years or from many sites in 1 year. Data from empirical datasets show that log‐skew generally goes from positive (right‐skewed) to negative (left‐skewed) as the number of temporally or spatially replicated samples increases. This suggests caution when interpreting log‐left‐skew as a pattern that needs biological interpretation.     

Sampling Atriplex bladders: a comparison of methods

Abstract. A major objection to the convenient sampling of Chenopodiacean bladders by brushing in aqueous solutions is the suspected leakage of ions from the leaf blade. To overcome this problem, a new method of sampling bladder hair is described, which also achieves rapid and quantitative separation and, at the same time, yields bladder samples of undoubtedly high purity. Leakage is stopped by the aid of liquid nitrogen. Comparison of this method to removal of bladders by brushing in aquenous solutions effectively confirm the validity of the customary method beyond dispute.     

Farms,pastures and woodlands: the fine‐scale distribution of Palearctic Culicoides spp. biting midges along an agro‐ecological gradient

The spatial epidemiology of Bluetongue virus (BTV) at the landscape level relates to the fine‐scale distribution and dispersal capacities of its vectors, midges belonging to the genus Culicoides Latreille (Diptera: Ceratopogonidae). Although many previous researches have carried out Culicoides sampling on farms, little is known of the fine‐scale distribution of Culicoides in the landscape immediately surrounding farms. The aim of this study was to gain a better understanding of Culicoides populations at increasing distances from typical dairy farms in north‐west Europe, through the use of eight Onderstepoort‐type black‐light traps positioned along linear transects departing from farms, going through pastures and entering woodlands. A total of 16 902 Culicoides were collected in autumn 2008 and spring 2009. The majority were females, of which more than 97% were recognized as potential vectors. In pastures, we found decreasing numbers of female Culicoides as a function of the distance to the farm. This pattern was modelled by leptokurtic models, with parameters depending on season and species. By contrast, the low number of male Culicoides caught were homogeneously distributed along the transects. When transects entered woodlands, we found a higher abundance of Culicoides than expected considering the distance of the sampling sites to the farm, although this varied according to species.     

The Hemiptera (Insecta) communities of tropical rain forest in Sulawesi

The structure and composition of Hemiptera communities of tropical rain forest in Dumoga-Bone National Park, Sulawesi, Indonesia were investigated over a 1-year period. The aim was to investigate the extent to which insect samples, obtained using six different techniques, were representative of the whole community, and to explore temporal and spatial variation in Hemiptera community composition. Sampling techniques employed were Rothamsted light trapping, canopy fogging, malaise trapping at both ground and canopy level, flight interception trapping and yellow water pan trapping. Overlap between faunas collected using different techniques was surprisingly low emphasizing the limitations of using any single method to sample and thereby describe the Hemiptera community. Differences were observed at the species, family and suborder levels. Similarly, there were major differences in faunal composition between sites and diversity appeared to peak at elevations between 600–1000 m. Seasonal changes were also significant but generally lower than between methods or sites. Results are discussed in relation to factors such as food availability and host specialization and the efficacy of each sampling method is reviewed in relation to the biology of the different Hemiptera groups and the composition of the samples obtained.