A Model for the Censoring Process in Incomplete Repeated Measurement Experiments

Premature terminations or dropouts occur often in repeated measurement experiments. A number of methods have been proposed to analyze such data but most of them assume that the censoring mechanism is, within each group, unaffected by the mechanism generating the response variables. In this paper, we propose a model for the censoring mechanism that generates dropouts. We then show how this model can be used to check whether the censoring mechanism is affected by the response variables and other covariates. Finally, the methods of the paper are applied to the “Halothane” data set.     

Spectroscopic characterization of Cicer arietinum metallothionein 1

The plant metallothioneins differ distinctively from other metallothionein families with respect to the cysteine distribution patterns, the presence of aromatic amino acids in most and histidine in some forms, as well as long cysteine-free amino acid stretches between cysteine-rich regions. Although known for more than 25 years, research activity on plant metallothioneins has been low increasing only in the past few years. In the following, we will present the first characterization of Cicer arietinum (chickpea) MT1. In this root-specific protein two cysteine-rich regions with six cysteine residues each are separated by a 42 amino acids long linker region. A synthetic gene encoding MT1 was designed, cloned into a suitable vector, and the protein was over-expressed in Escherichia coli. We could show, that MT1 has the ability to coordinate up to five Zn²⁺ or Cd²⁺ ions and even higher amounts of Hg²⁺. According to titration experiments pH-dependent zinc- and cadmium-thiolate cluster stability in MT1 is considerably lower than in vertebrate metallothioneins. The approximate contribution of secondary structural elements to the overall structure was assessed with circular dichroism and infrared spectroscopy. Hypothetical metal-thiolate cluster structures will be presented.     

Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications

The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or model selection is performed on a bootstrap sample as if it were the original sample. P‐values computed from bootstrap samples have been used, for example, in the statistics and bioinformatics literature for ranking genes with respect to their differential expression, for estimating the variability of p‐values and for model stability investigations. Procedures which make use of bootstrapped information criteria are often applied in model stability investigations and model averaging approaches as well as when estimating the error of model selection procedures which involve tuning parameters. From the literature, however, there is evidence that p‐values and model selection criteria evaluated on bootstrap data sets do not represent what would be obtained on the original data or new data drawn from the overall population. We explain the reasons for this and, through the use of a real data set and simulations, we assess the practical impact on procedures relevant to biometrical applications in cases where it has not yet been studied. Moreover, we investigate the behavior of subsampling (i.e., drawing from a data set without replacement) as a potential alternative solution to the bootstrap for these procedures.     

No foundation of a "3/4 power scaling law" for respiration in biology

Respiration rate ( R ) as a function of body mass ( W   ) is usually expressed as R  =  aW ^b . Empirically, the b value is often close to 3/4 when organisms covering a large span in body mass are compared. But recent years research on the energetic cost of growth demonstrate that young and fast growing stages show higher weight specific respiration rates than older and adult stages, and this implies that the b values tend to be higher: b ∼ 1 in small (young) organisms falling to b  = 0.6–0.7 in larger (older) stages. Thus, respiration and growth are integrated through the energetic costs of growth. This explains why the b value is not a "natural constant" and why a "3/4 power scaling law" cannot be deduced from the interplay between pure physical and geometric constraints of the transport of oxygen.     

Growth and physiology of Candida utilis NCYC 321 in potassium-limited chemostat culture

When grown in a defined simple salts medium, plus vitamins, Candida utilis displayed an absolute requirement for potassium. But the potassium content of this yeast was exceedingly variable and, with aerobic chemostat cultures (grown at a dilution rate of 0.1 h^-1; 30° C; pH 5.5), was low (< 0.2%, w/w) when they were potassium-limited and high (> 2%, w/2) when glucose-limited. With potassium-limited cultures, the cell-bound potassium content also varied markedly with growth rate, though hardly at all with glucose-limited cultures; magnesium- and phosphate-limited cultures gave intermediate responses.Changes in cell-bound potassium content correlated only weakly with changes in the cellular contents of magnesium, phosphate and RNA, but strongly with changes in both the Y _glucose and Y _O values, indicating an involvement of potassium in the generation of energy by oxidative phosphorylation reactions and/or the utilization of this energy for growth processes.Studies with isolated mitochondria revealed that potassium-limited organisms had a changed content of cytochrome b relative to cytochrome a, and lacked coupling at either site 2 or site 3 of the respiratory chain.These results are discussed in relation to the reported functions of potassium in the growth of micro-organisms, and the organizational differences between prokaryotic and eukaryotic cells.     

Linear programming as a tool in life cycle assessment

Linear Programming (LP) is a powerful mathematical technique that can be used as a tool in Life Cycle Assessment (LCA). In the Inventory and Impact Assessment phases, in addition to calculating the environmental impacts and burdens, it can be used for solving the problem of allocation in multiple-output systems. In the Improvement Assessment phase, it provides a systematic approach to identifying possibilities for system improvements by optimising the system on different environmental objective functions, defined as burdens or impacts. Ultimately, if the environmental impacts are aggregated to a single environmental impact function in the Valuation phase, LP optimisation can identify the overall environmental optimum of the system. However, the aggregation of impacts is not necessary: the system can be optimised on different environmental burdens or impacts simultaneously by using Multiobjective LP. As a result, a range of environmental optima is found offering a number of alternative options for system improvements and enabling the choice of the Best Practicable Environmental Option (BPEO). If, in addition, economic and social criteria are introduced in the model, LP can be used to identify the best compromise solution in a system with conflicting objectives. This approach is illustrated by a real case study of the borate products system. An erratum to this article is available at .     

High risk water pollution hazards affecting Aveiro coastal lagoon (Portugal) – A habitat risk assessment using InVEST

Anthropogenic pressures put at jeopardy ecosystem services (ES) provided by natural habitats. Ecosystem Based Management (EBM) approaches can support policymakers dealing with physical, chemical, and biological stresses caused by high-risk water pollution (HRWP) and sudden-accidental pollution (SAP). The objective of this study is to evaluate how alarming HRWP pressures might become in fragile marine, coastal, estuarine, and freshwater socio-ecological systems (MCEF-SES) surrounded by heavily industrialized and urbanized areas. To this end a spatially explicit analysis, using the InVEST-Habitat Risk Assessment (InVEST-HRA) model in combination with expert judgement from researchers from various fields, is performed. An application is provided for the case of the Ria de Aveiro (RdA) coastal lagoon in Portugal. Results show high spatial variance of HRWP hazards across RdA, with one major multi-layer risk hotspot at the center of the research area and a second patch of multiple risk hotspots towards the North of RdA. Salines emerge as the most threatened habitat followed by Intertidal flats and Saltmarshes. The most significant water pollution risk sources contributing to Salines cumulative risk are Fossil fuel processing, storage and sale units, Industrial units, Aquaculture, and the Marinas. Industries involving dangerous substances in the region threaten primarily Watercourses. This study confirms the InVEST-HRA model in combination with expert judgement is a transparent and easily replicable approach to build ES-based knowledge about habitat risks threatening MCEF-SES in a Natura 2000 site heavily pressured by HRWP hazards. After further valuation analysis, pondering gains and losses from regional development and environmental protection, this knowledge can support the planning and management of coastal areas and the prioritization of pollution abatement interventions. In particular, by estimating the loss that HRWP causes in the value of ecosystem services, defining HRWP abatement policies, assessing the effectiveness, costs and benefits of those abatement policies and, ultimately, evaluating the results for the well-being of local communities through global efficiency analysis, cost-benefit analysis or cost-effectiveness analysis. Hence, it bridges the gap between an informed EBM and the development policies of fragile regions.