The importance of patient-specific radiation dose calculations for the administration of radionuclides in therapy.

This paper advocates patient-specific approaches to radiation dose calculations for radionuclides used in therapy and outlines strategies for implementing such approaches. The use of a simple approaches to radionuclide therapy, e.g. a single amount of activity for all patients or the same amount of activity administered per unit body weight do not permit the optimization of individual patient therapy. While limitations in current models and logistic problems prevent dose calculations of the quality currently enjoyed with external radiation therapy approaches, improvements can be made, and models are constantly evolving. Specific suggestions regarding the extension of current models, and of the use of new models which use image data from individual patients, are discussed in the context of allowing radiotherapy with internal emitters to employ the kind of patient-specific approaches that are used in other therapeutic modalities, which are clearly in the patients' best interests.     

Evaluation of Ecotoxicity Effect Indicators for Use in LCIA (10+4 pp)

Goal, Scope and Background The paper describes different ecotoxicity effect indicator methods/approaches. The approaches cover three main groups, viz. PNEC approaches, PAF approaches and damage approaches. Ecotoxicity effect indicators used in life cycle impact assessment (LCIA) are typically modelled to the level of impact, indicating the potential impact on 'ecosystem health'. The few existing indicators, which are modelled all the way to damage, are poorly developed, and even though relevant alternatives from risk assessment exist (e.g. recovery time and mean extinction time), these are unfortunately at a very early stage of development, and only few attempts have been made to include them in LCIA. Methods The approaches are described and evaluated against a set of assessment criteria comprising compatibility with the methodological requirements of LCIA, environmental relevance, reproducibility, data demand, data availability, quantification of uncertainty, transparency and spatial differentiation. Results and Discussion The results of the evaluation of the two impact approaches (i.e. PNEC and PAF) show both pros and cons for each of them. The assessment factor-based PNEC approaches have a low data demand and use only the lowest data (e.g. lowest NOEC value). Because it is developed in tiered risk assessment, and hence makes use of conservative assessment factors, it is not optimal, in its present form, to use in the comparative framework of LCIA, where best estimates are sought. The PAF approaches have a higher data demand but use all data and can be based on effect data (PNEC is no-effect-based), thus making these approaches non-conservative and more suitable for LCIA. However, indiscriminate use of ecotoxicity data tends to make the PAF-approaches no more environmentally relevant than the assessment factor-based PNEC approaches. The PAF approaches, however, can at least in theory be linked to damage modelling. All the approaches for damage modelling which are included here have a high environmental relevance but very low data availability, apart from the 'media recovery-approach', which depends directly on the fate model. They are all at a very early stage of development. Conclusion Recommendations and Outlook. An analysis of the different PAF approaches shows that the crucial point is according to which principles and based on which data the hazardous concentration to 50% of the included species (i.e. HC50) is estimated. The ability to calculate many characterisation factors for ecotoxicity is important for this impact category to be included in LCIA in a proper way. However, the access to effect data for the relevant chemicals is typically limited. So, besides the coupling to damage modelling, the main challenge within the further development and improvement of ecotoxicity effect indicators is to find an optimal method to estimate HC50 based on little data.     

Comparison of Approaches for Developing Distributions for Carcinogenic Slope Factors

In recent years, risk assessors have increasingly been moving away from deterministic risk assessment approaches and are applying probabilistic approaches that incorporate distributions of possible values for each input parameter. This paper reviews several approaches that are being used or that could potentially be used to develop distributions for carcinogenic slope factors (CSFs). Based on the primary tool or framework that is applied, these approaches have been divided into the following three categories: the statistical framework, the decision analysis framework, and the biological framework. Work that has been done on each approach is summarized, and the aspects of variability and uncertainty that are incorporated into each approach are examined. The implications of the resulting distributional information for calculating risk estimates or risk-based concentrations is explored. The approaches differ in their stage of development, the degree to which they diverge from the U.S. Environmental Protection Agency's (EPA) current practice in establishing CSF values, their flexibility to accommodate varying data sets or theories of carcinogenicity, and their complexity of application. In some cases, wide ranges of potential potency estimates are indicated by these approaches. Such findings suggest widely divergent risk assessment implications and the need for additional evaluation of the goals of developing CSF distributions for use in risk assessment applications and the types of information that should be reflected in such distributions. Some combination of the features offered by these approaches may best support risk assessment and risk management decisions.     

Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model

Background  

New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models.     

Study of Tissue-Specific CpG Methylation of DNA in Extended Genomic Loci

Modern approaches for studies on genome functioning include investigation of its epigenetic regulation. Methylation of cytosines in CpG dinucleotides is an inherited epigenetic modification that is responsible for both functional activity of certain genomic loci and total chromosomal stability. This review describes the main approaches for studies on DNA methylation. Under consideration are site-specific approaches based on bisulfite sequencing and methyl-sensitive PCR, whole-genome approaches aimed at searching for new methylation hot spots, and also mapping of unmethylated CpG sites in extended genomic loci.     

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.     

Delimiting species using DNA and morphological variation and discordant species limits in spiny lizards (Sceloporus)

Haplotype phylogenies based on DNA sequence data are increasingly being used to test traditional species-level taxonomies based on morphology. However, few studies have critically compared species limits based on morphological and DNA data, and the methods used to delimit species using either type of data are only rarely explained. In this paper, we review three approaches for species delimitation (tree-based with DNA data and tree-based and character-based with morphological data) and propose explicit protocols for each. We then compare species limits inferred from these approaches, using morphological and mtDNA data for the Yarrow's spiny lizard (Sceloporus jarrovii), a traditionally polytypic species from the southwestern United States and Mexico. All three approaches support division of S. jarrovii into five species, but only two species are the same among the three approaches. We find the greatest support for the five species that are delimited based on mtDNA data, and we argue that mtDNA data may have important (and previously unappreciated) advantages for species delimitation. Because different data and approaches can disagree so extensively, our results demonstrate that the methodology of species delimitation is a critical issue in systematics.     

Genetic epidemiology: Approaches to the genetic analysis of rheumatoid arthritis

The basis of susceptibility to rheumatoid arthritis (RA) is complex, comprising genetic and environmental susceptibility factors. We have reviewed the available approaches to the investigation of the genetic basis of complex diseases and how these are being applied to RA. Affected-sibling-pair methods for nonparametric linkage analysis, linkage-disequilibrium-based approaches, transmission disequilibrium testing, and disease-association studies are discussed. The pros, cons, and limitations of the approaches are considered and are illustrated by examples from the literature about rheumatoid arthritis.     

Molecular epidemiology of group B streptococcal infections

Streptococcus agalactiae (GBS) is a causative agent of sepsis and meningitis in newborns and diseases in pregnant women and nonpregnant adults. Various approaches, including both nongenetic and genetic techniques, are currently used for the study of epidemiology of GBS infections. In the present paper the different methods of molecular epidemiology of GBS infections are reviewed, and several novel approaches are introduced. The advantages and disadvantages of molecular methods are discussed and compared with traditional serotyping technique. The possible use of the molecular approaches for identification of different genetic lineages in GBS as well as for identification and control of the epidemiologically actual clones is discussed.     

Competition among plants: Concepts, individual-based modelling approaches, and a proposal for a future research strategy

Competition is a key process in plant populations and communities. We thus need, if we are to predict the responses of ecological systems to environmental change, a comprehensive and mechanistic understanding of plant competition. Considering competition, however, only at the population level is not sufficient because plant individuals usually are different, interact locally, and can adapt their behaviour to the current state of themselves and of their biotic and abiotic environment. Therefore, simulation models that are individual-based and spatially explicit are increasingly used for studying competition in plant systems. Many different individual-based modelling approaches exist to represent competition, but it is not clear how good they are in reflecting essential aspects of plant competition. We therefore first summarize current concepts and theories addressing plant competition. Then, we review individual-based approaches for modelling competition among plants. We distinguish between approaches that are used for more than 10 years and more recent ones. We identify three major gaps that need to be addressed more in the future: the effects of plants on their local environment, adaptive behaviour, and below-ground competition. To fill these gaps, the representation of plants and their interactions have to be more mechanistic than most existing approaches. Developing such new approaches is a challenge because they are likely to be more complex and to require more detailed knowledge and data on individual-level processes underlying competition. We thus need a more integrated research strategy for the future, where empirical and theoretical ecologists as well as computer scientists work together on formulating, implementing, parameterization, testing, comparing, and selecting the new approaches.     

A review of wetland inventory and classification in Australia

Studies of wetlands in Australia, as in other countries, have taken a wide variety of approaches to defining, surveying and classifying these environments. Past and current approaches in Australia are reviewed for each of the States and Territories which provide the context for much of the natural resource investigation in the country. While there are obvious advantages of national, and perhaps international, agreement on definition and types of wetlands, a variety of approaches to inventory and classification will always be necessary for particular purposes. More fundamental than general agreement on approaches is the need for wetland scientists and managers to maximise the accuracy of survey information, to test the assumptions involved in the use of classifications, and to ensure that the classifications they use are the most appropriate for their purposes. The issue of a global wetland classification scheme is discussed on the basis of a representative range of views by Australian wetland workers.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

Machine learning methods without tears: a primer for ecologists

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.     

Antibody-based approaches to diagnosis and characterization of oxidative phosphorylation diseases

Mitochondrial disorders caused by defects in oxidative phosphorylation function are difficult to diagnose. Here we review the emerging use of antibody-based approaches for this diagnosis. Novel methods involving immunohistochemistry and immunocapture of defective enzymes for characterization are described that add to the arsenal of approaches available.     

A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions

Occupational health and safety (OHS) is a multidisciplinary activity working under the tasks of protection of workers and worksites. Risk assessment, as a compulsory process in implementation of OHS, stands out as evaluating the risks arising from the hazards, taking into account the required control measures, and deciding whether or not the risks can be reduced to an acceptable level. The diversity in risk assessment approaches is such that there are many methods for any industry. Multicriteria decision-making (MCDM)-based approaches contribute to risk assessment knowledge with their ability on solving real-world problems with multiple, conflicting, and incommensurate criteria. This article conducts a critical state-of-the-art review of OHS risk assessment studies using MCDM-based approaches. Additionally, it includes fuzzy versions of MCDM approaches applied to OHS risk assessment. A total of 80 papers are classified in eight different application areas. The papers are reviewed by the points of publication trend, published journal, risk parameters/factors, and tools used. This critical review provides an insight for researchers and practitioners on MCDM-based OHS risk assessment approaches in terms of showing current state and potential areas for attempts to be focused in the future.     

Genomic approaches to fungal pathogenicity

Within a few years, the genome sequences of a large number of medically and agriculturally important fungi will be known. With this resource come the promises of genomic approaches to study pathogenicity and host-fungus interactions. Genomics is particularly attractive for these questions, as conventional genetic and biochemical approaches are limited in many pathogenic fungi. Recent work has applied signature-tagged mutagenesis and DNA microarray analysis to virulence studies in several fungal species, and novel approaches, such as protein arrays and genomic deletion libraries, are being developed in Saccharomyces cerevisiae and have significant potential in other fungi. High-throughput gene-discovery approaches should greatly increase our understanding of fungal pathogenesis.     

Efficient isogeometric thin shell formulations for soft biological materials

This paper presents three different constitutive approaches to model thin rotation-free shells based on the Kirchhoff–Love hypothesis. One approach is based on numerical integration through the shell thickness while the other two approaches do not need any numerical integration and so they are computationally more efficient. The formulation is designed for large deformations and allows for geometrical and material nonlinearities, which makes it very suitable for the modeling of soft tissues. Furthermore, six different isotropic and anisotropic material models, which are commonly used to model soft biological materials, are examined for the three proposed constitutive approaches. Following an isogeometric approach, NURBS-based finite elements are used for the discretization of the shell surface. Several numerical examples are investigated to demonstrate the capabilities of the formulation. Those include the contact simulation during balloon angioplasty.     

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.     

<Emphasis Type="Italic">In vivo</Emphasis> imaging approaches in animal models of rheumatoid arthritis

The interaction of activated leukocytes with the rheumatoid synovial environment is a key process in arthritis. Understanding this process will play an important role in designing effective treatments. In vivo imaging approaches combined with molecular genetics in animal models provide important tools to address these issues. The present review will focus on approaches to in vivo imaging, with particular attention to approaches that are proving useful for, or have promise for, research on animal models of rheumatoid arthritis. These approaches will probably shed light on the specific local mechanisms involved in chronic inflammation and provide real time monitoring approaches to follow cellular and molecular events related to disease development.