A novel design for estimating relative accuracy of screening tests when complete disease verification is not feasible

The accuracy (sensitivity and specificity) of a new screening test can be compared with that of a standard test by applying both tests to a group of subjects in which disease status can be determined by a gold standard (GS) test. However, it is not always feasible to administer a GS test to all study subjects. For example, a study is planned to determine whether a new screening test for cervical cancer ("ThinPrep") is better than the standard test ("Pap"), and in this setting it is not feasible (or ethical) to determine disease status by biopsy in order to identify women with and without disease for participation in a study. When determination of disease status is not possible for all study subjects, the relative accuracy of two screening tests can still be estimated by using a paired screen-positive (PSP) design in which all subjects receive both screening tests, but only have the GS test if one of the screening tests is positive. Unfortunately in the cervical cancer example, the PSP design is also infeasible because it is not technically possible to administer both the ThinPrep and Pap at the same time. In this article, we describe a randomized paired screen-positive (RPSP) design in which subjects are randomized to receive one of the two screening tests initially, and only receive the other screening test and GS if the first screening test is positive. We derive maximum likelihood estimators and confidence intervals for the relative accuracy of the two screening tests, and assess the small sample behavior of these estimators using simulation studies. Sample size formulae are derived and applied to the cervical cancer screening trial example, and the efficiency of the RPSP design is compared with other designs.     

Estimation of the causal effects on survival of two-stage nonrandomized treatment sequences for recurrent diseases

In the treatment of cancer, patients commonly receive a variety of sequential treatments. The initial treatments administered following diagnosis can vary, as well as subsequent salvage regimens given after disease recurrence. This article considers the situation where neither initial treatments nor salvage treatments are randomized. Assuming there are no unmeasured confounders, we estimate the joint causal effects on survival of initial and salvage treatments, that is, the effects of two-stage treatment sequences. For each individual treatment sequence, we estimate the survival distribution function and the mean restricted survival time. Different treatment sequences are then compared using these estimates and their corresponding covariances. Simulation studies were conducted to evaluate the performance of the methods, including their sensitivity to the violation of the assumption of no unmeasured confounders. The methods are illustrated by a retrospective study of patients with soft tissue sarcoma, which motivated this research.     

The planning and control of reaching movements

The notion of internal models has become central to the study of visually guided reaching. Armed with this theoretical framework, researchers are gleaning insights into long-standing problems in the field, such as the ability to respond rapidly to changes in the location of a reach target and the fine control of the multi-joint dynamics of the arm. A key factor in these advances is our increased understanding of how the brain integrates feedforward control signals, sensory feedback, and predictions based on internal models of the arm.     

Enhancing students’ experimental planning competences by introducing and evaluating the IS4O teaching approach

ABSTRACT

We herein report the introduction of the IS⁴O (Improving Students’ Scientific Skills by Using Scaffolding Outlines) teaching approach and its implementation to successfully empower students with initial minor planning competences in mastering complex scientific challenges in the course of Germany’s most-renowned scientific competitions. The IS⁴O teaching approach itself comprises the unique combination of the differentiating teaching methods Backward Planning and Project Mapping as core teaching elements. The beneficial effect of the IS⁴O teaching approach on the enhancement of the students planning competences is also discussed. Based on the given results, this teaching approach is mandatory to empower students with initial minor competences in completing a scientific competition successfully with very high assignment of tasks.     

Refugee species: which historic baseline should inform conservation planning?

Understanding species' historical ranges can provide important information for conservation planning in the face of environmental change. Cromsigt et al. (this issue) comment on our recent European bison (Bison bonasus) range reconstruction, suggesting that bison were already 8000 years ago a refugee species (i.e. restricted to marginal habitat due to past human pressure) and that species distribution models (SDM) are generally of limited use for refugee species conservation. While we welcome this discussion, we find no evidence for the claim that human pressure prior to 8000 BP determined where bison occurred. More importantly, as human pressure is generally high and increasing, attempts to restore species across their former range may fail where the factors that relegated species into refugee status are still at play or where their optimal habitat has vanished. Identifying areas where human pressure is low and where refugee species have persisted over the last millennia is crucial, and SDM based on historical data are important for doing so. Refugee species suffer from the shifting baseline syndrome, but careful reality checks are needed and all available data should be considered before determining the baseline that should inform conservation planning.     

Identifying priority areas for reducing species vulnerability to climate change

Aim The dimensions of species vulnerability to climate change are complex, and this impedes efforts to provide clear advice for conservation planning. In this study, we used a formal framework to assess species vulnerability to climate change quantifying exposure, sensitivity and adaptive capacity and then used this information to target areas for reducing vulnerability at a regional scale. Location The 6500‐km² Mount Lofty Ranges region in South Australia. Methods We quantified the vulnerability of 171 plant species in a fragmented yet biologically important agro‐ecological landscape, typical of many temperate zones globally. We specified exposure, using three climate change scenarios; sensitivity, as the adverse impact of climate change on species’ spatial distribution; and adaptive capacity, as the ability of species to migrate calculated using dispersal kernels. Priority areas for reducing vulnerability were then identified by incorporating these various components into a single priority index. Results Climate change had a variable impact on species distributions. Those species whose range decreased or shifted geographically were attributed higher sensitivity than those species that increased geographic range or remained unchanged. The ability to adapt to range changes in response to shifting climates varies both spatially and between species. Areas of highest priority for reducing vulnerability were found at higher altitudes and lower latitudes with increasing severity of climate change. Main conclusions Our study demonstrates the use of a single spatially explicit index that identifies areas in the landscape for targeting specific conservation and restoration actions to reduce species vulnerability to climate change. Our index can be transferred to other regions around the world in which climate change poses an increasing threat to native species.     

Fast Carbon Footprinting for Large Product Portfolios

Publicly Available Specification 2050‐2011 (PAS 2050), the Green House Gas Product Protocol (GHGPP) standard and forthcoming guideline 14067 from the International Organization for Standardization (ISO) have helped to propel carbon footprinting from a subdiscipline of life cycle assessment (LCA) to the mainstream. However, application of carbon footprinting to large portfolios of many distinct products and services is immensely resource intensive. Even if achieved, it often fails to inform company‐wide carbon reduction strategies because footprint data are disjointed or don't cover the whole portfolio. We introduce a novel approach to generate standard‐compliant product carbon footprints (CFs) for companies with large portfolios at a fraction of previously required time and expertise. The approach was developed and validated on an LCA dataset covering 1,137 individual products from a global packaged consumer goods company. Three novel techniques work in concert in a single approach that enables practitioners to calculate thousands of footprints virtually simultaneously: (i) a uniform data structure enables footprinting all products and services by looping the same algorithm; (ii) concurrent uncertainty analysis guides practitioners to gradually improve the accuracy of only those data that materially impact the results; and (iii) a predictive model generates estimated emission factors (EFs) for materials, thereby eliminating the manual mapping of a product or service's inventory to EF databases. These autogenerated EFs enable non‐LCA experts to calculate approximate CFs and alleviate resource constraints for companies embarking on large‐scale product carbon footprinting. We discuss implementation roadmaps for companies, including further road‐testing required to evaluate the effectiveness of the approach for other product portfolios, limitations, and future improvements of the fast footprinting methodology.     

Maximising return on conservation investment in the conterminous USA

Efficient conservation planning requires knowledge about conservation targets, threats to those targets, costs of conservation and the marginal return to additional conservation efforts. Systematic conservation planning typically only takes a small piece of this complex puzzle into account. Here, we use a return‐on‐investment (ROI) approach to prioritise lands for conservation at the county level in the conterminous USA. Our approach accounts for species richness, county area, the proportion of species' ranges already protected, the threat of land conversion and land costs. Areas selected by a complementarity‐based greedy heuristic using our full ROI approach provided greater averted species losses per dollar spent compared with areas selected by heuristics accounting for richness alone or richness and cost, and avoided acquiring lands not threatened with conversion. In contrast to traditional prioritisation approaches, our results highlight conservation bargains, opportunities to avert the threat of development and places where conservation efforts are currently lacking.     

Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression

Recent advances in technologies such as DNA microarrays have provided an abundance of gene expression data on the genomic scale. One of the most important projects in the post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data are an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from various network candidates. Common core binomial genetic interactions are reliable interactions with a higher possibility of existence, and are important for understanding the dynamic behavior of gene expression networks. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy (Y. Maki, Y. Takahashi, Y. Arikawa, S. Watanabe, K. Aoshima, Y. Eguchi, T. Ueda, S. Aburatani, S. Kuhara, M. Okamoto, An integrated comprehensive workbench for inferring genetic networks: Voyagene, Journal of Bioinformatics and Computational Biology 2(3) (2004) 533.) with an analysis method for extracting common core binomial genetic interactions.