The method matters: A guide for indicator aggregation in ecological assessments

Ecological assessment requires the integration of many physical, chemical, and/or biological quality elements. The choice of the aggregation method of such partial assessments into an overall assessment can considerably affect the assessment outcome – an issue that has been controversially discussed within the scientific community for the last decade. Current practice often considers only two different aggregation methods, the weighted arithmetic mean (additive aggregation) and the one-out, all-out method (minimum aggregation). However, both have important drawbacks. Additive aggregation compensates a bad status of one quality element by a number of elements featuring good status. Minimum aggregation can lead to overly pessimistic assessment results, since only the quality element in the worst status is considered. Here, we introduce a toolbox containing current and new aggregation methods, demonstrate and discuss their properties with simple, didactical examples, and suggest in which situations best to use them. Then, we illustrate the consequences of selected aggregation schemes for ecological river assessment with the case study of the Swiss Modular Concept of stream assessment (SMC), which we apply to ten river reaches in the Mönchaltdorfer Aa catchment in Switzerland. To be able to do so, we used multi-criteria decision analysis, i.e., multi-attribute value theory, to arrange the SMC quality elements into an objectives hierarchy, and to translate their individual assessments into value functions. Our case study revealed that choosing the most appropriate aggregation method particularly matters, if objectives with significantly different qualities are aggregated. We argue that redundant objectives (i.e., quality elements), often found at the lower levels of the objectives hierarchy, should best be aggregated additively allowing for compensation to increase the statistical significance of the results. Further, we suggest that complementary sub-objectives that often occur at higher levels may be optimally aggregated with a mixture of additive and minimum aggregation. Such a mixed method will allow some compensation, but nevertheless penalize for very bad states. Since here we compare commonly used aggregation methods with some which we believe have never been discussed in an assessment context before, our study concurrently informs ecological assessment in theory and in practice.     

Human kinesiology

This paper discusses kinesiological aspects of human locomotion in terms of the incomplete morphological adaptation of the body to current lifestyles. Modern instrumentation used for biomechanical analyses is outlined for the most part in the context of human running. Investigations of spatiotemporal and segmental measures by electronic walkways, photography, optoelectronics and angle-angle diagrams are referred to, as is electromyography and direct force measurement. Three commonly identifiable areas of applied kinesiological research, namely sport, industry and medicine, are considered with reference to the prevention, treatment and after-care of injuries whether received on the sporting field, in the work place, or in an automobile accident.     

Cryo-electron microscopy reveals the membrane insertion mechanism of V. cholerae hemolysin

Vibrio cholerae hemolysin (HlyA) is a 65?kDa pore-forming toxin which causes lysis of target eukaryotic cells by forming heptameric channels in the plasma membrane. Deletion of the 15?kDa C-terminus β-prism carbohydrate-binding domain generates a 50?kDa truncated variant (HlyA50) with 1000-fold-reduced pore-forming activity. Previously, we showed by cryo-electron microscopy that the two toxin oligomers have central channels, but the 65?kDa toxin oligomer is a seven-fold symmetric structure with bowl-, ring-, and arm-like domains, whereas the 50?kDa oligomer is an asymmetric jar-like heptamer. In the present study, we determined three-dimensional(3D) structures of HlyA and HlyA50 in presence of erythrocyte stroma and observed that interaction of the 65?kDa toxin with the stroma induced a significant decrease in the height of the β-barrel oligomer with a change in conformation of the ring- and arm-like domains of HlyA. These features were absent in interaction of HlyA50 with stroma. We propose that this conformational transition is critical for membrane-insertion of the toxin.     

Linking behavioral states to landscape features for improved conservation management

1. A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat‐specific behaviors.
2. Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysaetos) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation‐relevant habitat associations.
3. We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short‐distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer‐distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m).
4. Behaviors such as perching and low‐altitude hunting were associated with short‐distance movements in updraft‐poor environments, at higher elevations, and over steeper and more north‐facing terrain. In contrast, medium‐distance movements such as hunting and transiting were over gentle and south‐facing slopes. Long‐distance transiting occurred over the desert habitats that generate the best updraft.
5. This information can guide management of this species, and our approach provides a template for behavior‐specific habitat associations for other species of management concern.

     

“dendRoAnalyst”: A tool for processing and analysing dendrometer data

Dendrometers are vital tools for studying the response of trees to intra-annual environmental changes in different temporal resolutions, ranging from hourly, daily to weekly time resolution. Dendrometers are increasingly used in forest management and tree physiological studies. Besides the data analysis, data processing is also challenging, time-consuming and potentially error-prone due to the immense number of measurements generated by self-registering electronic dendrometers. We present the package ‘dendRoAnalyst’ based on R statistical software to process and analyse dendrometer data using various approaches. This package offers algorithms for handling and pre-cleaning of dendrometer data before the application of subsequent data analytical steps. This includes identifying and erasing artefacts in dendrometer datasets not related to actual stem circumference change, identifying data gaps within records, and the possibility of change(s) in temporal resolution. Furthermore, the package can calculate different daily statistics of dendrometer data, including the daily amplitude of tree growth. The package dendRoAnalyst is therefore intended to facilitate researchers with a collection of functions for handling and analysing dendrometer data.     

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

Generalized additive models (GAMs) have been widely used for flexible modeling of various types of outcomes. When the outcome in a GAM is subject to missing, practical analyses often assume that missingness is missing at random (MAR). This assumption can be of suspicion when the missingness is not by design. Evaluating the potential effects of alternative nonignorable missing data mechanism on the MAR inference from a GAM can be important but often challenging due to the complicatedness of alternative nonignorable models. We apply the index approach to local sensitivity (Troxel, Ma, and Heitjan 2004 (2004). Statistica Sinica 14 , 1221–1237) to evaluate the potential changes of the GAM estimates in the neighborhood of the MAR model. The approach avoids fitting any complicated nonignorable GAM. Only MAR estimates are required to calculate the resulting sensitivity index and adjust the GAM estimates to account for nonignorable missingness. Thus the proposed approach is considerably simpler to conduct, as compared with the alternative methods. The simulation study shows that the index provides valid assessment of the local sensitivity of the GAM estimates to nonignorable missingness. We then illustrate the method using a rheumatoid arthritis clinical trial data set.     

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient‐specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient‐specific combined therapy should be designed. Large‐scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.     

PDMS Microwell Stencil Based Multiplexed Single‐Cell Secretion Analysis

Multiplexed single‐cell protein secretion analysis provides an in‐depth understanding of cellular heterogeneity in intercellular communications mediated by secreted proteins in both fundamental and clinical research. However, it has been challenging to increase the proteomic parameters co‐profiled from every single cell in a facile way. Herein, a simple method to improve the multiplexed proteomic parameters of PDMS microwell based single‐cell secretion analysis platform by sandwiching PDMS stencil in between two antibody‐coated glass slides is introduced. Two different antibody panels can be immobilized easily by static coating, without using sophisticated fluid handling or bulky equipment. 5‐plexed, 3‐fluorescence color single‐cell secretion assay is demonstrated with this platform to investigate human monocytic U937 cells in response to lipopolysaccharide and phorbol myristate acetate stimulation, which identified the existence of functional subsets dictated by different cytokine profiles. The technology introduced here is simple, easy to operate, which holds great potential to become a powerful tool for profiling multiplexed single‐cell cytokine secretion at high throughput to dissect cellular heterogeneity in secretome signatures.