Biophysical Metrics Research Articles

Putting people on the map improves the prioritization of ecosystem services

With limited resources to improve the provision of multiple ecosystem services, conservation programs often rely on spatial tools to identify the best locations for restoration or protection. However, most programs still base prioritization on biophysical metrics that do not fully predict where interventions will deliver the greatest benefits to people. We argue that prioritization metrics need to consider not only biophysical data on the supply of ecosystem services but also socioeconomic data that highlight the value of those services. We use the example of prioritizing best management practices (BMPs) in an agricultural watershed in the US state of Iowa to evaluate how considering the location and preferences of beneficiaries affects the spatial prioritization of investments. We show that adopting metrics that incorporate endpoints of interest and value to people changes the spatial prioritization of BMPs, especially for air quality, lake recreation, and drinking water. Our work demonstrates how publicly available biophysical and social data can be integrated into spatial ecosystem services metrics to enhance the delivered benefits of conservation activities.

Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands: Harrisia pomanensis as a case study

ABSTRACTOrthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost- and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root mean square error of 0.063. These findings suggest that given the entire scene being mapped is experiencing similar atmospheric and illumination conditions, then prediction equations developed using mean reflectance values measured in only one spot within the scene can be used to calibrate the entire orthomosaic in semi-arid woodlands. The proposed methodological framework can potentially be tested and adapted for use in large-scale crop mapping and monitoring in precision agriculture, land-use/land-cover classification as well as plant species delimitation, particularly for mapping widespread invasive alien plants such as H. pomanensis.

Dose-volume metrics and their relation to memory performance in pediatric brain tumor patients: A preliminary study.

Advances in radiation treatment (RT), specifically volumetric planning with detailed dose and volumetric data for specific brain structures, have provided new opportunities to study neurobehavioral outcomes of RT in children treated for brain tumor. The present study examined the relationship between biophysical and physical dose metrics and neurocognitive ability, namely learning and memory, 2 years post-RT in pediatric brain tumor patients. The sample consisted of 26 pediatric patients with brain tumor, 14 of whom completed neuropsychological evaluations on average 24 months post-RT. Prescribed dose and dose-volume metrics for specific brain regions were calculated including physical metrics (i.e., mean dose and maximum dose) and biophysical metrics (i.e., integral biological effective dose and generalized equivalent uniform dose). We examined the associations between dose-volume metrics (whole brain, right and left hippocampus), and performance on measures of learning and memory (Children's Memory Scale). Biophysical dose metrics were highly correlated with the physical metric of mean dose but not with prescribed dose. Biophysical metrics and mean dose, but not prescribed dose, correlated with measures of learning and memory. These preliminary findings call into question the value of prescribed dose for characterizing treatment intensity; they also suggest that biophysical dose has only a limited advantage compared to physical dose when calculated for specific regions of the brain. We discuss the implications of the findings for evaluating and understanding the relation between RT and neurocognitive functioning.

Biophysical metrics for detecting more sustainable urban forms at the global scale

Abstract To test metrics for rapid identification and global evaluation of more sustainable urban forms, we examine the configuration of the Sao Paulo Metropolitan Region (SPMR) in Brazil using satellite remote sensing data and landscape metrics. We adopt principles from landscape ecology and urban planning to evaluate urban heterogeneity and morphology that may constitute more sustainable urban forms, including connectivity, density, geometric complexity (mixed land use), diversity, and greening. Using 2-D wavelet multiresolution analysis and satellite-derived fractional vegetation cover (Fr), the variability of landscape metrics from Landsat (30 m) to MODIS (1 km) scales are investigated. According to our findings, metrics of Patch Density and Landscape Shape Index can be used at the 1-km scale to asses density and geometric complexity of urban form. With the addition of MODIS land surface temperature (LST) data, available at high temporal resolution, a move away from or toward more sustainable urban forms is defined in relation to mitigation of the urban heat island. As the geometric complexity and density of finer-scale urban characteristics are related to climatic impacts at the neighborhood scale, sustainability assessments may be more attainable across urban areas.

Biophysical properties of the clinical-stage antibody landscape

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.

Comparing World Economic and Net Energy Metrics, Part 1: Single Technology and Commodity Perspective

We translate between biophysical and economic metrics that characterize the role of energy in the economy. Specifically, using data from the International Energy Agency, we estimate the energy intensity ratio (EIR), a price-based proxy for a power return ratio (PRR ∼ P out / P invested ). The EIR is a useful metric, because for most countries and energy commodities, it can indicate the biophysical trends of net energy when data are too scarce to perform an original net energy analysis. We calculate EIR for natural gas, coal, petroleum and electricity for forty-four countries from 1978 to 2010. Global EIR values generally rise from 1978 to 1998, decline from 1998 to 2008 and then slightly rebound. These trends indicate one interpretation of the net energy of the world economy. To add perspective to our recent, but short, time series, we perform the same calculations for historical England and United Kingdom energy prices to demonstrate that a given energy price translates to different PRRs (EIR in this case) depending on the structure of the economy and technology. We review the formulation of PRRs and energy return ratios (ERR ∼ E out / E invested ) to indicate why PRRs translate to (the inverse of) energy prices and ERRs translate to (the inverse of) energy costs. We show why for any given value of an ERR or PRR, there is not a single corresponding energy cost or price, and vice versa. These principles in turn provide the basis to perform better modeling of future energy scenarios (e.g., low-carbon transition) by considering the relationship between economic metrics (cost and price) and biophysical metrics (energy and power return ratios) based on energy, material and power flows.

Inclusive Wealth as a Metric of Sustainable Development

Inclusive wealth is a measure designed to address whether society is on a sustainable development trajectory. Inclusive wealth is defined as the aggregate value of all capital assets. Increases in inclusive wealth indicate an improved productive base capable of supporting a higher standard of living in the future. To be truly inclusive, measures of inclusive wealth must include the value of all forms of capital that contribute to human well-being: human capital, manufactured capital, natural capital, and social capital. Sustainability concerns have increased attention on the ways of measuring the value of natural capital. We review various attempts to measure natural capital and to incorporate these into inclusive wealth including estimates using national wealth accounts and integrated ecological and economic models used to estimate ecosystem services. Empirically measuring the value of various types of capital in terms of a common metric is hugely challenging, and no current attempt to date can be said to be fully inclusive. Despite the empirical challenges, inclusive wealth provides a clear, coherent, and systematic framework for addressing sustainable development. Combining measures of semi-inclusive wealth that capture forms of capital that can be relatively easily measured in monetary terms with a set of biophysical metrics capturing important aspects of natural capital that are difficult to measure in monetary terms may provide a good set of signals of whether society is proceeding along a sustainable development trajectory.

The unequal exchange of Dutch cheese and Kenyan roses: Introducing and testing an LCA-based methodology for estimating ecologically unequal exchange

The theory of ecologically unequal exchange (EUE) posits that international trade is structurally organized in a manner that allows a net transfer of resources from peripheral developing to core industrialized countries. The consequence, it is argued, is under-development in the periphery and augmented productive capacity in the core. EUE thus challenges the neoliberal free-market argument that exchange at market prices is symmetric and fair. An LCA-based methodology for estimating EUE that holds constant the variable market price is introduced and tested on contemporary trade of Dutch cheese and Kenyan coffee and roses. Specifically, the exchange of embodied land, water, energy, global warming potential, and labor is assessed. The results confirm the theory's hypothesis. At a fixed market price, more embodied Kenyan resources are exchanged for less Dutch resources. However, Kenyan roses give different results from coffee. EUE between countries can only be conclusively determined by considering the total biophysical trade balance, but by calculating quantities of embodied resources per unit of exchange value, it is possible to detect unequal exchange even at the level of individual commodities. While integration of biophysical metrics alongside monetary valuation is recommended, ultimately, rethinking the structure, policies and politics of international trade is necessary.

A hydrologic connectivity index for jurisdictional analysis of headwater streams in a montane watershed.

A hydrologic connectivity index (HCI) was developed to aid the US Clean Water Act Section 404 jurisdictional evaluation of headwater streams in montane watersheds, using the Cement Creek Watershed in the San Juan Mountains, Colorado as a case study. Jurisdictional determinations are required for many intermittent and ephemeral streams, where significant nexus with downstream traditional navigable waters (TNWs) must be evaluated. Three biophysical metrics (a streamflow metric, distance from the stream to the TNW, and slope between the stream and the TNW) for 239 streams in the watershed were used to develop three indices. These were then combined to develop the HCI, which is a compound, additive index. There was a large range of HCI values for ephemeral streams, which all require a significant nexus evaluation to determine jurisdiction. Statistical analysis of HCI values across stream duration and order classes showed that mean values differed significantly among classes. The flow index component constituted the greatest proportion of the HCI for perennial streams, was sensitive to the flow metrics used, and was greatest for high flows. Although ephemeral streams are only connected to the TNW less than 3 months of the year, the cumulative average annual flow contribution of ephemeral first-order streams can be 5% of the total to the TNW, and their flow contribution is larger during high flows. Some ephemeral streams with high HCI values are farthest from the TNW but contribute the greatest flow out of all ephemeral reaches, and may have significant nexus with the river. The study shows that the HCI can be useful for initial evaluation of connectivity, significant nexus, and jurisdiction of headwaters at the watershed scale.

Economic and ecological views on climate change mitigation with bioenergy and negative emissions

AbstractClimate stabilization scenarios emphasize the importance of land‐based mitigation to achieve ambitious mitigation goals. The stabilization scenarios informing the recent IPCC's Fifth Assessment Report suggest that bioenergy could contribute anywhere between 10 and 245 EJ to climate change mitigation in 2100. High deployment of bioenergy with low life cycle GHG emissions would enable ambitious climate stabilization futures and reduce demands on other sectors and options. Bioenergy with carbon capture and storage (BECCS) would even enable so‐called negative emissions, possibly in the order of magnitude of 50% of today's annual gross emissions. Here, I discuss key assumptions that differ between economic and ecological perspectives. I find that high future yield assumptions, plausible in stabilization scenarios, look less realistic when evaluated in biophysical metrics. Yield assumptions also determine the magnitude of counterfactual land carbon stock development and partially determine the potential of BECCS. High fertilizer input required for high yields would likely hasten ecosystem degradation. I conclude that land‐based mitigation strategies remain highly speculative; a constant iteration between synoptic integrated assessment models and more particularistic and fine‐grained approaches is a crucial precondition for capturing complex dynamics and biophysical constraints that are essential for comprehensive assessments.

Three-dimensional traction forces of Schwann cells on compliant substrates.

The mechanical interaction between Schwann cells (SCs) and their microenvironment is crucial for the development, maintenance and repair of the peripheral nervous system. In this paper, we present a detailed investigation on the mechanosensitivity of SCs across a physiologically relevant substrate stiffness range. Contrary to many other cell types, we find that the SC spreading area and cytoskeletal actin architecture were relatively insensitive to substrate stiffness with pronounced stress fibre formation across all moduli tested (0.24-4.80 kPa). Consistent with the presence of stress fibres, we found that SCs generated large surface tractions on stiff substrates and large, finite material deformations on soft substrates. When quantifying the three-dimensional characteristics of the SC traction profiles, we observed a significant contribution from the out-of-plane traction component, locally giving rise to rotational moments similar to those observed in mesenchymal embryonic fibroblasts. Taken together, these measurements provide the first set of quantitative biophysical metrics of how SCs interact with their physical microenvironment, which are anticipated to aid in the development of tissue engineering scaffolds designed to promote functional integration of SCs into post-injury in vivo environments.

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

AbstractThis study analyzed stream characteristics in a mountain watershed in southwestern Colorado and developed a three‐level hierarchical classification scheme using national datasets to demonstrate jurisdictional evaluation as “waters of the United States (U.S.)” under U.S. Clean Water Act Section 404 at the watershed scale. The National Hydrography Dataset and USGS StreamStats were used with field observations to classify streams in the 53 km2 Cement Creek Watershed based on flow duration (Level 1), stream order (Level 2), and other biophysical metrics (Level 3). Kruskal‐Wallis tests and discriminant analysis showed significant differences among Level 2 classes. Level 3 classification used cluster analysis for stream length, distance to the downstream traditional navigable water (TNW), and the ratio of mean annual flow from the source stream to the TNW. Results showed all perennial and intermittent streams are jurisdictional relatively permanent waters (RPWs), which include over a third of all streams, 64% are intermittent or ephemeral, and almost half are ephemeral first order. All ephemeral reaches are non‐RPWs requiring significant nexus evaluation to determine jurisdiction. These ephemeral first‐order streams can contribute 5% of the annual flow to the TNW at the confluence, while the Cement Creek main stem contributes 21% of the TNW flow. The study demonstrated that the classification provides key biophysical and regulatory information to aid jurisdictional evaluations in mountain watersheds.

Adhesive and mechanical regulation of mesenchymal stem cell differentiation in human bone marrow and periosteum-derived progenitor cells

SummaryIt has previously been demonstrated that cell shape can influence commitment of human bone marrow-derived mesenchymal stem cells (hBMCs) to adipogenic, osteogenic, chondrogenic, and other lineages. Human periosteum-derived cells (hPDCs) exhibit multipotency similar to hBMCs, but hPDCs may offer enhanced potential for osteogenesis and chondrogenesis given their apparent endogenous role in bone and cartilage repair in vivo. Here, we examined whether hPDC differentiation is regulated by adhesive and mechanical cues comparable to that reported for hBMC differentiation. When cultured in the appropriate induction media, hPDCs at high cell seeding density demonstrated enhanced levels of adipogenic or chondrogenic markers as compared with hPDCs at low cell seeding density. Cell seeding density correlated inversely with projected area of cell spreading, and directly limiting cell spreading with micropatterned substrates promoted adipogenesis or chondrogenesis while substrates promoting cell spreading supported osteogenesis. Interestingly, cell seeding density influenced differentiation through both changes in cell shape and non-shape-mediated effects: density-dependent adipogenesis and chondrogenesis were regulated primarily by cell shape whereas non-shape effects strongly influenced osteogenic potential. Inhibition of cytoskeletal contractility by adding the Rho kinase inhibitor Y27632 further enhanced adipogenic differentiation and discouraged osteogenic differentiation of hPDCs. Together, our results suggest that multipotent lineage decisions of hPDCs are impacted by cell adhesive and mechanical cues, though to different extents than hBMCs. Thus, future studies of hPDCs and other primary stem cell populations with clinical potential should consider varying biophysical metrics for more thorough optimization of stem cell differentiation.

P1-03-01: ECM Stiffness and Breast Tumor Histology and Treatment Phenotype.

Abstract Currently, the extent of local surgery and radiation for breast cancer is not guided by biological or anatomical principles, and the optimal volume of treatment is not defined. Local tumor progression is strongly influenced by a dynamic interplay between the genetically-modified epithelium and the associated cellular and non-cellular stroma (Butcher, 2009; Levental, 2009). The extracellular matrix (ECM) is modified in breast tumors and data indicate that the ECM stiffens progressively as mammary tumors evolve and enhancing ECM stiffness promotes mammary tumorigenesis while inhibiting ECM stiffening reduces tumor progression. However, the degree and extent of local stromal changes, and their clinical impact in breast cancer evolution and treatment remain unclear. This has led us to predict that the desmoplastic stroma surrounding breast tumors reflects tumor phenotype and behavior and can be characterized by biophysical metrics of the ECM stroma and molecular signatures of the breast epithelium. Accordingly, we hypothesize that these features may be integrated into a stromal phenotype that can be developed as a predictive tool. To test this concept we used a rigorous biomechanical and biochemical analysis of mastectomy specimens from women with various stages and histology phenotypes of treated and non-treated breast cancer to explore the relationship between ECM stiffness, mechanotransduction and tumor behavior. The goal of the study was to characterize stromal changes as a function of tumor stage and histology phenotype as well as before and after chemotherapy by measuring biophysical and topological features of the ECM and biochemical and molecular signatures of the mammary epithelium. The objective was to determine if there is a significant association with tumor histology phenotype that could be integrated to generate a stromal phenotype. Egan sections of were acquired from breast tissue containing benign and invasive ER positive and negative carcinoma and from treated and non-treated patients. All tissues were subjected to IHC histological and mechanosignaling analysis (H&amp;E; 397FAK; Lysyl Oxidase; phosphomyosin) and biomechanical assessment (Atomic Force Microscopy; Structured Illumination Polarized Imaging; Two Photon Imaging). Preliminary data were consistent with prior in vitro and in vivo studies and showed that there was a significant increase in ECM stiffness as the tissues transitioned from normal to an invasive lesion with the highest stiffness being located at the tumor edge (∼2-4 folds greater). Intriguingly, we observed that ER negative tumors were substantially stiffer than ER positive tumors (50% increase in upper 10-percentile) and that there were tracks of ECM stiffness that correlated with orientated parallel collagen fibers and ECM birefringence. Moreover, we quantified a significant decrease in ECM stiffness following treatment (40% lower) with the most striking reduction in ECM tension being noted in ER negative patient tissues who demonstrated the most robust response. This study should lead to a deeper understanding of the nature of breast cancer stroma and its role in tumor phenotype and response to therapy. Supported by: NCI SPORE P50 CA058207 to VMW, CP, &amp; SH; U01 ES019458-02 to ZW; NCI R01 CA138818-01A1 to VMW; &amp; U54 CA143836-01 to VMW &amp; JL. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-03-01.

A dimensionally consistent aggregation framework for biophysical metrics

We develop a formal representation of the economy–ecosystem interaction problem by distinguishing between stock-flow, and fund-flux spaces (Georgescu-Roegen, 1971). We then define dimensionless quantities in both the cardinal stock-flow space and the ordinal fund-flux space. This leads to analytic definitions of natural capital and natural income in the fund-flux space. We show that a stock-fund representation of the economy–ecosystem interaction problem helps investigate aggregation properties of biophysical metrics. In particular, we show how a metric that is dimensionally consistent in the stock-flow space can have dimensional problems in the fund-flux space. Ecological footprint is used as an illustrative example. Finally, we argue that dimensionally consistent metrics are keys to further the development of biophysical assessments as a tool for practical environmental policy.

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

BackgroundPromoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort.ResultsHere we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.ConclusionThis strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new Chlamydia trachomatis σ66 promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.

The argument against a reductionist approach for measuring sustainable development performance and the need for methodological pluralism

Both sustainability and sustainable development continue to remain elusive concepts even now, 20 years after the Brundtland Commission report that brought them into prominence. This situation most likely stems from the fact that sustainability science encompasses the need to address a wide set of issues over different time and spatial scales and thus inevitably accommodates opinions from diverse branches of knowledge and expertise. However, despite this multitude of perspectives, progress towards sustainability is usually assessed through the development and utilisation of single sustainability metrics such as monetary tools, composite sustainability indices and biophysical metrics including emergy, exergy and the ecological footprint. But is it really justifiable to assess the progress towards sustainability by using single metrics? This paper argues that such a choice seems increasingly unjustifiable not least due to these metrics’ methodological imperfections and limits. Additionally, our recent awareness of economies, societies and ecosystems as complex adaptive systems that cannot be fully captured through a single perspective further adds to the argument. Failure to describe these systems in a holistic manner through the synthesis of their different non-reducible and perfectly legitimate perspectives amounts to reductionism. An implication of the above is the fact that not a single sustainability metric at the moment can claim to comprehensively assess sustainability. In the light of these findings this paper proposes that the further elaboration and refinement of current metrics is unlikely to produce a framework for assessing the progress towards sustainability with a single metric. Adoption of a diverse set of metrics seems more likely to be the key for more robust sustainability assessments. This methodological pluralism coupled with stakeholder involvement seems to offer a better chance of improving the outcome of the decision making process.